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Sehbaoui, F

Ajeigbe, H.A

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Authors

Hmimou, M

Laamrani, A

Sehbaoui, F

Chehbouni, A

Khabba, S

Dhiba, D

Tukur, A

Ajeigbe, H.A

Akinseye, F.M

Mohammed, I.B

Badamasi, M.M

Sehbaoui, F

Topics

Precision Nutrient Management

Satellite Imagery

Plenary Session

Type

Poster

Oral

Year

2020

2022

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Authors

Filter results3 paper(s) found.
1. MAPPING AND ASSESSING AFRICAN SOILS FERTILITY USING HIGH-RESOLUTION REMOTE SENSING AND MACHINE LEARNING APPROACHES: STATE-OF-THE-ART AND PERSPECTIVES Africa 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 Download paper
2. Predicting in-Season Sorghum yield potential using Remote Sensing Approach: a case study of Kano in Sudan Savannah agro- ecological zone, Nigeria The preliminary estimation of expected yields and the accuracy of this evaluation provide information for decision-making related to the harvest. Estimating crop yield using remote sensing techniques has proven to be successful, having the ability to provide yield estimates prior to harvest. This study was conducted to examine the applicability of Sentinel-2B for estimating sorghum yield during the 2018 rainy season in Bebeji, Dawakin-Kudu and Rano Local Government Areas Kano State, in the Sudan... A. Tukur, H.A. Ajeigbe, F.M. Akinseye, I.B. Mohammed, M.M. Badamasi Download paper
3. Agricultural Data Market to Empower African Farmers By 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

Filter results3 paper(s) found.

1. MAPPING AND ASSESSING AFRICAN SOILS FERTILITY USING HIGH-RESOLUTION REMOTE SENSING AND MACHINE LEARNING APPROACHES: STATE-OF-THE-ART AND PERSPECTIVES

Africa 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

Download paper

2. Predicting in-Season Sorghum yield potential using Remote Sensing Approach: a case study of Kano in Sudan Savannah agro- ecological zone, Nigeria

The preliminary estimation of expected yields and the accuracy of this evaluation provide information for decision-making related to the harvest. Estimating crop yield using remote sensing techniques has proven to be successful, having the ability to provide yield estimates prior to harvest. This study was conducted to examine the applicability of Sentinel-2B for estimating sorghum yield during the 2018 rainy season in Bebeji, Dawakin-Kudu and Rano Local Government Areas Kano State, in the Sudan... A. Tukur, H.A. Ajeigbe, F.M. Akinseye, I.B. Mohammed, M.M. Badamasi

Download paper

3. Agricultural Data Market to Empower African Farmers

By 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