March 14, 2024, 4:41 a.m. | Djavan De Clercq, Adam Mahdi

cs.LG updates on

arXiv:2403.07967v1 Announce Type: new
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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