Crop Yield Prediction: A Comprehensive Review of Machine Learning and Deep Learning Approaches
Keywords:
Crop Yield Prediction, Machine Learning, Deep Learning, NDVI, Weather Data, Soil Parameter, Punjab AgricultureAbstract
Predicting crop yields accurately is essential for farm management, policymaking, and food security. With an emphasis on applications in India, this paper examines current developments in machine learning (ML) and deep learning (DL) techniques for crop yield estimation worldwide. The current study examines different obstacles (data scarcity, model transferability, interpretability), input variables (weather, soil, satellite indices), model types (regression, tree ensembles, neural networks, hybrid architectures), and future possibilities (transformers, multimodal fusion, IoT). In addition, this paper points out gaps and suggests recommended practices for further study.
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