# Assessment of the Faidherbia albida effect on millet yield using UAV images analysis and geostatistical techniques

Serigne Mansour Diene, Romain Fernandez, Eric Goze, Ibrahima Diack, Marième Faye, Al Housseynou Dabo, Pape Oumar Ba Bousso, Alain Audebert, Olivier Roupsard, Louise Leroux, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

Serigne Mansour Diene, Romain Fernandez, Eric Goze, Ibrahima Diack, Marième Faye, et al. Assessment of the Faidherbia albida effect on millet yield using UAV images analysis and geostatistical techniques. EGU General Assembly 2023, Apr 2023, Vienna, Austria. 2 p., 10.5194/egusphere-egu23-7531. [https://doi.org/10.5194/egusphere-egu23-7531](https://doi.org/10.5194/egusphere-egu23-7531)

HAL Id: hal-05182481

https://hal.science/hal-05182481v1

Submitted on 23 Jul 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_3.png" /></figure>

EGU23-7531

https://doi.org/10.5194/egusphere-egu23-7531

EGU General Assembly 2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

# Assessment of the Faidherbia albida effect on millet yield using UAV images analysis and geostatistical techniques

Serigne Mansour Diene<fnref n="1" />,<fnref n="3" />, Romain Fernandez ID<fnref n="3" />, Eric Goze ID<fnref n="4" />, Ibrahima Diack<fnref n="1" />,<fnref n="4" />,<fnref n="6" />, Marième Faye<fnref n="7" />, Al Housseynou Dabo<fnref n="8" />, Pape Oumar Ba Bousso<fnref n="8" />, Alain Audebert<fnref n="3" />, Olivier Roupsard ID<fnref n="2" />, Louise Leroux<fnref n="4" />,<fnref n="5" />, Modou Mbaye ID<fnref n="9" />, Abdou Aziz Diouf<fnref n="6" />, Moussa Diallo<fnref n="1" />, and Idrissa Sarr<fnref n="1" />

Agroforestry, the association between trees/shrubs and crops, a widespread practice in West Africa, is presented as a lever for ecological intensification to optimize cereal yields in the face of strong population growth and the fight against climate change. Within the framework of the EU-DESIRA SustainSAHEL project, we aim to develop techniques to spatially assess the effect of trees on millet yields on an intra-field scale using imagery from an UAV equipped with a multispectral camera combined with geostatistical approaches. Indeed, recent advances in earth observation technologies position the UAV as an effective tool for evaluating the agronomic performance of agroforestry systems and for taking into account the intra-field variability of yields caused by environmental conditions, agricultural practices or the presence of trees (Roupsard and al., 2020; Leroux and al., 2022). The objective of this study was to estimate millet yields intra-field variability using UAV and up-to-date geostatistical approaches.

The study was carried out over the 2018-2022 cropping seasons in one representative Faidherbia parkland of the groundnut basin of Senegal. To that end, a Random Forest (RF) algorithm was first calibrated to estimate millet yield at sub-plot scale using a thresholding classification to eliminate non-vegetation elements and also to integrate texture data, in

<footnote n="1">¹University of Cheikh Anta Diop</footnote>

<footnote n="2">²CIRAD, UMR AGAP, Dakar, Senegal</footnote>

<footnote n="3">³CIRAD, UMR AGAP, Dakar, Senegal</footnote>

<footnote n="4">⁴CIRAD, UMR AIDA, Montpellier, France</footnote>

<footnote n="5">⁵IITA, ICIPE campus, Nairobi, Kenya</footnote>

<footnote n="6">⁶CSE, Dakar, Senegal</footnote>

<footnote n="7">⁷University Gaston Berger, Saint-Louis, Senegal</footnote>

<footnote n="8">⁸University Iba Der Thiam, Thiès, Senegal</footnote>

<footnote n="9">⁹CERAAS, ISRA, Thiès, Sénégal</footnote>
order to take into account the spatial relationships between pairs of pixels. Millet yields data and vegetation and textural index from aerial images at a flight height of 25 meters acquired in farmers' plots were used to calibrate the RF model. The RF model was used to upscale yield at the whole field scale thus allowing to obtain a map of millet yield. Then Voronoï diagram, with Faidherbia as a reference, was applied to each yield map, considering each Voronoï region as a zone of influence of its included Faidherbia. We then applied a transformation and rotation matrix to overlay all the zones of influence of a population of 50 Faidherbia by putting all the trees at the same geographical position. Finally, we build an atlas, which is an average structure representative of a population and which makes possible to detect the patterns and properties of the evolution of the population considered, to evaluate the distance and directional effect of Faidherbia on vegetation index of the population and then on millet yield.

The RF model is able to explain between 70 and 90 % of the millet yield variability. Then the analysis has shown that the tree has an influence on the millet stand density with a distance-decay effect from the tree. This stand density is about 60 % around the tree and 30 % at 15m from the tree.

Key words: Agroforestry, Uav, Machine learning, Image analysis, Geostatistics, Atlas

**How to cite:** Diene, S. M., Fernandez, R., Goze, E., Diack, I., Faye, M., Dabo, A. H., Bousso, P. O. B., Audebert, A., Roupsard, O., Leroux, L., Mbaye, M., Diouf, A. A., Diallo, M., and Sarr, I.: Assessment of the Faidherbia albida effect on millet yield using UAV images analysis and geostatistical techniques, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-7531, https://doi.org/10.5194/egusphere-egu23-7531, 2023.

Corresponding supplementary materials formerly uploaded have been withdrawn.

<figure><img src="image_6.png" /></figure>