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| Filter results4 paper(s) found. |
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1. Potato Yield Prediction Using Multi-temporal Sentinel-2 Data and Multiple Linear RegressionTraditional potato growth models have a number of flaws, i.e., the cost of data collection, quality of input data, and the absence of spatial information in some cases. To address these challenges, we created a multiple linear regression model (MLRM) that uses the multi-temporal Sentinel-2 derived indices to predict potato yield. Along the growing season (from October 2019 to February 2020) eight Sentinel-2 imageries were collected, afterwards, the normalized difference vegetation index (NDVI)... M.E. Amin, M.A. Abdelfattah, E.S. Mohamed, A.A. Belal, M. Nabil, A.G. Mahmoud |
2. Post-Harvest Assessments of On-Farm Maize ExperimentationThe completion of the most recent maize-growing seasons in northern Cote d’Ivoire and Kenya (western and eastern regions) provided a first opportunity for farmers to share assessments from their participation in a new on-farm research initiative for sub-Saharan Africa called NUTCAT - meaning Nutrient-Catalyzed Agricultural Transformation. ... I. Adolwa |
3. Spatial-Temporal Assessment of Drought in the Northern Region, Ghana... B. Asante |
4. 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 |