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Feasibility of machine learning-based rice yield prediction in India at the district level using climate reanalysis data
March 14, 2024, 4:41 a.m. | Djavan De Clercq, Adam Mahdi
cs.LG updates on arXiv.org arxiv.org
Abstract: Yield forecasting, the science of predicting agricultural productivity before the crop harvest occurs, helps a wide range of stakeholders make better decisions around agricultural planning. This study aims to investigate whether machine learning-based yield prediction models can capably predict Kharif season rice yields at the district level in India several months before the rice harvest takes place. The methodology involved training 19 machine learning models such as CatBoost, LightGBM, Orthogonal Matching Pursuit, and Extremely Randomized …
abstract arxiv climate cs.lg data decisions forecasting india machine machine learning planning prediction prediction models productivity science stakeholders study type
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