Authors:
Pascal Faye
1
;
Jeanne Faye
2
and
Mariane Senghor
1
Affiliations:
1
Université du Sine Saloum El Hadj Ibrahima NIASS (USSEIN), Sing Sing, Kaolack, Senegal
;
2
Université Aix-Marseille, Institut de Management Public et Gouvernance Territoriale, Aix-Marseille, France
Keyword(s):
Agriculture Calendar, Internet of Things, Data Analysis, Artificial Intelligence, Crop Yield.
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 i
n agricultural yields.
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