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| Filter results4 paper(s) found. |
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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. Monitoring Corn (Zea mays) Yield using Sentinel-2 and Machine Learning for Precision Agriculture ApplicationsCurrently, there is a growing demand to apply precision agriculture (PA) management practices at agricultural fields expecting more efficient and more profitable management. One of PA principal components for site-specific management is crop yield monitoring which varies temporally between seasons and spatially within-field. In this study, we investigated the possibility of monitoring within-field variability of corn grain yield in a 22ha field located in Ferarra, North Italy. Archived yield data... A. Kayad, M. Sozzi, F. Pirotti, F. Marinello, L. Sartori, S. Gatto |
3. Mapping of micronutrients status in soils under multivarietal Citrus sinensis production for precision agricultureCitrus production in Nigeria is below the world average; and this is caused among other things by poor soil management. The situation is further acerbated by blanket fertilizer application and low application of precision in soil fertility management. A study was carried out on a 34 year old multi varietal citrus orchard under sweet orange (Citrus sinensis) to determine the current soil fertility status and variability of micronutrients. Soil samples were collected at a sampling depth... B.N. Okafor, B.N. Okafor, V. Aduaramigba, O. Denton |
4. 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 |