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| Filter results3 paper(s) found. |
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1. Using Remote Sensing to Develop Site-Specific Nitrogen Management in Citrus OrchardsIntegrating multivariate spatial analysis with the delineation of site-specific management zones (MZ) provides a basis for practical and cost-effective management of water and nitrogen (N) fertilization in precision agricultural (PA). In many crops, measurements of leaf N content are used to assess the plant’s nutritional status and to develop fertilizer application plans for optimal yields. Accordingly, the aim of this study was to develop leaf N content prediction for citrus based on multispectral... E. Rave, N. Ohana, R. linker, D. Termin, A. Beryozkin, T. Paz-kagan, S. Baram |
2. 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 |
3. An Ensemble-Based Deep Learning Approach for Early and Accurate Wheat Disease DetectionCrop diseases are the primarily cause for yield loss and a factor for food security issue around the globe. Crop diseases caused by pathogens pose a significant threat to global food security, the challenge become worst particularly in developing countries like Ethiopia. Rapid population growth and accurate disease identification is crucial for timely intervention and minimizing crop losses. However, traditional methods often rely on expert analysis, which can be time-consuming and resource-intensive.... T. Aboneh, P. Rorissa |