Ar ﬁcial Intelligence for Agriculture and Food
Systems (AI4AFS) Innova on Research Network
Project Implementa on Brief

Suppor ng the responsible development, deployment, and scale of ar ﬁcial
intelligence applica ons and tools that ensure gender equality and social
inclusion in Africa’s Agriculture and Food Systems.

Funded by

Proudly AI4AFS Hub

Proudly AI4AFS Hub

Proudly AI4AFS Hub

Ar ﬁcial Intelligence for Agriculture
and Food Systems (AI4AFS)
Innova on Research Network Project
Implementa on

PARTNERSContents
MESSAGES FROM KEY PLAYERS

7

ABOUT THE DONORS

14

ABOUT THE AI4AFS PARTNER ORGANIZATIONS

16PART A: ARTIFICIAL INTELLIGENCE FOR AGRICULTURE AND
FOOD SYSTEMS’ PROJECT OVERVIEW AND GRANTEES’SELECTION PROCESSES

19

PART B: PRESENTATION OF THE 10 FUNDED AI4AFS PROJECTS

32

6Proudly AI4AFS HubMESSAGESFROM KEY PLAYERS

7

MESSAGE FROM THE PRINCIPAL INVESTIGATORT

Dr Nicholas Ozor
Execu ve Director, ATPS
Principal Inves gator, AI4AFS

he African Technology Policy
Studies Network (ATPS) andits partners, icipe and Kumasi
Hive are very pleased to present
an overview of our journey, status,and milestone so far as a Managing
Organiza on (Hub) for Responsible
Ar ﬁcial Intelligence for Agriculture
and Food Systems Innova on
Research Network in Africa (AI4AFS).
This project is part of the innova on
stream of the Ar ﬁcial Intelligence
for Development Africa (AI4D Africa)
program dedicated to a future where
Africans across all regions create
and use ar ﬁcial intelligence to leadhealthier, happier, and greener lives.
The project is predicated on the needto provide food, feed, ﬁbre, and fun
to over 1.3 billion African popula onthat is es mated to reach 2.6 billion
by 2050. Achieving sustainable andadequate food supply to meet thedemands of this teeming popula onrequires the adop on of emerging
technologies and the responsible
ar ﬁcial intelligence technologies
stand out among many to accomplish
this daun ng task.

The AI4AFS aims to advance the responsible development, deployment, and scaling of
homegrown AI research and innova ons to tackle pressing challenges in agriculture andfood systems in Africa. In the last 20 months of incep on of this project, the AI4AFS Hub has
eﬀec vely set-up, managed, and supported ten (10) innova on research network projects on
AI for agriculture and food systems drawn from eight African countries to develop, deploy, test,
and seek to scale responsible and homegrown ar ﬁcial intelligence research and innova ons.
Careful considera ons have been made to ensure that the 10 selected innova on research
projects were drawn from the Anglophone, Francophone, and Lusophone countries of subSaharan Africa with equal distribu on across gender and collabora ons among research,
private sector, and civil society organiza ons/ Think Tanks that make our AI4AFS network Hub

8Proudly AI4AFS Hub

a unique one. This structure is already paying oﬀ in terms of deepening the understanding of
the network on how to develop, deploy, and scale responsible AI innova ons for sustainable
AFS in Africa as well as in the use of lessons learned to inform African and interna onal AI
policy and prac ce conversa ons. The AI4AFS network projects as presented in this brochureare at diﬀerent levels of project implementa on and we are happy to report that they are allon course according to their work plans.
Finally, as the Principal Inves gator of the AI4AFS Innova on research network Hub, I am
happy to report that we have presented many papers in diﬀerent interna onal fora on AI
in agriculture and food systems, served as Panelists on the subject ma er, and published a
Working Paper on “Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es”. This paper can be accessed on the ATPS AI4AFS project website
at: h ps://atpsnet.org/ai4afs-hub/. I want to appreciate my team from the ATPS and ourpartners at icipe and Kumasi Hive for their full dedica on and commitment to this project.
Most importantly, I want to thank the IDRC and Sida for their ini a ve to fund the AI4D Africa
Program and par cularly the AI4AFS project. We commit to ensuring high impact and value
for money in our AI4AFS innova on research Hub and projects even as we look forward to thecon nued partnership and support to this laudable ini a ve, the ﬁrst of its kind and scale in
Africa.

Proudly AI4AFS Hub

9

MESSAGE FROM THE CO-PRINCIPAL INVESTIGATOR
As a partner in the AI4AFS hub, icipe has
worked with ATPS and Kumasi hive from thestart of the project in September 2021, andnow working with grantees on high-quality
AI research projects that are currently atdevelopment and/or deployment stage
in eight countries in Africa. The Hub has
ensured that such AI tools are developed
responsibly and designed with inclusion of
the vulnerable and marginalised farmers sothat such groups are not excluded from AIrelated beneﬁts.
The AI innova ons range from tools forearly detec on and rapid diagnosis of
diseases and pests, to tools that use dataon remote sensing for predic on of crop
Dr. Henri Tonnangyield, use of wireless sensor networks to
Co-PI , icipe & Head, DMMG
monitor soil, weather condi ons, irriga on
water availability coupled with drones tomonitor crop growth and smart irriga on
systems and e-extension support andother tools. The compliance of our grantees to co-design of AI tools by inclusion is key as AI
technologies might widen the gap between the vulnerable and the well to do/commercial
farmers. For icipe, this project is being coordinated by the Data Management, Modelling,
and Geo-Informa on (DMMG) Unit, a unit that leverages applica on of responsible ar ﬁcial
intelligence (AI) in Plant Health, Animal Health, Human Health, and Environmental Health,the 4Hs of icipe’s research focus (h p://www.icipe.org/research). The unit uses advanced
methods and analy cs including machine learning (ML), AI, design thinking, system thinking
and system dynamics, and computer vision algorithms to exploit and interpret ‘big data’ todevelop mobile apps and geospa al cloud-based tools that are opera onally u lized for ‘real
me’ surveillance, monitoring, and forecas ng of insect pests, crop and environmental healthindicators in the strife to achieve sustainable development goals.

10

Proudly AI4AFS HubMESSAGE FROM THE CO-PRINCIPAL INVESTIGATOR

Jorge Appiah
Co-PI & Execu ve Director, Kumasi Hive

Ar ﬁcial Intelligence (AI) has the poten al torevolu onize agriculture and food systems
in Africa by improving crop yields, reducing
food waste, and increasing eﬃciency
throughout the supply chain. AI-powered
technologies such as precision farming,
predic ve analy cs, and automateddecision-making systems can help farmers
make more informed decisions aboutplan ng, harves ng, and managing theircrops. In addi on, it can be used to monitor
soil quality, track weather pa erns,
and op mize irriga on, all of which cancontribute to sustainable agriculture
prac ces. However, some challenges must
be addressed for AI to be eﬀec vely u lized
in African agriculture and food systems.
These challenges include limited access totechnology and infrastructure, lack of data
and digital literacy, and concerns about
privacy and security.

The AI4AFS Hub together with the sub-grantees over the period have been innova ng aroundto develop homegrown solu ons using AI technologies. We acknowledge our poten al as
Africans to address food system challenges with the help of AI technologies. Overall, thepoten al beneﬁts of AI for agriculture and food systems in Africa are signiﬁcant, but carefula en on must be paid to ensure that these technologies are implemented in an equitable,
sustainable, and responsible manner.

Proudly AI4AFS Hub

11

MESSAGE FROM THE PROJECT MANAGER
The opportunity to coordinate such a
wonderful project is a rare privilege,
ﬁlled with lots of knowledge sharing
and gaining. Working closely with
programme management team to selectten innova ve projects across eight subSaharan African countries was somethingof joy and highly tasking. As the Program
Manager of the Responsible Ar ﬁcial
Intelligence for Agriculture and Foodsystems Innova on Research Network,
my role involves overseeing the selec onof network grantees through a rigorous
process of call for expression of interest,
call for full proposal, and selec on ofinterna onal expert reviewers. Addi onally,
I am responsible for conduc ng capacity
training workshops, robust monitoring, andEngr Dr. Joel Nwakaireevalua on, and developing technical reports
Project Manager, AI4AFS
to be submi ed to IDRC. We have madesure that all processes are transparent andethically right. I am commi ed to ensuring
the responsible development and deployment of AI applica ons in Africa’s agriculture andfood systems that is gender equitable and socially inclusive.I am commi ed to ensuring thatthe network achieves these objec ves and delivers meaningful outcomes that contributeto improving the lives of African people while promo ng responsible AI development anddeployment. I am grateful to Dr. Nicholas Ozor for the privilege to serve in this capacity.

12

Proudly AI4AFS HubMESSAGE FROM THE IDRC PROJECT OFFICERT

he AI for Agriculture and Food Systems
Innova on Research Network is an
excep onal group of researchersthat is making signiﬁcant strides in u lizing
AI to address some of the most pressing
issues in Africa. The network is led by ATPS,
Kumasi Hive, and Icipe, who have createda mul disciplinary and collabora ve
environment that fosters innova on andlearning.
One of the standout features of thisnetwork is its commitment to suppor ng
the next genera on of African scien sts
and AI experts. By providing training andresources, the network is ensuring that
there will be a sustainable pool of talent
in the region to con nue the work ofdeveloping innova ve solu ons.

The network is also highly commi ed
to incorpora ng gender and inclusivity
considera ons in the design of solu ons.
This is a crucial aspect of responsible AI
Gillian Dowie
development, and it is encouraging to see
IDRC Project Oﬃcer
the network ac vely working to ensure thatthe technology they develop is accessible
and beneﬁcial to all communi es. The
AI4AFS Innova on Research Network has
the poten al to showcase how AI can be used as a tool to support vulnerable people andcommuni es. By designing technology that is accessible and relevant to those who need it
most, the network is contribu ng to making AI an accessible technology instead of a barrier.
Finally, the network’s commitment to collabora on is exemplary. In the fast-paced world of
AI research, sharing knowledge and resources is essen al to achieving meaningful progress.
By working together, the network is advancing the ﬁeld and developing solu ons that havethe poten al to transform agriculture and food systems across Africa. It is a privilege towork with this group of researchers and innovators, and I look forward to seeing the exci ng
developments that emerge from this group in the future.

