ML-Based Insights for Crop Yield Forecasting in Indian Farming
Keywords:
Crop Yield Prediction, Machine Learning, Agricultural Data Analysis, Indian Agriculture, Regression Models, Ensemble Learning, Hybrid Models, SustainabilityAbstract
This study investigates the use of machine learning methods to forecast crop yields in Indian agriculture, using a large dataset that covers the period from 1997 to 2020. Data preparation methods, such as feature selection, one-hot encoding, and transformation, were used to improve the quality and appropriateness of the dataset for modelling. Individual regression models, including Decision Tree, Random Forest, Support Vector Machine (SVR), and K-Nearest Neighbours (KNN), were trained and assessed. These models showed strong performance in accurately forecasting crop yields. Furthermore, the integration of a hybrid ensemble model, which incorporates voting ensemble approaches, resulted in higher predicted accuracy and increased model resilience. Our technique stands out from previous approaches due to its uniqueness and improvements. These include the use of a bigger dataset, a longer time frame, and the use of hybrid ensemble models. The results provide useful insights for stakeholders engaged in agricultural decision-making, enabling informed allocation of resources and implementation of risk management measures to improve productivity and sustainability in Indian agriculture.
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Copyright (c) 2024 Nitish Kumar, Dr. Pankaj Richhariya
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