Integrated Ensemble Learning Techniques for Precision Crop Yield Prediction

Agriculture is an essential element of human civilization. It bolsters the economy while also offering nourishment. Plant leaves and crops are vulnerable to several diseases in agricultural practices. The agricultural sector's growing dependence on technology has facilitated the development of sophisticated data-driven approaches, with crop yield prediction becoming a pivotal emphasis. Precise crop production forecasting is crucial for strategic planning, optimal resource distribution, and enhancing agricultural output. Forecasting crop yields continues to be a significant difficulty in agriculture, since these predictions impact decision-making on global, regional, and individual crop scales. Historically, such forecasts have used several data sources, including agricultural, land, meteorological, atmospheric, and other relevant information. This research presents an innovative method for forecasting agricultural production with an ensemble learning model. The suggested system employs historical agricultural data, including district, crop year, season, and area. In recent years, the use of machine learning methods has become a significant analytical method for assessing agricultural production, therefore guiding choices related to crop selection and management strategies across the whole growing season. Diverse machine learning algorithms have been used in studies to predict agricultural yields. Our research presents a stacked ensemble model aimed at forecasting crop production. This study presents a stacking ensemble model that incorporates K-Nearest Neighbours Regressor, Random Forest Regressor, and Multiple Linear Regressor as base learners, with Decision Tree Regressor serving as the meta-learner. This ensemble method improves predictive performance by capitalizing on the strengths of each individual model while mitigating their flaws. This suggested system provides robust decision-making assistance for farmers and agricultural stakeholders, enabling them to make educated, data-driven decisions that improve sustainability and efficiency in agriculture.