Proudly AI4AFS Hub

13

ABOUT THE DONORS

14

The Interna onal Development Research Centre(IDRC) is a Canadian federal corpora on that supports
research to ﬁnd innova ve solu ons to some of themost pressing global challenges, including poverty,
inequality, and climate change. In its eﬀort to support
Africa’s development, the IDRC launched the Ar ﬁcial
Intelligence for Development (AI4D) ini a ve, a panAfrican program aimed at harnessing the poten al of AI for sustainable development.
Through AI4D, the IDRC provides funding and technical support for research projects that use
AI to address development challenges in Africa. The program focuses on building AI capacity
in African countries and promo ng gender and social inclusion in AI research and applica ons.
The AI4D African Program brings together a network of African AI researchers, policymakers,
and prac oners, enabling them to collaborate and share knowledge to advance the use of AI
for development on the con nent.
The IDRC’s support for the AI4D African Program demonstrates its commitment to promo ng
inclusive and sustainable development through innova on and research. Website: h ps://www.idrc.ca/
The Swedish Interna onal Development Coopera on
Agency (Sida) is a government agency that provides
support for sustainable development ini a ves in low - andmiddle - income countries. One of the key areas of focus for
Sida is promo ng the use of technology and innova on toaddress development challenges. In line with this, Sida has
provided signiﬁcant support to the AI4D African Program, which aims to harness the power ofar ﬁcial intelligence (AI) to drive socio-economic development across the African con nent.
The AI4D African Program is a collabora on between various African governments, the African
Ins tute for Mathema cal Sciences (AIMS), and the Interna onal Development ResearchCentre (IDRC). The program aims to build capacity in AI research and development across the
con nent, promote the use of AI to address social and economic challenges, and facilitate
collabora on between researchers, policymakers, and other stakeholders. Sida has provided
funding to support the program’s ac vi es, which include providing research grants to African
researchers working on AI-related projects, organizing training workshops and hackathons tobuild capacity in AI, and suppor ng policy dialogues and stakeholder engagement ac vi es toensure that the use of AI is aligned with local needs and priori es.
Through its support for the AI4D African Program, Sida is playing a key role in promo ng theuse of technology and innova on to address development challenges in Africa. By building
local capacity in AI research and development, the program is helping to ensure that Africancountries can fully par cipate in the global AI revolu on and leverage the poten al of AI todrive socio-economic development and improve the lives of people across the con nent.
Website: h ps://www.sida.se/

Proudly AI4AFS Hub

15

ABOUT THE AI4AFS PARTNER
ORGANIZATIONS

16The African Technology Policy Studies Network (ATPS) is a transdisciplinary network of researchers, policymakers, private sector
actors, and civil society actors that promote science, technology,
and innova on (STI) for African development, environmental
sustainability, and global inclusion. With its Headquarters locatedin Nairobi, Kenya where it enjoys diploma c privileges andimmuni es accorded other Interna onal and United Na ons
Organiza ons, the ATPS implements its ac vi es through its
Na onal Chapter Oﬃces distributed across 30 countries (27 in
Africa and 3 in the Diaspora – Australia, UK, and USA). The ATPS hasover 5,000 network members and 3,000 stakeholders in over 51 countries in 5 con nents, withins tu onal partnerships worldwide. The ATPS is the lead organiza on in the AI for Agriculture
and food systems project, administering and coordina ng all ac vi es of the Hub. As the leadorganiza on, ATPS ensure a transparent selec on of the research/innova on project proposal.
Addi onally, the organiza on leads in research synthesis, knowledge mobiliza on, and trainingof network grantees, speciﬁcally in the transla on of knowledge outputs into policy products
such as policy briefs for sustained impacts. The ATPS will also lead in all policy dialogue,knowledge brokerage, and network collabora on ac vi es of the Hub. The organiza on’s
mission is to build Africa’s capabili es in science, technology, and innova on for sustainable
development and global inclusion. The ATPS Phase VIII Strategic Plan, 2017-2022 iden ﬁesfour thema c priority areas of interven on, including Agriculture, food, and nutri on, Energy,
Climate change, and environment, and Health innova ons. The Strategic Plan also iden ﬁes
ﬁve cross-cu ng programma c priority areas to deliver on the overall and speciﬁc objec vesof the Ar ﬁcial Intelligence for Agriculture and Foods System Hub. For more informa on, kindly
visit the ATPS website at: h ps://atpsnet.org/

The Interna onal Centre of Insect Physiology andEcology (icipe) is an inter-governmental organiza on
opera ng in 41 African countries with over 500 staﬀ
and 300 partners globally. icipe’s core focus is building
the capacity of people and ins tu ons to respond to
Africa’s development needs, par cularly in training young scien sts and informal training ofextensionists, Na onal Agriculture Research Systems staﬀ, and farmers. The organiza on’s
DMMG Unit comprises seven scien sts who u lize advanced mathema cal, physical, AI, andDS methods and tools to track and predict pest trends, linkages, interac ons, rela onships,
and mechanisms worldwide. The team’s research ac vi es are aimed at developing newcomputa onal techniques for decision support in the context of AFS, Integrated Pest Management
implementa on and prac ces, and climate change and variability impact assessments. icipe
is commi ed to reducing the use of persistent organic pollutants for pest management as a
Stockholm Conven on Regional Centre, collabora ng with the World Organiza on for Animal
Health for Bee Health in Africa, serving as an FAO Reference Centre for Vectors and Vector-borne
Animal Diseases, and partnering with WHO-AFRO for Vector Management. The organiza on
has contributed to the AI for Agriculture and food systems innova on network by guiding

Proudly AI4AFS Hub

17

grantees with its scien ﬁc exper se, selec ng high-quality research projects, increasing skills,
knowledge, and leadership, providing backstopping in research and science applica on of AI,
and training and capacity building of the grantees. Website: h p://www.icipe.org/

Kumasi Hive is a technology and innova on hub in Ghana thataims to build the capacity of young people in technology andbusiness while crea ng sustainable innova ons that solve local
and global challenges. It supports entrepreneurs and innovatorsof all types and provides access to shared working spaces, Biolab
and Makerspace with shared advanced manufacturing tools/
resources that strengthen the goal of rapid prototyping of ideas.
The Hive focuses on bridging the exis ng Digital and tech skills
gap in the African digital entrepreneurship ecosystem and seeks
to address it through integrated innova on. It provides training
covering emerging technologies such as IoT, biotechnology,
blockchain, mobile app/game/web development, AI, virtual/augmented reality, and 3D prin ng. Kumasi Hive also runs a training academy school forcourses on augmented reality, hardware garage, IoT, code school, drone and UAVs, robo cs, 3D
prin ng/3D modelling, mobile app/game/web development, and AI. It also incubates selected
promising hardware start-ups, helping them move from the idea stage through prototyping to
the product-for-market stage with both technical and business development support. Kumasi
Hive will oﬀer these supports to the Hub. Kumasi Hive, as a private sector prac oner andentrepreneur is providing valuable inputs in internally determining the poten al prac cal
impacts of the AI research and innova on projects and how they can transit to commercially
viable products and services for AFS. It will provide linkages with the private sector for theprovision of much-needed research infrastructure, incuba on facili es and exper se in AI.
Website: h ps://kumasihive.com/

18

Proudly AI4AFS Hub

PART A: ARTIFICIAL INTELLIGENCE FORAGRICULTURE AND FOOD SYSTEMS’PROJECT OVERVIEW AND GRANTEES’SELECTION PROCESSES

19

Overview of the AI4AFS projectA

frica’s popula on is expected to reach about 2.6 billion
by 2050. This will require an increase in agriculturaland food produc on by up to 70% to ﬁt the need ofthe popula on, a serious challenge for the agriculture andfood systems. Such requirement, in a context of resource
scarcity, climate change, COVID-19 pandemic, and very harshsocioeconomic conjecture, is diﬃcult to a ain without theinterven on of emerging technologies and innova ons such as
ar ﬁcial intelligence to leapfrog the transforma ons requiredin the sector. The overall objec ve of this ini a ve, therefore,
is to advance the responsible development, deployment,
and scaling of homegrown ar ﬁcial intelligence research andinnova ons to tackle pressing challenges in agriculture andfood systems in Africa.

Three sets of interven ons have been proposed by the
managing organiza ons (Hub) to achieve the stated objec ve.
These include se ng up an ar ﬁcial intelligence innova on
research network for agriculture and food systems; managing
the innova on research network; and fostering collabora ons,
knowledge exchange and valorisa on among the networkand beyond. It is expected that through these interven ons,
responsible and homegrown ar ﬁcial intelligence research andinnova ons will be developed, deployed and scaled to tackle
pressing challenges in agriculture and food systems in Africa.
New and high-quality skills and capacity, learning opportuni es
and collabora ons, and new policies and strategies will be
developed through the interac ons of science-policy-prac ce
systems in the network to sustain a con nued applica on ofar ﬁcial intelligence research and innova ons in transforming
agriculture and food systems in Africa.

20

Proudly AI4AFS HubGoal of the AI4AFS
The general goal of the innova on research
network is to advance the responsible
development, deployment and scalingof home-grown AI innova ons to tackle
pressing challenges in agriculture and foodsystems in Africa. Achieving this objec ve
would make a signiﬁcant contribu on toagriculture, food and nutri onal security as
well as enhance the livelihoods of African
people especially women and youth whoform the majority of the small-scale farmers
in Africa. By developing, deploying andscaling AI innova ons that are homegrown,
peculiar agricultural produc on andproduc vity challenges unique to Africa
can be addressed. Overall, there will beadequate endogenous capacity, technology
and innova ons, infrastructure, and enabling
policy environment to tackle pressing
AFS challenges to achieve sustainabletransforma on in the sector and overall
economies on the con nent.

Core ac vi es of the
Project
The implementa on of the research
network was organized in three
diﬀerent Work Packages (WP) forease of management, administra on,
implementa on, reference, accountability
and budget alloca ons. Each of the three
WPs addresses aspects of the speciﬁc
objec ves of the research network.
The three WPs are:1.

