Download the Conference Proceedings
Proceedings
Authors
| Filter results4 paper(s) found. |
|---|
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. A geostatistical approach to define a soil fertility index based on the main soil macronutrientsSoil fertility is greatly affected by main soil macronutrients such as nitrogen (N), phosphorus (P), and potassium (K). These macronutrients can be used to define a synthetic fertility index to support soil fertilization. The study was aimed to propose a geostatistical approach to define a synthetic fertility index based on factorial cokriging. It consists in quantifying and reducing the spatial variability of multivariate data to only a few factors, related to different spatial scales. Such factors... H. Aboelkhier, A. Nasrallah, S. Shaddad, G. Buttafuoco |
3. Monitoring irrigation water use at large scale irrigated areas using remote sensing in water scarce environmentIncreasing pressure on available water resources in semi-arid region will affect the availability of water for irrigated agriculture. In this context, adoption of innovative and cost-effective tools for water management and analysis of water use patterns in irrigated areas is required for an efficient and sustainable use of water resources. This study aims to evaluate a remote sensing-based approach which allows estimation of the temporal and spatial distribution of crop evapotranspiration... M. Kharrou, V. Simonneaux, M. Le page, S. Er-raki, G. Boulet, J. Ezzahar, S. Khabba, A. Chehbouni |
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 |