Machine Learning for a Better Agriculture Calendar

Pascal Faye, Jeanne Faye, Mariane Senghor

2024

Abstract

In Senegal, agriculture is subsistence, low-input, and significantly less mechanized than many other nations in Africa, and is also highly dependent on soil, climate, and water. Food crops take up to 46% of the total land and make up 15% of the Gross Domestic Product (GDP), ensuring between 70% and 75% employment. In this work, we provide a set of mechanisms that uses a set of trust database of agro-climatic parameters and a set of artificial intelligence algorithm in order to assess agricultural calendar for a good distribution of the farm’s activities over time and find the relationship between crops. Our results show the effectiveness of our solution to overcome the abandonment of agricultural perimeters or an agriculture depending on the raining season. That means, taking these data into account makes possible to understand crops dependencies and anticipate the agroecological phenomena, the crop diseases and pests that impact the planning of production facilities and variations in agricultural yields.

Paper Citation

in Harvard Style

Faye P., Faye J. and Senghor M. (2024). Machine Learning for a Better Agriculture Calendar. In Proceedings of the 19th International Conference on Software Technologies - Volume 1: ICSOFT; ISBN 978-989-758-706-1, SciTePress, pages 307-314. DOI: 10.5220/0012691800003753

in Bibtex Style

@conference{icsoft24,
author={Pascal Faye and Jeanne Faye and Mariane Senghor},
title={Machine Learning for a Better Agriculture Calendar},
booktitle={Proceedings of the 19th International Conference on Software Technologies - Volume 1: ICSOFT},
year={2024},
pages={307-314},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012691800003753},
isbn={978-989-758-706-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Software Technologies - Volume 1: ICSOFT
TI - Machine Learning for a Better Agriculture Calendar
SN - 978-989-758-706-1
AU - Faye P.
AU - Faye J.
AU - Senghor M.
PY - 2024
SP - 307
EP - 314
DO - 10.5220/0012691800003753
PB - SciTePress