Se ng up of the AI4AFS innova on
Hub and selec on of research
network grantees;2.

Management and administra onof the AI4AFS innova on research
network; and3.

Knowledge valorisa on, exchange
and collabora on in the research
network.

Objec ves
The speciﬁc objec ves of the research
network are to:i)
Deepen understanding of how todevelop, deploy, and scale responsible
AI innova ons for sustainable AFS in
Africa;

Key achievements of the
AI4AFS Hub so farii)

Build the capacity of African innovatorsand researchers to develop, deploy andscale such AI applica ons in AFS; and

1.

iii)

Facilitate knowledge exchange andthe contribu on of African research
to relevant interna onal AI policy andprac ce conversa ons.

The key achievements of the innova on
research network Hub are:Successfully selected 10 innova on
research network grantees that are
equitably distributed across 8 subSaharan African countries namely
Cabo Verde, Ghana, Kenya, Malawi,
Nigeria, Senegal, Tanzania, andUganda with the representa onof English, French, and Portuguese
languages.

Proudly AI4AFS Hub

21

2.

Enhanced the capacity of the innova on
research network grantees in the areaof responsible data collec on andmanagement, ethical research design andmethods, and responsible carbon footprint
repor ng through series of virtual training
workshops and engagements.

3.

Mainstreamed gender equality andinclusive principles in the selec on of the
grantees and in the implementa on andmonitoring of grantees’ projects.

4.

Enhanced crea on of awarenesson responsible AI development anddeployment through par cipa on ininterna onal workshops and webinars
and publica on of a working paper on
the “Responsible Ar ﬁcial Intelligence for
Africa’s Agriculture and Food Systems:
Challenges and Opportuni es. African
Technology Policy Studies Network(ATPS).” Working Paper Series No.
h ps://atpsnet.org/wp-content/
80.
uploads/2023/03/Responsible-Ar ﬁcialIntelligence-for-Africas-Agriculture-andFood-System-Working-Paper-80.pdf.

5.

Documented Grantees’ unique datasets
in local crops and agricultural prac cesthat will enhance the development anddeployment of responsible AI applica ons
in Africa for Africans.

Selec on and Evalua onof the AI4AFS Innova on
Research Network

evaluated and gained a measure ofskills through the pre-proposal training
programmes organized for the poten al
Grantees by the Hub.

Thema c Areas
The call for expression of interest andfull proposals had four (4) thema cthemes of food security (Availability,
Access, Stability and U lity), All selected
applica on cut across these key thema careas with 70% of the applica on having
the component of food availability.

Selec on of quality
Concept Notes for EoI
The call for expression of interest was
launched on 28 March 2022 and closed on
10 May 2022. At the close of submissions,a total of107 EOIs were received from23 countries in sub-Saharan Africa(SSA), through the survey Monkey apply
pla orm.
The use of the Survey Monkey apply
pla orm assisted in colla on and ini al
due diligence that was conducted on
the 107 applica ons. Figure 1 shows
the regional distribu on of the 107
applica ons received across the SSA; 49
applica ons were received from West
Africa, 46 from Eastern Africa, 7 fromSouthern Africa, and 5 applica ons fromCentral Africa.

The selec on of the innova on network
grantees was a two-step process of Callfor Expression of Interest and Call for Full
Proposals. The Hub adopted the two-stepprocess to ensure that par cipants are fairly

22

Proudly AI4AFS Hub

Central Africa,
5, 5%West Africa,
49, 46%

East Africa,
46, 43%
Southern
Africa,7, 6%

Figure 1: Regional distribu on of the EOI Applica ons received

nin
e
B
Togo
%
, 4 Cape Verde1, 2% 21, 2%
SenegalGhana
5, 10%
3, 6%

Nigeria
37, 76%

Zambia1, 14%

Bostwana
2, 29%South Africa
1, 14%

Malawi1, 14%

Mozambique
2, 29%

Figure 4: Distribu on of applica on from

Figure 2: Distribu on of applica on fromcountries in West Africa

Uganda5, 11%

countries in Southern Africa

Ethiopia
9, 20%

Congo
1, 20%

Tanzania
10, 22%

Sudan, 1, 2%
Rwanda2, 4%
Figure 3: Distribu on of applica on fromcountries in East Africa

Kenya
19, 41%

Cameroon
4, 80%
Figure 5: Distribu on of applica on fromcountries in Central Africa

Proudly AI4AFS Hub

23

Gender Analysis of the EOIApplica ons
The EoI Applica ons received validate
the understanding that women are in theminority in AI research in Africa as seenfrom the gender analyses in ﬁgure 6

Female,(English, French, and Portuguese). A er due
diligence by the Program Implementa on
Team (PIT), 28 EoIs were selected for the ﬁrst
round of reviews.

Criteria for due diligence
check on EoI applica ons
received
1.

23, 22%

Male,84, 78%2.

MaleFemaleFigure 6: Distribu on of male and female leadapplicants from the 107 EOI applica ons received

Process of selec ng 28
concept notes
The eligibility criteria for ini al screening of
the 107 applica ons were team composi on(consor um of organiza on from private,
research, and CSOs), Gender composi on ofteam; lead applicant and organiza on mustcome from sub-Saharan Africa, language

24

3.
4.
5.
6.
7.

The team composi on must reﬂect
a strong partnership among three
ins tu ons drawn from research/
universi es; private sector; and civilsociety organiza ons/ NGOs/ Think
Tanks.
The lead organiza on must havesubmi ed all the necessary documents
required including le er of support forgovernment ins tu ons or Cer ﬁcateof incorpora on or registra on for
private sector and CSOs/ NGOs/ Think
Tanks.
The budget is between USD 40,000 and
USD 60,000.
The partners’ names are listed, and all
support le ers are a ached.
The lead applicants are from SSA
countries.
Lead applicants must have a minimumof a degree in a relevant ﬁeld.
Priority is to be accorded to teams withgender balance.

The External Review of the
EOI.
Earlier, the program implementa onteam (PIT) reached out to experts across
Africa to build the capacity of the AI4AFS
network Hub in suppor ng the selec onof the AI4AFS innova on research network
grantees. The experts were to further assist
in training and con nuous technical support
to the Hub and the network grantees todeepen the network capacity to develop,
deploy, and scale responsible AI tools andproducts to leapfrog Africa’s agriculture andfood systems.
The contact details of these experts wereadded on the ATPS interac ve collabora ve
environment (ICE). For informa on about
these experts, please visit h ps://atpsice.
org/ai-experts/. From the list of experts, 28
were iden ﬁed with specialty in the ﬁeld ofAI, data science, computer science, electronicengineering, agricultural engineering, andmechatronic engineering.
These 28 experts were contacted toexpress their willingness to review the EoI
applica ons. Seventeen (17) experts were
ﬁnally selected to review the 28 shortlisted
EOI. The review assignment started on 20thMay 2022 and was concluded on 3rd June
2022.

Criteria for assigning
external reviewers for the
AI4AFS EOIThe following are the criteria used for
assigning reviewers for the AI4AFS EOI:
1. Every proposal was to be reviewed byat least three External Reviewers
2. Out of the three External Reviewers,
only one is allowed to come from thesame country with the applicant toprovide assessment on local content
that may be necessary in the proposal.
The rest must come from outside theapplicant’s country.
3. The reviewer will not have published orco-researched with the applicant.
4. The reviewer is not part of any
applica ons.
5. The reviewer has signed the AI4AFS
non-disclosure Agreement.

Gender composi on of thereviewers
In line with mainstreaming the GEI
principles, the AI4AFS Hub ensured proper
representa on of gender in the selec on of
the external reviewers. Figure 7 shows the
distribu on of gender among the selected
external reviewers.

25

Selec on of 17 EoIs for full
proposal StageMale,
9, 53%

FemaleFemale,8, 47%

Male

Fig. 7 Gender distribu on of the expert reviewers.

Training of the reviewers
A training webinar was organized for all
selected 17 external reviewers to build theircapacity in evalua ng the EoIs. The training
centred around understanding the objec vesof the programme and selec ng EoI thatclearly shows alignment of their concepts
with the overall goal of the ini a ve as wellas with the GEI principles, responsible AI, andresponsible carbon footprint repor ng.
Other areas that they were trained on include
the alignment of the proposed projects to the
key thema c arrears, iden ﬁca on of teams
with relevant experience in area of researchand innova on, technical capabili es ofthe project’s methods, impact pathway forachieving responsible AI4AFS, and wellar culated budget. A training video wasprepared to assist the reviewers use thesurvey monkey apply pla orm. To ensure
standard scoring of the EOIs, scoring criteria
was developed to guide the reviewers. The
same template was created on the survey
monkey apply pla orm for collec on ofscores.

26

28 concept notes were sent for external
reviews, and at the conclusion of the external
reviews, 15 concept note/EoIs that had
the highest scores and ranked 1-15 wereselected; 2 EoIs with considerable high scores
were added to the 15, to bring the total
number selected to 17 concept notes. Their
selec on was based on our determina on
to be inclusive, hence their addi on was
based on language (Portuguese and French)and equitable distribu on of the grantees
across SSA. The selec on was presented to
the Hub Management Commi ee (HMC) forra ﬁca on. At the Fi h Hub Management
Commi ee Mee ng held on 08 June 2022,
the 17-concept note were approved to moveto the full proposal stage.
The breakdown of the 28 applica ons
reviewed showed that the highest score by
an applicant was 86% while the lowest score
was 44%. The 17 concept notes shortlisted
had 6 female leads, and 11 male leads.
In respect to language composi on of the
17 EoIs shortlisted, there were 3 French
applica ons, 2 Portuguese applica ons, and
12 English applica ons.
The Regional breakdown of the selected
applicants are summarized as follows:•
East Africa – 6 applica ons [Kenya (3),Tanzania (2), Uganda (1).]•West Africa – 7 applica ons [Nigeria (3),Ghana (2), Senegal (1), Cape Verde (1).]•South Africa - 2 applica ons[Mozambique (1), Malawi (1).]•Central Africa - 2 applica ons [Cameroon(2).]

