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• W4308273695 abstract "Regular and faster crop yield prediction can mitigate the extreme effects of severe weather events, such as drought, heavy rainfall, etc. This work explores a precise, scalable, and automatic way to understand rice yield dynamics and its correlation with satellite-based solar-induced fluorescence (SIF), climate variables, such as temperature and rainfall, as they exhibit a significant correlation with rice yield. A district-wise analysis in Punjab, India, is carried out using Pearson correlation coefficient and different regression techniques, such as linear, ridge, lasso, and elastic net for the Kharif season. A comparative study shows that the elastic net performs better than the other models, with the best coefficient of determination (R <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> ) of 0.792 and root mean square error (RMSE) of 300.5 kg/ha. This study can be extended in multiple dimensions by including a variety of crops, climate factors, and multi-satellite SIF data for any crop yield pattern analysis and prediction." @default.
• W4308273695 created "2022-11-10" @default.
• W4308273695 creator A5019016567 @default.
• W4308273695 creator A5052563081 @default.
• W4308273695 date "2022-09-16" @default.
• W4308273695 modified "2023-09-23" @default.
• W4308273695 title "Crop Yield Analysis using SIF and Climate Variables: A Case Study in Punjab, India" @default.
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• W4308273695 doi "https://doi.org/10.1109/r10-htc54060.2022.9929744" @default.
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