Enhancing Precision Agriculture through Stacked Ensemble Model and Interpretability

The varied soil conditions across India pose a notable challenge for farmers to select the right crops. This leads to under-utilized resources and restricts agricultural output, ultimately contributing to the global shortage of food. Given the increasing world population, there is a necessity to increase productivity in agriculture to meet the rising demand for food. To address this, This research aims to revolutionise precision agriculture by integrating a Crop Recommender System (CRS) with a Weighted Stacked Generalization Ensemble architecture enhanced with interpretability features by designating the Long Short-Term Memory (LSTM) model as the meta-classifier within the ensemble framework. LSTM, with abilities to learn effectively from sequential data, is the overarching model that combines the predictions of various base learners. The ability of Local Interpretable Model-agnostic Explanations (LIME) to deliver transparent insights for the decision-making processes intends to improve the interpretability of the prediction in practical agricultural contexts. The focus of this work is to improve the precision of crop recommendations’ by amalgamating diverse models’ predictive capabilities by leveraging LSTM as the meta-classifier combined with stacked ensemble learning integrated with LIME interpretations which represents a significant advancement in providing farmers with reliable decision-support tools. The proposed system enables informed decision-making and facilitates the adoption of precision agriculture technologies.