Managing Organization (Hub) for Responsible Artificial Intelligence for Agriculture
and Food Systems (AI4AFS) Innovation Research Network in Africa

AFRICAI CONFERENCE 2023 PARTICIPATION AND MID-TERM
REVIEW AND EVALUATION REPORT

Date:
12-14 June 2023

Place of Review:
Radisson Blu, Kigali, Rwanda
Submitted by:

Dr. Nicholas Ozor
Executive Director
African Technology Policy Studies Network (ATPS)The Chancery, 8th Floor, Valley Road
P.O. Box 10081-00100, Nairobi, Kenya
executivedirector@atpsnet.org; nozor@atpsnet.org
Telephone: +254202714092;
Cell Phone: +254 713161928; +254 734777708
Website : https://atpsnet.org/

Submitted to:

Gillian Dowie
International Development Research Center (IDRC)
Email: AI4DAfrica@idrc.ca

Date:

September 2023

Executive Summary
This mid-term evaluation report assesses the progress and performance of the Managing
Organization (Hub) for Responsible Artificial Intelligence for Agriculture and Food Systems (AI4AFS)
Innovation Research Network in Africa project during the reporting period from October 2021 toSeptember 2023. The project has made significant strides in achieving its objectives, with key
milestones reached and positive impacts observed among target beneficiaries. Grantee selection
was conducted transparently and in compliance with established criteria. Grantees have showncommendable performance, although some capacity-building needs have been identified andtraining taking place to build the capacity of the grantees. Financial management has been
efficient, with no major discrepancies except for the re-allocation of funds from the costs oftranslation to the Electronic Grant management system Subscription Renewal.
Introduction
The Managing Organization (Hub) for Responsible Artificial Intelligence for Agriculture and Food Systems(AI4AFS) Innovation Research Network in Africa project was initiated to advance the responsible
development, deployment and scaling of home-grown AI innovations to tackle pressing
challenges in agriculture and food systems in Africa. Achieving this objective would make asignificant contribution to agriculture, food and nutritional security as well as enhance thelivelihoods of African people especially women and youth who form most of the small-scale
farmers in Africa. By developing, deploying and scaling AI innovations that are homegrown,
peculiar agricultural production and productivity challenges unique to Africa can be addressed.
The mid-term evaluation aims to provide insights into the project's progress, grantee
performance, and its impact on target beneficiaries.
Methodology
The evaluation employed a mixed-methods approach, combining document review, grantees
presentation (physical), virtual meetings, and on-site visits. Data was collected from project
records, grantee reports, and direct interactions with stakeholders. While every effort was madeto ensure accuracy, limitations include the inability to physically visit all grantees to have a firsthand observation of projects.
Project Progress
During the reporting period, the project successfully implemented all key activities which include;
project launch, establishment of the Hub Management Committee (HMC) and other key
personnel, development and issuance of call for Expression of Interest (EoI), selection of quality
Concept Notes, training of shortlisted applicants on key aspects of AI and AFS and on full proposal
development, development and issuance of Call for full Proposals (CfPs), identification of external
expert reviewers and evaluation of proposals, selection of the research network grantees, thesigning of sub-grant agreements and inception meeting, conduct capacity needs assessment ofnetwork grantees, training of network grantees, mid-term review meeting and cross-learning
opportunity, continuous monitoring, evaluation and learning, data archiving and storage, financial
audit, documentation of proven AI research and innovations that have been developed, deployed
and up-scaled for AFS under the research network, and research synthesis and knowledge
mobilization Notable achievements include selection of network grantees that is equitably
distributed and gender balance, training of the grantees in identified areas of need, organizationof conference session for knowledge dissemination and participation in the 1st Africa AI
conference in Kigali, Rwanda where the network influenced international and African policy
1discussion providing strong evidence for reforms and use of AI to improve Agriculture and foodsystems. Challenges encountered included the selection of experts for the evaluation of proposals,
identification of female AI experts, and tokenism in engaging women researchers in the AI field.
Grantee Selection Process
The grantee selection process adhered to the rigorous criteria outlined in the grant guidelines. Itwas conducted transparently, with careful consideration of applicants' qualifications and project
proposals. Ten grantees from eight countries in sub-Saharan Africa with equal representation ofboth gender were selected and award grants amounting to between 40,000 USD to 60,000 USD.
Grantee Performance
Individual grantees have demonstrated satisfactory performance in meeting project goals. TheAfricaAI conference 2023 offered an opportunity to individually assess for each grantee is doing as
it relates to their specific project objective and the AI4AFS overall project goal. On the 13 th of June,
2023 during the parallel session on Advancing Responsible and Open AI in Agriculture and Food
Systems in Africa, held at the Intel Room (AD4), Radisson Blu Hotel and Convention Centre, Kigali,Rwanda, each grantee made a 10 minutes presentation of their projects. The presentation
involved stating the clear objectives of the projects, activities that have been undertaken,
pathways to gender equality and inclusion, communication and coordination among project
partners, and key outputs and outcomes. These presentations were compared with their statedactivities and principles guiding the AI4AFS Network by a panel of experts drawn for the AI4AFS
partners and an international gender inclusion expert, Dr. Shannon. Compliance with grant terms
and conditions has been observed. Some images from the meeting are presented below as Figure
1-5.