Proudly AI4AFS Hub

Call for Proposals
On 8th June 2022, the Hub Management
Commi ee (HMC) during the 7th monthly
mee ng approved that the Call for Proposal(CfP) document be shared among the partners
and their inputs harmonised. The HMC
approved that the CfP be launched on 13th
June 2022 with the deadline for submissionof the proposal from all applicants set at
13th July 2022. The AI4AFS CfP document
was harmonised and shared to all the 17
shortlisted applicants on the 13th of June2022.
The call document contained informa onon the pre-training workshop survey linkto determine the training needs of theapplicants. The CfP contained checklists
for gender equality and inclusion andcarbon footprint repor ng for the proposed
project.Thischecklist was an addi on and an
improvement onthe EOI call document. To
make sure that all applica ons and processes
are in line with responsible AI development
and deployment, the AI4AFS Hub adopted
and modiﬁed the ItechLaw template forresponsible AI assessment, in addi on to
wri ng the GEI considera ons and the carbon
footprint pathway for the proposed projectand its ac vi es.
The highlight of the CfP was the modiﬁed
assessment criteria. The assessment
criteria had clear focus on the theore cal
background to the methods to be used,project governance structure, and knowledge
mobiliza on strategy.
The assessment criteria focused on six mainareas namely:i)
Qualiﬁca ons and team composi on(10%):
The
team
must
be
mul disciplinary,gender-sensi ve,
inclusive, and equitable. The teammust comprise of a research ins tu on

ii)

iii)

iv)

v)

vi)

(government or private), private
sector, and civil society organiza onteams/ Think Tanks, including but notlimited to government, private sector,
academia, farmer associa ons, andagribusiness entrepreneurs, which arehighly encouraged and will be an added
advantage. Support from government
parastatal overseeing relevant sectors is
an added advantage.
Experience in the relevant area of
research and innova on (10%): The
team must prove that they haverelevant experience in the chosen ﬁeldof endeavour demonstrated by previous
project references and track record aswill be shown in the proponent’s CVs.
Technical capabili es (25%): The
Proposal must align with the overall
objec ve of the AI4AFS and theiden ﬁed thema c priority areas.
It must have clarity on the aim andobjec ves, research/innova on designs
and methods, ac vi es, outputs,
outcomes, impact, and sustainability.
Knowledge - mobiliza on strategy(10%): The proposal must show how
ac vi es and outputs of the project will
engage, on an ongoing basis, poten al
knowledge users, including ministries ofagriculture; the strategies to ensure that
research results are used by relevant
stakeholders in Africa.
Responsible AI principles embeddedin the research project: (10%): The
proposal must demonstrate a clear
pathway for integra ng all RAI principles
in all AI development and deployment
ac vi es. It must also contain a clear
pathway for carbon footprint repor ng
in all ac vi es.
Gender Equality and Inclusion (10%):The project shows a clear path to gendersupport and inclusion in the projectac vi es, including machine learning

Proudly AI4AFS Hub

27

algorithms, training, engagements, andworkshops.
vii) Carbon Footprint Repor ng (10%): The
proposed project demonstrates how
the carbon footprint of all ac vi es,
especially machine learning processes,
will be tracked. A clear statementof how the project will adapt to thegreen ac vi es’ pathway is an added
advantage.
viii) Governance structure (5%): The teammust demonstrate how it will begoverned and managed to accomplish
the stated objec ves.
ix) Budge ng (10%): The proposed project
must demonstrate clear and coherent
plans for the use of available funds forthe proposed project ac vi es.

Final Selec on Process of
AI4AFS Network Grantees
The processes involved in the ﬁnal selec onof the network grantees were; Pre-training
workshop for the proponents; submissionof proposals by the 17 applicants shortlistedfrom the EoI stage; due diligence on thesubmi ed proposal, the ﬁnal review of the
proposals; and the selec on of the network
grantees.

Pre-training Workshop for
the AI4AFS Proponents
A pre-training workshop was organized forthe 17 proponents from22-23 June 2022to
provide them with more informa on on
the overall goal of the AI4AFS innova on
research network project. Most importantly,
the workshop provided the proponents withthe opportunity to present their projectideas among their peers and broker somepartnerships and collabora ons especially28

between proponents with similar ideas. The
pla orm further oﬀered the opportunity toaddress some ques ons and inquiries fromthe proponents to enable them to submit
high quality proposals.

Submission of full Proposal
and due Diligence
At the expira on of the deadline forsubmission of proposals (13th July 2022;
23:59 EAT (GMT+1), 16 proposals out of
the 17 EoI shortlisted were received; the17th applica on requested for extensionof me due to network issues, the Program
Implementa on Team obliged for eight (8)hours. The applicant did not submit; hence
16 proposals were evaluated internally todetermine if the requirements were met
for review stage. The due diligence wasconducted in line with criteria on teamcomposi on, which requires a consor um
of mul disciplinary teams that are gendersensi ve, inclusive, and equitable. Eachconsor um must be made up of the following
en es:
i.
Research ins tu ons (public researchins tu ons/government organiza ons/
universi es) comprising scien sts,
policymakers, engineers, agriculturists,
etc.
ii. Private sector en es including Micro,
Small and Medium-scale Enterprises(MSMEs), Entrepreneurs, Innova onhubs/parks, etc.
iii. Civil Society actors including Think
Tanks, Rights-based Organiza ons,
NGOs, Consumer Organiza ons, etc.
From the due diligence conducted, seven (7)proposals had the three en es (research,
private sector, civil society) in its teamcomposi on; another seven (7) proposals
had two en es in its team composi on,

Proudly AI4AFS Hub

while two (2) proposals had one (1) en ty inits team composi on. At the conclusion of thedue diligence, 14 proposals scale the checkon team composi on and two (2) dropped
for lack of the en es required for teamcomposi on. The fourteen (14) proposals
shortlisted a er due diligence weremoved tothe ﬁnal stage of proposal reviews.

Selec on and Assignment ofReviewers
For the full proposal reviews, eleven (11)reviewers were selected from the earlier
pool of experts used for the EoI stage. The
following are the criteria used for selec ng
and assigning reviewers for the Proposals;
1. The reviewer should not come from thesame country as the applicants
2. Proposals from West Africa would be
assigned to applicants from Southern
Africa or Eastern Africa, or Central Africa
and vice visa;
3. Each proposal will be reviewed bythree (3) independent experts withone (1) reviewer from the region of the
proposal.
4. The reviewer will not have published orco-researched with the applicant.
5. The reviewer is not part of any
applica ons.
6. The reviewer has signed the AI4AFS
non-disclosure Agreement.
The review assignment started on 14th
July 2022 and was concluded on 30th
July 2022.

Proudly AI4AFS Hub

29

Expert Reviewers for the selec on of AI4AFS Innova on Research Grantees
Details of Reviewers
Patrick Kinyua Gikunda
Ar ﬁcial Intelligence researcher, Dedan Kimathi University of Science and Technology,Kenya
Joseph K. Mung’atu
Sta s cian and a University Lecturer. Jomo Kenya a University of Agriculture andTechnology (JKUAT), Kenya.
Samuel Musili Mwalili
Research Scholar on AI at Jomo Kenya a University of Agriculture and Technology, Nairobi,
Nairobi County, Kenya
Albert Njoroge Kahira
Research scien st Jülich Supercompu ng Centre, Germany
Kennedy Senagi
Data scien st, Icipe, Kenya
Tatenda Duncan Kavu
Researcher, African Leadership University, Kigali, Rwanda
Seydou Nourou Sylla
Data Scien st/ Machine learning researcher, DIT, Senegal
Mouhamad M. Allaya
Assistant Professor, Thies University, Senegal
Talaat MohyEldin Wahby Elamin
Associate Professor, Sudan University of Science and Technology, Sudan
Adimora, Kyrian Chinemeze
Lecturer, University of Agriculture, Umudike, Nigeria
Ozoemena Anthony Ani
Lecturer, University of Nigeria Nsukka, Nigeria
Fredrick Mzee Awuor
Lecturer, Kisii University, Kenya.
Deborah Dormah Kanubala
Natural language processing(nlp) engineer, Proto, Germany
Salomey Osei,
ML Engineer, Deustotech, University of Deusto., Ghana/ Spain
Tejumade Afonja
Machine Learning Researcher
Helmholtz Center for Informa on Security team | Mario Fritz Lab, Germany
Ogechukwu Iloanusi
Professor, Department of Electronic and Computer Engineering, University of Nsukka,
Nigeria
Niemah Izzeldin Mohamed Osman,Assistant Professor, Sudan University of Science andTechnology, Sudan
Ivone Muocha, Researcher, Centre for Research and Technology Transference for Community
Development, Ministry of Science, Technology and High Educa on, Mozambique,
Ijeoma Ezika, Senior Lecturer, MAAMI Nigeria/ University of Nigeria, Nsukka., Nigeria
Professor Mpho Raborife,Computa onal linguis cs. Ins tute for Intelligent Systems,
University of Johannesburg, South Africa.

30

Proudly AI4AFS Hub

Sex
M
M

M

M
M
M
M
M
M
M
M
M
F
F
F

F

F
F
F
F

Shortlis ng of Network Grantees
The review results as seen in ﬁgure 8 show that the highest total score was 83.83 and theminimum score was 34.50. The program implementa on team decided to select 10 proposals
out of the fourteen (14) that were reviewed. From the selected proposals, the highest score
was 83.83 and the lowest score was 56.17. The selected ten (10) grantees had ﬁve (5) female
and ﬁve (5) male principal inves gators drawn from three (3) major languages in Africa, namely
English, French, and Portuguese, with a spread across eight (8) sub-Saharan African countries
namely Cabo Verde, Ghana, Kenya, Malawi, Nigeria, Senegal, Tanzania, and Uganda.
Expressionof Interest
EOI review

107
Applica ons
received

1st Duediligence79
applica ons
notshortlisted

28
applica ons

Proposalstage
2nd Duediligence

17
applica ons

11
applica ons
notshortlisted

Proposal
review
3rd Duediligence14
proposals

3
applica on
notshortlisted

Final
Duediligence

10
Grantees
Fig. 8 The Selec on process of the network grantees at a glance

Proudly AI4AFS Hub

31

PART B: PRESENTATION OF THE 10 FUNDED
AI4AFS PROJECTS

32

Project 1: Monitoring and Ar ﬁcial
Intelligence Tools for Smart Agriculture.
Principal Inves gator: Dr Sónia Semedo
Organiza on: Universidad e de Cabo Verde
Project Amount: US$ 50,000.00 (US Dollar Fi y thousand only)
Grant Award Number: AFS-9957569475
Country of Implementa on: Cape Verde
Project Partners
Co-PI 1: Claudio Gonçalves
Organiza on: Associação de Produtores da Ribeira de São
Filipe (APRSF), Cabo Verde
Co-PI 2: Érico Fortes
Organiza on: PrimeBo cs, Cabo VerdeGoal
The main goal of the project is the use of Internet of Thing
and Ar ﬁcial Intelligence solu ons suitable for Cape Verdeto increase the produc on level that allows mi ga on andincreased resilience in the face of climate change.

