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1. MAPPING AND ASSESSING AFRICAN SOILS FERTILITY USING HIGH-RESOLUTION REMOTE SENSING AND MACHINE LEARNING APPROACHES: STATE-OF-THE-ART AND PERSPECTIVESAfrica is far from exploiting its true agricultural potential. United Nations Food and Agriculture Organization (FAO) indicates that the continent has 60% of non-cultivated lands worldwide. While soil fertility is well highlighted as one of the major limiting factors, only limited information is available on soil nutrient contents and nutrient availability in the African soils. Soil fertility of agricultural fields is related to many physical and chemical properties, such as texture, organic matter... M. Hmimou, A. Laamrani, F. Sehbaoui, A. Chehbouni, S. Khabba, D. Dhiba |
2. Agricultural Data Market to Empower African FarmersBy transforming the agricultural data into agronomic advices by using AI model, farmer can get a strong tool to help them making the right decision in the right time. Decision about the quantity and the quality of fertilizer to apply, the quantity and the timing of the irrigation,… Also he can get valuable information about yield prediction, phytosanitary risk. All of this information can help famers reducing its operational cost by up to 30%. To develop robust AI model,... F. Sehbaoui |
3. Optimizing Durum Wheat Nitrogen Nutrition Index (NNI) Prediction Through Sentinel-2 Vegetation Index IntegrationNitrogen is crucial for durum wheat growth and productivity, but excess or insufficient levels can harm both the environment and farmers' finances. Remote sensing offers rapid, cost-effective, and nondestructive ways to assess crop nutrition, with vegetation indices (VIs) indicating plant health. This study aims to enhance the accuracy of durum wheat nitrogen status prediction by investigating modified formulations of Nitrogen Nutrition Index (NNI) coupled with various vegetation indices (VIs),... N. Boughattas, K. Marwa, Z. Mohamed, A. Sawsen, A. Soumaya, H. Hafedh, H. Imen, T. Youssef |