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| Filter results3 paper(s) found. |
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1. Spectral assessment of chickpea morpho-physiological traits from space, air and groundChickpea (Cicer arietinum) is an important grain legume in semi-arid regions and water-stress is a major constraint to its productivity. Area under chickpea cultivation is growing but climate change toward greater aridity results in higher precipitation instability and risks yields. The ability to assess water potential can support irrigation decisions. Thus, improved ability to spatially assess plants water status can promote more efficient irrigation. The current... I. Herrmann, R. Sadeh, A. Avneri, Y. Tubul, R. Lati, S. Abbo, D.J. Bonfil, Z. Peleg |
2. From Drone to Satellite – Does It Work?Multispectral drone-sensors are useful for detailed studies of crop characteristics in field trials, e.g. to create prediction models on nitrogen (N) uptake, or even estimates of optimal N rate to apply. To enable wide application of such models, they may be applied in satellite image-based decision support systems for farmers. However, successful transfer of models based on spectral data from one platform to another, requires strong and stable correlation between data from the different sensors.... M. Söderström, K. Persson |
3. 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 |