Objec ves
The speciﬁc objec ves of the study are to:
1. Develop a wireless sensor network to monitor soil,
weather condi ons and water availability;
2. Develop a drone for crop mapping.
3. Create a database for AI training and evalua on.
4. Implement a pilot system.
These objec ves are aligned with two of the four priority
areas of Agriculture and Food Systems, Availability andStability. The project is developing a farm management
system to perform automa c irriga on and drone mapping ofcrops. It will also conduct training and knowledge exchange
through capacity building of students and farmers in the ruralcommunity drone controller and smart agriculture system.

Proudly AI4AFS Hub

33

Key ac vi es
•
•

•
•
•

Project incep on mee ng
Project in debt literature review documen ng
speciﬁca ons for IoT and AI models; documenta on
about drone and wireless sensor network speciﬁca ons
Iden fy with the community partner pilot land formapping and project implementa on.
Develop a wireless sensor network to monitor soil,
weather, and water.
Build a drone for crop mapping.

Key Achievements
The key projects achievements iden ﬁed during the review ofthe progress report are as follows:
Iden ﬁca on of the 3 plots of land where the pilot
project will be implemented,(14.955380554321751,-23.51010696300595) with a total area of 4000 m2 forthe implementa on of the pilot.
In-depth literature review and iden ﬁca on of trends inthe use of IoT and Ar ﬁcial Intelligence models in smart
agriculture.
Successfully tested the system that monitors weather
parameters (windspeed, air temperature and humidity,
precipita on, wind direc on), soil parameters (soilmoisture and temperature).

3D prin ng of the components and moun ng of theweather sta on,
Development of the soil monitoring system prototype
and electronic valve actuator; andSe ng up a prototype in the laboratory.

Next Steps
Future ac vi es will involve crea on of the applica on for
integra ng sensor data, prepara on of the ﬁeld for installa onof the pilot (construc on of the tank and adapta on of
the electronic valve); data collec on for the crea on of AI
models; training of students and farmers in drone pilo ng
and opera on of smart agriculture system; and dissemina onof results .Since the design of the pilot was deﬁned together
with the farmers by adding minor adapta ons to the exis ng
drip irriga on system it is possible for other farmers to adopt
the technology allowing for scale up the product.

34

Proudly AI4AFS Hub

Project 2: Development of Machine
Learning Model for Crop Pests andDiseases Diagnosis Based on Crop
Imagery Data.
Principal Inves gator: Dr. Hudson Laizer
Organiza on: Mbeya University of Science and Technology
Grant Amount: US$ 51,000.00 (US Dollar Fi y-one thousand only)
Grant Award Number: AFS-2504001568
Country of Implementa on: Tanzania
Project Partners
Co-PI 1: Dina Machuve
Organiza on: Dev Data Analy cs, Tanzania
Co-PI 2: Adam Siwingwa
Organiza on: Southern Corridor Alliance of Agriculture
Producers, Tanzania

Goal
The goal of the project is to develop a Machine Learning models
for early detec on of Common bean and Irish potato diseases
in the Southern Highlands regions of Tanzania.

Objec ves
The speciﬁc objec ves of the study are to:
1. To assess farmers’ knowledge and percep ons on Ar ﬁcial
Intelligence, Machine Learning, pests and disease
management in Common bean and Irish potato farming.
2. To collect high-quality imagery data for healthy anddiseased Common bean and Irish potato crops.
3. To annotate and prepare the collected image data for
Machine Learning models development.
4. Train and build capaci es of farmers, agricultural extensionoﬃcers and other stakeholder.

Key ac vi es
The key ac vi es for the six months of the project where:Incep on mee ng for project stakeholders to communicate
the project objec ves, responsible AI prac ces, risks andProudly AI4AFS Hub

35






Enrolment of farmers to the project with the goal of
Iden fying with the community partners
Development of data collec on tool using ODKTraining of project team and enumerators on using
digital data collec on tools
Ini al data collec on for tool tes ng and veriﬁca on

Key Achievements
1.

2.

The project team has collected ﬁeld data and areverifying the uploaded data. The target was 120,000
but have managed to collect 129,500 images for allclasses. The dataset that has been pre-processed formachine learning is a total of 36,000 images. The data
explora on ac vity involved baseline training of models
for image classiﬁca on and object detec ons tasks.
500 images were used from each class to ensure allclasses are balanced. The standard GPU on Google colab
environment was used to train the baseline models.
The project PI (Dr. Hudson Laizer) and ML expert (Dr.
Neema Mduma) presented the project work in Black in
AI workshop co-located with NeurIPS 2022 Conferencein New Orleans, Louisiana, United States of America. The
workshop enabled the project team to establish bothformal and informal networks with other researchers inthe ﬁeld of AI and ML for future collabora ons.

Next Steps
The image pre-processing work is ongoing to reach the targetof 120,000+ images for six classes. The dataset that will beshared in ATPS open repositories. The project team will train
more model architectures for image classiﬁca on and object
detec on for suitable model selec on on deployment onmobile applica on. A er model development and tes ng, theproject team will organize workshops, training and dialogues
where par cipants including smallholder farmers, government
oﬃcials, private sectors etc. will be engaged to discuss ma ers
of mutual beneﬁts. The engagement of mul ple stakeholders
during dissemina on of project results is important not onlyin sharing project outcomes but also in collec ng the diverse
opinions that will help to iden fy any need for policy review
and change 2.2.4 Gender equality and inclusion ac on.

36

Proudly AI4AFS Hub

Project 3: Enhancing Farm-scale Crop Yield
Predic on using Machine Learning models
for Internet of Agro – Things in Tanzania.

Principal Inves gator: Dr. Barakabitze Alcardo Alex
Organiza on: Sokoine University of Agriculture, Tanzania
Grant Amount: US$ 51,000.00 (US Dollar Fi y-one thousand
only)
Grant Award Number: AFS-3882447997
Country of Implementa on: Tanzania
Project Partners
Co-PI 1: Dr. Dominic Ringo
Organiza on: Research Community andDevelopment Associa on (RECODA), Tanzania
Co-PI 2: Mr. Adam Rowland
Organiza on: SAHARA Ventures, Tanzania

Organiza onal

Goal
The overall objec ve of the project is to develop reliable andreusable ML-driven crop yield predic on models using historicalmeteorological data, satellite data and proximal sensor data toenhance crop yield predic ons in Tanzania.

Objec ves
The speciﬁc objec ves of the study are to:
To develop a model that u lizes historical mul -source
data to predict maize and sorghum yield at the districtlevel using climate reanalysis data, satellite imagery data,
weather data, soil data, and proximal IoT data to train
machine learning models to predict district-level yielddata.

To deploy a small-scale smart farming system using lowcost Internet of Agro Things (IoAT) sensors and interac ve
cloud-based big data analy cs to monitor and evaluate
crops’ performance in real- me.

To pilot a big data model to predict farm-level yieldusing low-cost agricultural Internet of Agro Things (IoT)
sensor data by enhancing district-level resolu on yield
Proudly AI4AFS Hub

37




data to farm-level resolu on yield data using Genera ve
Adversarial Networks (GANs).
To conduct the economic feasibility of using agricultural
IoT and big data for small-scale farm monitoring and yieldpredic on.
To formulate a data-driven policy brief on crop predic onusing mul -source big data. The research will iden fy andreach poten al farmers to use agricultural IoT.

Key ac vi es
The key ac vi es for the ﬁrst quarter where:•
Incep on workshop conduc on and engagement with
farmers•
Data collec on from the online dataset repository(Historical data of 14 years for predic on of crop yields).
•
Cleaning and forma ng of historical dataset for machinelearning (ML) applica on•
Acquisi on of IoAT devices for the project•
Discrete-level predic on model development and tes ng.

Key Achievements
The key projects achievements iden ﬁed during the review ofthe progress report are as follows:•
The development and implementa on of the agriculture- Internet of Agro -Things (IoATs) ML- based farming
monitoring and management system. The developed
prototype can help smallholder farmers to see diﬀerent
farm a ributes like temperature, humidity, soil moisture
which are directly seen or observed through a farmer’s
Smartphone App.
•
Conducted workshop on importance of developing
innova ve approaches for predic ng crop yield using
machine learning and Internet of agro-things sensors toincrease agriculture produc vity of smallholder farmers in
Tanzania.
•
Prepara on of demonstra on farm regarding enhancing
crop yield predic on using ML and IoT at SUA farming sites.
This demo farm is being implemented in collabora onwith the NEGGROW company that has been contracted towork on the installa on of IoT sensors in the farm.
•Collected yield data from maize and sorghum crops from
Lindi, Morogoro and Dodoma regions. These data will

38

Proudly AI4AFS Hub

be used for the development of ML – driven crop yieldpredic on models.
IoAT has been developed. The project has conducted
ini al prototyping and helping smallholder farmers to see
diﬀerent farm a ributes like temperature, humidity, soil
moisture which are directly seen or observed througha farmer’s Smartphone App. We have also been able toprogramme the hardware by imbedding and linking themwith web applica ons. The developed prototype has
been tested at SUA farming sites available at the Sokoine
University of Agriculture

Next Steps
The project team plans to develop and test a real- me analy cs
dashboard for the Internet of Agricultural Things (IoAT), as
well as a farm-level predic on model, data augmenta on,and economic analysis in Tanzania. Feedback will be gatheredfrom smallholder farmers through focus group discussions,interviews, and ques onnaires. A policy brief will be developed
to encourage adop on of the ML tools by farmers in Tanzania
and Africa. The project will be scaled to include the Dodoma
region and will be replicated at the Dodoma farming blocks
where youths are being trained in digital agriculture. A research
paper will be submi ed to the Agriculture and Electronics
Elsevier Journal for publica on considera ons. Finally, training
workshops will be conducted with farmers to demonstrate thetools’ innova veness and credibility.