2

3

Evaluation of Grantees
The results of each grantee's project evaluation are in the link: Grantees Project Mid-Term
Evaluation. The overall average score of 7 out of 10 from the experts shows that the grantees are
successfully implementing their project. The overall grantees' project review presentations are inthe link Presentation at the AfricaAI conference 2023. Some project outputs have been recordedby the grantees; some include the development of mobile applications for Cashew and Maize
disease detection in Ghana, the development of a mobile application for early disease diagnosis inUganda, and the development of an AI-based Raspberry PI Nsukka yellow pepper disease
detection in Nigeria. Other outputs such as publication and media engagement have beenreported in the 4th technical progress report.
Overall performance of the grantees
The network grantees' overall performance as a team was assessed collectively to understand if
as a group their projects are aligning with the overall objectives of the AI4AFS project. The analysis
below presents the team performance against selected questions which aligns with the core goalof the AI4AFS project. Mid-Term Project Evaluation Form: Artificial Intelligence Applications inAgriculture and Food Systems. The scores will be used to determine further support to theGrantees. The instruction to the experts was to provide honest and thoughtful feedback by ratingeach criterion on a scale of 1 to 5, where 1 represents "Poor" and 5 represents "Excellent."
Additionally, provide comments or suggestions where necessary. (1=Poor, 2= Fair, 3= Good, 4=Very Good, and 5= Excellent)
A. The ability of the project team to meet AI4AFS project objectives.
Figure A below shows that the experts believed that the grantees met the project objectives in avery good way with a score of 73.9% and in an excellent manner with a score of 21.7%.

Figure A: Assessment of grantees ability to meet project objectives.
B. Assessment of grantees' project's adherence to responsible AI principles? (Transparency, Fairness
and Non-Discrimination, Safety and Reliability)

Figure B below shows that the experts believed that the grantees adhered to RAI principles toa large extent scoring the projects 69.6% of the projects very good and 13% as excellent. Thisshows that the projects are utilizing the various capacity-building initiatives of the network toa great advantage.

4

Figure B: Assessment of grantees adherence to RAI principles.
C. Assessment of grantees projects' potential for scale-up

Figure C below shows that the experts believed that the 47.8% and 26.1% of the projects havevery good and excellent potential for scale-up, while 21.7% have good potential for scale-up.
Overall the grantees project has potential for scale-up.

Figure C: Assessment of grantees projects’ potential for scale-up.
D. Assessment of projects’ integration of Gender Equality and Social Inclusion (GESI) Artificial
Intelligence Development and Deployment.

Figure D below shows that the experts believed that 43.5% and 39.1% of the projects had very
good and excellent integration of GESI principles in the implementation of their projects, while
4.3% each had fair and poor integration of GESI principles. The result illustrates the depth ofsupport received from the AI4D on GESI considerations in the AI4AFS project implementation.

Figure D: Assessment of grantees projects’ integration of GESI principles.
E. The extent of evidence being generated from the projects will inform policy and practice in theagriculture and food systems.

5Figure E below shows that the experts agreed that 78.3% and 8.7% of the projects have very
good and excellent opportunities to influence policy and practice from the evidence
generated. This shows that the network is on course to deliver on its objectives.

Figure E: Assessment of the extent the evidence generated will inform policy and practice inAFS.
F. Overall comment of the evaluators

The projects are characterized by high resource input and strong partnerships with stakeholders,making it practical for smallholder setups, particularly in extension services. It has room for morepartnerships and the potential to improve farm productivity. However, some commenters
expressed concerns about its pacing and the need for evidence of progress. Nevertheless, almost
all the projects were considered viable projects for smallholder farmers with scalability potential.
The inclusion of a carbon footprint in the CNN models is seen as a positive aspect of the projects’implementation. Most evaluators find the projects impactful and on track, making it an overall
good project that requires close monitoring during implementation.
The comments of the external participants on the effectiveness of the AI4AFS project during theparallel session at the AfricaAI Conference 2023, show that the overall project implementation isa success. The network valued the feedback from the grantees on how the management of theproject has evolved and the overall outlook of the project implementation. The feedback videos
from the grantees are found in the following link: AI4AFS grantees project implementation
feedback video 1 and video 2. The pictures, list of attendees and programme of the AI4AFS session
during the AfricaAI conference 2023 are found in the links for photos, list of attendees and session
programme.
Impact on Target Beneficiaries
The project has positively impacted 100 farmers across the 8 countries such as improving accessto education, understanding improved water monitoring and conservation systems, and improved
capacity to develop, deploy, and scale responsible AI tools in agriculture and food systems inAfrica.
Conclusion
The mid-term evaluation indicates that the AI4AFS project is on track to achieve its objectives.
Grantee selection has been transparent, and performance is generally commendable. The project
has had a positive impact on beneficiaries. Through the review, the project management
committee will further enhance the project's effectiveness and ensure its successful completion.

6