Proudly AI4AFS Hub

39

Project 4: Using Ar ﬁcial Intelligence toenhance the Produc on, Management andMarke ng of Nsukka Yellow Pepper(Capsicum Chinese Nsukkadrilus).
Principal Inves gator: Engr Prof. Chinenye Anyadike
Organiza on: Associa on of Professional Women Engineers of
Nigeria
Grant Amount: US$ 59,000.00 (US Dollar Fi y-nine thousand
only)
Grant Award Number: AFS-0575120455
Country of Implementa on: Nigeria
Project Partners
Co-PI 1: Engr Alex Onyia
Organiza on: Educare Nigeria
Co-PI 2: Dr Cynthia Nwobodo
Organiza on: University of Nigeria, Nsukka, Enugu State,
Nigeria

Goal
The overall objec ve of this project is to develop and deploy
Ar ﬁcial Intelligence to improve the produc vity and yieldof Nsukka Yellow Pepper by strengthening the resilience of
farmers in adap ng to climate change, applying mi ga on
strategies, and thus empowering the women and youths.

Objec ves
The speciﬁc objec ves of the study are to:•Analyse and review gender dynamics, prac ces, andins tu onal support gaps in Nsukka yellow pepper
produc on to understand statutes support gaps.
•
Co-develop an AI-based pest detec on, smart irriga on,
nutrient monitoring, and e-extension service applica on
for sustainable produc on, management, and marke ng
of Nsukka yellow pepper.
•
Co-deploy, monitor, train, and engage stakeholders in theuse of AI tools for sustainable produc on, management,
and marke ng of Nsukka Yellow Pepper.

40

Proudly AI4AFS Hub

Key ac vi es
•

•

•

•
•
•
•
•

Desk review, IDIs, KIIs, FGD to document gaps in the
produc on, management and marke ng of Nsukka Yellow
Pepper.
Key team members will pay a visit to the chosencommuni es and farmers. Mee ngs will be scheduledwith key community leaders as community awarenessand project buy-in strategy.
Data Collec on to understand gender dynamics, whatworks, what does not work, and why. This is to increase
gender equality and inclusivity from the start of the
project.
Results and Data Analysis - Analysis of IDIs, KIIs, desk
reviews and FGDs.
Dataset collec on of Nsukka yellow pepper leaf forlabelling and classiﬁca on with smartphones
Development of an AI system on ﬁeld camera based
unhealth leaf detec on and communica on system
Development IoT based systems for soil nutrient andmoisture monitoring system.
Development of Smart phone based NYP extension
service and marke ng applica on.

Key Achievements
The key projects achievements iden ﬁed during the review ofthe progress report are as follows:•
The project has the relevant data on gender dynamics,
plan ng prac ces, barriers, and technology acceptancein the produc on, management, and marke ng of NYP.
In terms of gender dynamics and socio-demographics,
we discovered that more women par cipate in NYP
produc on, management, and marke ng. Also, bothpeople with formal educa on and no formal educa on
are involved in NYP produc on. Most of the farmers
are married and that farmland is obtained throughinheritance.
•
The project was able to obtain relevant informa on on thecommon plan ng prac ces such as the use of seedlings
and that farmers determine the quality of seedlings/seedby visual observa on of physical appearance of the seeds/
seedlings. The use of fer lizers is also a common prac ce,and the most used fer lizers are NPK and Urea. The eﬀect

Proudly AI4AFS Hub

41

•

•

•
•
•

of climate change according to farmers is prevalent inincreased pests and disease infesta on, destruc on ofplants in ﬁelds by ﬂooding, wil ng due to drought, ro ngof pepper due to heat waves, increased weed infesta on.
The project team understands the major barriers in NYP
produc on, management and marke ng that include
accessibility to land and diﬃculty in transporta on of
goods. Since land is obtained by inheritance, scalabilityof NYP produc on is a major challenge for most farmers.
Also, transporta on of produce to buyers takes an averageof 24hrs during which some losses occur.
Dataset for machine learning ac vity have been collected
through a gender inclusive data collec on process. 3000
datasets of healthy and unhealthy leaves of Nsukka yellow
pepper have been collected and classiﬁed. This is a majorachievement of the project as at the me of the start ofthe project such dataset did not exist.
The algorithm for the disease detec on system has beendeveloped and implementa on been carried out.
The soil nutrient and moisture IoT monitoring systems
developed.
Version 4 of the extension service and marke ng app
developed, and ﬁnaliza on of the app almost completed
for deployment in google play store.

Next Steps
•

•

•
•

•

42

Deployment and evalua on of the AI systems formonitoring a smart irriga on system on a 10m x 20m
demonstra on farm with a control.
On-site demonstra ons and training of selected women,
youth, and men on the technicali es of the smart farming
system.
Publica on of knowledge product
Organiza on of farmers into co-opera ve groups withwomen leading some of the selected groups and trainingof the groups on the use of the marke ng and extension
service app.
Policy dialogue involving all stakeholders through focus
group discussions and engagement with government,
private sector, and civil society organiza ons to buy in into
the use of the products and services. This will ensure that
there is an insured exit strategy for commercializa on.

Proudly AI4AFS Hub

Project 5: Project: Scaling Smartphone-Based Tools for Early Crop
Diseases Detec on & Monitoring.
Principal Inves gator: Dr. Godliver Owomugisha
Organiza on: Busitema University, Uganda
Grant Amount: US$ 55,000.00 (US Dollar Fi y-ﬁve thousand
only)
Grant Award Number: AFS-0163245214
Country of Implementa on: Uganda
Project Partners
Co-PI 1: Emmanuel Ofumbi
Organiza on: Papoli Community Development Founda on,
UgandaCo-PI 2: Evarist Abiine
Organiza on: Nyakasozi Tukooreamwe Coﬀee Farmers’
Co-opera ve Society Limited, Uganda

Goal
The overall goal of the project is to create posi ve impact on
the livelihoods of smallholder farmers by availing them with
automated disease diagnosis tools for in-ﬁeld and real- me
feedback thus improving on food security and produc on.

Objec ves
The speciﬁc objec ves of the study are to:•
Scale smartphone applica ons for crop disease diagnosis
and real me advisory informa on to smallholder farmers.
•
Build mul modal machine learning algorithms to predict
crop diseases before they are asymptoma c using spectral
data.
•
To disseminate and pilot the research studies throughconferences, workshops, and the communi es.

Proudly AI4AFS Hub

43

Key ac vi es
•
•
•
•
•
•

Incep on mee ng and projectplanning with partners
Iden ﬁca on and engagement ofsmallholder farmers
Data collec on.
Deployment of mobile diagnos capplica on.
Training of farmers to use themobile tool.
Construc on of a low-cost
spectrometry device

Key Achievements
•

•

•

44

The constructed portable, lowcost spectrometer device using
3-D prin ng technology is a breakthrough in the ﬁeldof agriculture. This innova ve device can be easilya ached to a smartphone, making it highly accessible andconvenient for farmers to use. The key components of thisdevice include its design and the automa on of the light
spectrum absorbed from the leaf sample. The device has
been fully opera onal and is currently undergoing ﬁeld
tes ng. Ini al data collected using this device has shown
dis nct plant features, and it is being used to monitor
plants in a controlled setup.
The project has successfully scaled up the usability of ourtools beyond the ini al pilot phase of 100 farmers. We
have built a network of over 500 farmers across Uganda
through farmer coopera ves and groups. By educa ng
more farmers about the beneﬁts of phone-based
technologies in agriculture, we are improving their qualityof life and helping them to become more self-suﬃcient,
without having to rely on experts.
The study has made signiﬁcant contribu ons to theavailability of data in spectrometry, imagery, and textual
datasets for Natural Language Processing (NLP) models.
We have been able to crowd source more data fromfarmers, including textual, imagery, and audio data, which
has been labelled and made publicly accessible. This
will provide researchers and prac oners with valuable

Proudly AI4AFS Hubinforma on to develop more accurate and reliable NLP
models for the agriculture sector. By increasing theavailability of data, we can accelerate progress towards
more sustainable and produc ve farming prac ces.

Next Steps
The implementa on of AI-powered tools in agriculture has thecapacity to enhance produc vity, lower expenses, and advancesustainable prac ces. Moving forward, the project aims toaccomplish the following objec ves:
1. U lise our disease diagnos c tools to gather real- me
crop data via crowdsourcing from farmers throughout theregion.
2. Leverage our advanced spectrometry add-on tools tosense mobile symptoma c and asymptoma c crops.
3. Combine various modali es to oﬀer a more thoroughassessment of crop health and environmental condi ons.
This step is crucial in unlocking the advantages of AI
tools in agriculture and guaranteeing their successful
implementa on in the industry.

Proudly AI4AFS Hub

45

Project 6: Pest Occurrence Early
Warning System and Diagnos c Tool
Development using Geoinforma on andAr ﬁcial Intelligence: A Case Study ofTomato Leaf Miner (Tuta Absoluta), andWhiteﬂies in Machakos County, Kenya)
Principal Inves gator: Dr. Hilda Manzi
Organiza on: Geospa al Research Interna onal, Kenya
Grant Amount: US$ 52,000.00 (US Dollar Fi y-two thousand
only)
Grant Award Number: AFS-4241717219
Country of Implementa on: Kenya.
Project Partners
Co-PI 1: Cosmus Muli
Organiza on: Kathaana Vegetable Growers, Kenya
Co-PI 2: Joseph Sang
Organiza on: Jomo Kenya a University of Agriculture andTechnology (JKUAT), Kenya

Goal
The overall objec ve of this research is to develop a Spa albased AI web Tool to detect, iden fy, and monitor leaf miner(Tuta absoluta) and whiteﬂies to allow for the integra on andimplementa on of Integrated Pest Management Solu ons
in smallholder tomato vegetable farms in Machakos county,
Kenya, as a gateway to develop and scale sustainable African
agriculture and food systems.

Objec ves
1.

2.

46

Use AI to digi ze tomato farms in Machakos, Kenya, for
improved pest surveillance and management of leaf miner
and whiteﬂies by smallholder farmers.
Analyze remote sensing data to establish correla on
between environmental factors and occurrence of leaf
miner and whiteﬂies in tomato farms in Machakos,

Proudly AI4AFS Hub

3.

4.

5.

Kenya, for evidence-driven adop on of integrated pest
management prac ces.
Develop an AI-based pla orm for accurate detec on anddiagnosis of leaf miner and whiteﬂies to fast-track costeﬀec ve pest outbreak surveillance and management,
leading to increased farm yield among smallholder farmers
in Machakos, Kenya.
Create a spa al-based web and mobile tool integra ng
AI, remote sensing, and data mining techniques for early
diagnosis and control of leaf miner and whiteﬂies in tomato
farms in Machakos, Kenya.
Develop a sustainable business model for AI-based private
e-extension services aimed at young women in agriculture
for pest disease diagnoses and control measures.

Key ac vi es
•

•

•

•

•

Understanding farmers’ knowledge, percep on, andmanagement of Tuta absoluta and whiteﬂies throughbaseline surveys. This informa on formed the basis of
this study and is assis ng in the development of ar ﬁcial
intelligent monitoring tools for these pests.
Farm demonstra on to study Tuta absoluta and whiteﬂies
pest occurrence and popula ons for the training of the
AI models. This involved the collec on of land surface
temperature data from the ﬁeld for comparison with pest
occurrence and popula ons.
Collec on of farmer data on the occurrence of Tutaabsoluta and whiteﬂies. This targeted the speciﬁc dates inevery month for the last six years. The data will enable thetraining and tes ng of the machine-learning models.
Extrac on of satellite data products; land surface
temperature and soil surface moisture for the last six
years to study pa erns and trends in temperature and soil
surface moisture change for every decadal for six years.
The design of the early warning system tool for pest
monitoring in tomatoes. This will assist in the deploymentof the front-end and back-end processes of the system.
This also involved the development of algorithms for land
surface temperature and soil surface moisture for use inanalysing satellite data and the deployment of various
outputs via an API from the sen nel hub to the PeMoST
tool servers.

Proudly AI4AFS Hub

47

Key Achievements
1.

2.

3.

4.
5.

6.

Baseline surveys showed that farmers in Kathaana are highly awareof Tuta Absoluta and white ﬂies as well as the severe impact thesepests have on tomato crops. This knowledge is important for thedevelopment and implementa on of eﬀec ve pest management
strategies.
Tomato farming is an important part of the Kathaana community,
grown both for subsistence and for the market. The project aims toaddress crop management issues that lead to high varia on in yields
among farmers.
The project aims to address knowledge gaps and improve extension
services for farmers. Most respondents were willing to pay forextension services and were interested in using the PeMoST tool tosolve pest problems in tomatoes.
Pest popula ons were found to be high in demonstra on farms,
highligh ng the need for eﬀec ve pest management strategies.
The project studied the rela onship between land surface temperatureand pest occurrence, ﬁnding that higher temperatures led to increases
in Tuta Absoluta and whiteﬂy popula ons.
The PeMoST tool was designed based on datasets collected frombaseline surveys, pest popula on surveillance, pest occurrences data,and satellite data products for the last six years.

Next Steps
•

•

•

•

48

The project intends to proceed with the development of machinelearning models for monitoring changes in land surface temperaturesand soil surface moisture. This ac vity is ongoing, and the AI expert isundertaking this ac vity.
The development of the PeMoST tool has commenced and so far thedesigns have been ﬁnalized. The back-end and front-end processes
have started. The project team has organized a one-day workshop to
develop and consolidate all the databases required for the PeMoST
tool. This workshop is scheduled for the 12th of May 2023.
The wri ng of one paper for publica on is currently ongoing and a
dra will be ready for review by the various team members from the12th of May 2023. It is expected that the manuscript will be submi ed
for publica on by the end of May 2023.
It is an cipated that farm demonstra ons for the training of farmers
on the PeMoST tool will begin towards the end of June 2023 andproceed on for every season the crop will be grown by the Kathaana
vegetable growers. Currently, the team is working on the relevant
brochures and media material that will be required for the capacity
building of Kathaana vegetable growers farmers.

Proudly AI4AFS Hub

Project 7: Empowering Smallholder
Farmers (SHF) in Busia County using
Low-Cost IoT (Internet of Things) and AI(Ar ﬁcial Intelligence) Tools
Principal Inves gator: Dr. Betsy Muriithi
Organiza on: Strathmore University, Kenya
Grant Amount: US$ 53,000.00 (US Dollar Fi y-three thousand
only)
Grant Award Number: AFS-1465894639
Country of Implementa on: Kenya.
Project Partners
Co-PI 1: Dr Joyce Malinga
Organiza on: Kenya Agricultural Livestock and Research
Organiza on (KALRO)
Co-PI 2: Dr. Joseph Wabwire Masinde
Organiza on: Centre for Enterprising Communi es (CECO),
Kenya

Goal
The overall objec ve of the research is to provide localised
and ac onable gender-responsive climate informa on services
to help rural smallholder farmers become climate resilient andincrease yields and proﬁts without risking the crops or livestock from climate vagaries.

Objec ves
The speciﬁc objec ves of the study are to:•
To take on a GEI lens in understanding speciﬁc stakeholder
needs for climate informa on services related to weather,and climate in Kenya.
•
To design and prototype 4 low-cost automa c weather
sta ons using edge compu ng and IoT that will provide
accurate daily/weekly forecasts and seasonal forecasts forsmallholder crop farmers in Busia County.
•
To design localised gender-responsive climate informa on
services using the generated climate and weather
predic ons with agro-experts tailored to the needs of the
stakeholders.

Proudly AI4AFS Hub

49

•

To monitor and evaluate the use and eﬀec veness of
informa on and climate services by rural smallholder farmers
from a scaling perspec ve

Key ac vi es••••

Literature review of smallholder farmer access to climate
informa on and its impact on produc on
Stakeholder readiness assessment for low-cost smart weather
sta ons report
Assessment of capacity gaps and infrastructure needed for
digital climate informa on services.
Development of func onal block diagram and so ware
conﬁgura on including detailed hardware se ng up.

Key Achievements
1.

50

Through stakeholder engagement, we achieved alignment of
the project objec ves and the gaps felt by the smallholder
farmers in the region in terms of climate informa on services.
There was a consensus that the project objec ves aligned withthe ac vi es taking place around climate-smart agriculture.
The proposed technology also aligns with government planson climate-smart agriculture focusing on improving agrometeorological forecas ng and monitoring and advisory
services.

Proudly AI4AFS Hub

2.

3.

4.

Smallholder farmer training and engagementsmallholder farmer engagement was a success primarily
due to working with smallholder farmer groups ac vely
working with the county climate-smart agriculture
ac vi es. The smallholder farmers were recep ve to theproject and could iden fy gaps that can be addressed.
Discovery of the youth owning smart mobile devices
which informed on the of the delivery of the advisories.
A mobile applica on was seen as the most ideal form ofinterfacing with farmers compared to USSD which has
limited func onality.
Local Manufacturing - development of high-quality
electronic devices at minimal cost through local
Printed Circuit Board (PCB) manufacturing outlets. This
substan ally lowered the cost of produc on of one
mini-weather sta on.

Next Steps
The current phase of the project involves the alignmentof so ware and hardware elements to the speciﬁca ons
deﬁned. The next step is the tes ng of the devices in the ﬁeld.
A two-stage tes ng phase will be conducted by deploying asensor at the KARLOs farm laboratory as well as deploying
the devices in selected farms in Busia County. There areseveral possibili es for scaling up our project to reach a larger
number of smallholder farmers and have a broader impact.
Some poten al avenues for scaling up include geographic
expansion that will replicate and expand our opera ons toother geographic areas with similar agricultural contexts
and climate condi ons. This would involve establishingpartnerships with local organiza ons, governments, andfarmer coopera ves to ensure eﬀec ve implementa on
and sustainability. Partnering with exis ng farmer-facingorganiza ons, such as coopera ves, agribusinesses, or
agricultural extension services, can signiﬁcantly enhance
the scalability of our project. By integra ng our weather andclimate informa on services into their exis ng pla orms or
programs, we can leverage their networks and infrastructure
to reach a larger number of smallholder farmers.

Proudly AI4AFS Hub

51

Project 8: Building the ar ﬁcial
intelligence (AI) for soil moisture andnutrient monitoring under irrigatedagriculture among smallholder farmers,
academic and agriculture experts in
Malawi.

Principal Inves gator: Dr. Isaac Fandika
Organiza on: Department of Agricultural Research Services,
Malawi.
Grant Amount: US$ 54,000.00 (US Dollar Fi y-four thousand
only)
Grant Award Number: AFS-4193755526
Country of Implementa on: Malawi
Project Partners
Co-PI 1: Mr. Alinafe Emannuel Kaliwo
Organiza on: MECHRO Ltd, Malawi
Co-PI 2: Mr. Adam Rowland; Organiza on: SAHARA Ventures,
MalawiGoal
The overall goal for this project is to build ar ﬁcial intelligencefor soil moisture and nutrient monitoring under irrigatedagriculture to improve food availability and stability being
challenged by poor water and nutrient management amongsmallholder farmers and agriculture experts in Malawi.

Objec ves
The speciﬁc objec ves of the study are to:•
Improve water governance and water management at 30
irriga on schemes by commercially advancing irriga on
management services using suitable digital technologies
that are more eﬃcient at a lower cost to the small-scale
producer through a public-private partnership.
•
Eﬀec vely empower one private sector service provider,ﬁve private data collectors, 30 public extension staﬀ, and60 lead farmers on how to install, maintain and interpret

52

Proudly AI4AFS Hub••

AI tools (sensor) results for eﬃcient water and nutrient
management at 30 irriga on schemes in Malawi.
Properly reﬁne the data repor ng system and data
analy cs, so that each sensor array installed and eachmeasurement can be tracked to the individual farmer andthe data collector.
Eﬀec vely advocate farmer-friendly ar ﬁcial intelligenceuse, commercializa on and data management policy for
agriculture and food system in Malawi.

Key ac vi es
•
•
•
•

•
•

Incep on mee ng and project planning.
Mobilize and sensi ze farmers and stakeholders on the
project.
Set-up and operate public private partnership for AI tools
scaling.
Acquire and deploy AI tool for soil moisture and nutrient
monitoring under irrigated agriculture to enhancenutrient monitoring and use.
Develop training manuals for the AI tool
Training of end-user on the developed AI tool.

Key Achievements•••

Se ng-up an ins tu onal framework for A.I. scaling to
improve water governance and water management in
the irriga on sector in Malawi. This has been achieved
through mobilizing and sensi zing farmers at 30 irriga on
schemes, engaging private sector and civil society on
the applica on of AI for irriga on water and nutrient
management in Malawi. This is important because
farmers and stakeholder have been given knowledge
about AI4AFS and will par cipate in applying their use in
their respec ve schemes,
Strengthened the capacity of farmers and partners on
the use of A.I. tools (sensors) for improved irriga on andnutrient management in Malawi. Achieved by training a
total of 86 farmers (37 male and 49 females in Chiradzulu)
and 9 Staﬀ in Chiradzulu district on the applica on of AI
tools in agriculture and food system. This has been followedby acquisi on of AI tools for the actual monitoring.
Developed site and applica on to ﬁnd the correla on ofpair data parameters.

Proudly AI4AFS Hub

53

Next Steps
Project implementa on started in the middle of irrigatedfarming season, and it is an cipated that this year, prepara on,and eventual implementa on will be on me, thus early on setof irriga on period. The project team plans to install sensors,
conduct more data collec on and visualiza on, as well as
conduc ng advocacy on the AI tools and training in the districtsof Mwanza, Neno, Blantyre, Chiradzulu, Phalombe, Mulanje,
Chikwawa and Nsanje.

54

Proudly AI4AFS Hub

Project 9: TOLBI AI, an AI-based
digital tool for smart, sustainable andeﬃcient agriculture
Principal Inves gator: Mouhamadou Kebe
Organiza on: TOLBI, Senegal
Grant Amount: US$ 48,000.00 (US Dollar Forty-eight thousand
only)
Grant Award Number: AFS-5795725875
Country of Implementa on: Senegal
Project Partners
Co-PI 1: Mouhamadou Moustapha Cissé
Organiza on: African ins tute for Mathema cal Sciences,
SenegalCo-PI 2: Cheikh Ahmadou Bamba Fall
Organiza on: Yessal Agrihub, Senegal

Goal
The overall goal of the eTolbi AI project is to provide farmers,
producers and public organiza ons with real- me yield
forecas ng informa on and a ﬁeld management pla orm tomonitor plant health, fer liza on, and water requirements.

Objec ves
The speciﬁc objec ves of the study are to:•Develop robust and scalable AI models for Senegal and for
future deployments in other African countries.
•
Increase produc on and income of farmers and women
by 30% with more eﬃcient ﬁeld management based on
eTolbi AI decision support tool to op mize agricultural
inputs (fer liza on) and be er plant health management.
•
Reduce post-harvest losses by 60-80% with an AI-based
decision-making system and yield forecasts for producers
and public organiza ons (+6M acre, 18 months, 6 seasons)in Senegal.

Proudly AI4AFS Hub

55

Key ac vi es
•
•
•
•

•

Development of the state-of-the-art in Yield Forecast
model
Local language data collected (agriculture limited domain)
Design a system and Data architecture of TOLBI AI
Implementa on of the data and system architecture(Yield forecast L1(30m), L2(100m)) with the par cipa onof Tolbi and AIMS teams Incep on mee ng and projectplanning.
Data collec on in the Google Cloud/AWS servers avec

Key Achievements
The project team were able to:•
The project implemented a new innova ve methodology
in the collec on of spa al imagery data for precision
agriculture called crop remote labelling which will
eventually allow to automate the collec on of high and lowresolu on image data for prac ces such as classiﬁca on,
phenological modelling, monitoring of vegeta on status.
This will allow the industry and research ins tu ons tohave an innova ve tool to accelerate the development of
reliable solu ons and their introduc on on the market.
•
Data collec on in local language (limited to agriculture)
have been carried out.
•
Design of a new approach to collect high quality data to
train the yield predic on model•
Design of a TOLBI AI system and data architecture.
•
Developed state of the art yield forecas ng model.
•
Developed state of the art remote labelling data withsatellite imagery

Next Steps
•
•
•

56

Training and tes ng of the model with one or moreitera ons
Integra on of the models in a Geographic Informa on
SystemTraining and future users (Agent of the Hor culture
Department) and maintenance of the system

Proudly AI4AFS Hub

Project 10: Title of Project: Detec onof Crop Pests and Diseases on Web
and Mobile Devices using Deep
Learning
Principal Inves gator: Dr. Patrick Kwabena Mensah,
University of Energy and Natural Resources, Sunyani, Ghana
Grant Amount: US$ 49,000.00 (US Dollar Forty-nine thousand
only)
Grant Award Number: AFS-1233809296
Country of Implementa on: Ghana
Project Partners
Co-PI 1: Mr. Suweidu Abdulai
Organiza on: Ghana Developing Communi es Associa on,
Ghana
Co-PI 2: Mr. Francis Ata Amponsah
Organiza on: DIGILECT SYSTEM, Ghana

Goal
The overall goal of the project is to develop a mobile/web AIbased system with which farmers can detect crop illnesses andpests in the early stages.

Objec ves
The speciﬁc objec ves of the study are to:•Create a deep learning model to iden fy crop diseases
and pests on a variety of Ghanaian crops speciﬁcallymaize, tomato, cassava, and cashew.
•
Deploy mobile and web-based applica ons for theiden ﬁca on of maize, tomato, cassava, and cashew pest
and diseases.
•
Communicate the pest and disease iden ﬁed in the
popular Ghanaian language; “Twi” through the mobile/
web apps.
•
Generate environmentally friendly recommenda ons
such as pruning to control the disease/pest based on thestage of the infec on using the mobile/web apps.

Proudly AI4AFS Hub

57

Key ac vi es
The key ac vi es for the ﬁrst six (6) months where:•
Stakeholder feedback document.
•
Raw pests and disease datasets, one dataset for each of 4
crops (cassava, maize, tomato, cashew).
•
Labelled and annotated datasets for the four (4) crops•
Train deep learning (DL) models for each crop

Key Achievements
The key projects achievements iden ﬁed during the review ofthe progress report are as follows:
The project team were able to:
1. A successful stakeholder engagement: Stakeholders,
especially farmers in smaller communi es do not usually
see the importance and contribu ons of some of the
project ac vi es to their profession. They usually preferdirect support in the form of cash handouts with most ofthem not educated enough to understand the beneﬁts ofthe project ac vi es. However, our ac vi es during thestakeholder engagement and the data collec on phases
received massive support from the community members.
This is a posi ve sign that the communi es are willing toaccept and adopt AI to improve their livelihood.
2. Local dataset: Data in Africa is scarce. Many AI applica ons
from Africa depends on datasets collected outside of
the con nent. A successful collec on of local datasets
will help local AI models to generalize well based on thecondi ons in the study area.
3. Training of the deep learning models completed. The teamhave succeeded in training three deep learning models(one for each crop). The performance of the models,in terms of training, valida on and tests accuracies
are comparable the state-of-the-art models for therecogni on of complex images.

58

Proudly AI4AFS Hub

Next Steps

Bacterial Blight

The next phases of the project startwith the development of tensorﬂow lite
(TFLite) models from the exis ng Deep
Green MiteaLearning models. These TFLite models
will be deployed in the mobile app.
The mobile and web app development
process will also start in the next phase;
Mosaichowever, the AI models will be usedwithout conversion to TFLite models
for the web app. Based on preliminary
user requirements being gathered now,
prototypes will be developed based
Red rust
on which the user requirements will
be reﬁned a er this phase, paving theway for the actual deployment of the bmodels to mobile and the web. This will
Leaf Miner
give users the opportunity to detect
their crop images using the mobile
phone or the computer. A user manual
will subsequently be developed, andthe stakeholders iden ﬁed during the
Red rust
ini al stakeholder engagement will be
trained to use the app eﬀec vely. We
plan to pilot 5 e-kiosks in 5 communi es
to promote the adop on of the apps.
The e-kiosk a endant will act as an
immediate contact in the community and as an instructor tofarmers who have diﬃculty in accessing/using the apps. Twomore publica ons are expected to come out of the work in
the next phase; thus, the models, their carbon footprint, how
responsible they are in terms of AI, and the mobile and web
apps.

MosaicBacterial Blight

Healthy

Bacterial Blight

Brown Spot

Bacterial Blight

Healthy

Anthracnose

Red rust

Red rust

Anthracnose

Anthracnose

Sample images fromthe augmented (a)cassava and (b)cashew datasets

We hope to raise the interests of the farmers in using the apps
to enable us to increase the coverage to other crops such as
garden eggs, yam, plantain, etc. We will seek support from the
Ministry of Agric and non-governmental organiza ons to scale
up the project. If this is successful, the number of e-kiosks
will be increased, and farmers may be asked to contribute
marginally to sustain the project.

Proudly AI4AFS Hub

59

NOTES....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................

NOTES....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................

NOTES....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................
....................................................................................................................................................

For more Informa on Please Contact:
Dr. Nicholas Ozor
Execu ve Director, African Technology
Policy Studies Network (ATPS)
8th Floor Chancery Building, Valley Road
P. O. Box 10081-00100, Nairobi, Kenya
Tel: +254 (020) 2714092
Email: execu vedirector@atpsnet.org
Website: www.atpsnet.org

Proudly AI4AFS Hub