Artificial Bee Colony Algorithm-based Feature Selection and Hybrid ML Framework for Efficient Rice Yield Prediction

Abstract

India's economy predominantly depends on monsoon and agricultural output. Agribusiness products contribute to nearly a quarter of its gross domestic product and 58% of its population depends on agriculture for their livelihood. Certain crops, like rice, are vital to its food security being the most widely grown crop and accounting for one-third production of foodgrains in India. Understanding and enhancing its production is critical in ensuring food availability and promoting sustainable agricultural practices. Rice yield prediction has been a most researched area in the agriculture domain. Machine Learning (ML) frameworks have been found to perform well in patches with large, complex datasets as insufficient feature engineering and temporal dependencies plague efficacy. In this paper, we propose a swam-based meta-heuristic artificial bee colony (ABC) algorithm for feature selection from the dataset sourced from the Agricultural Production and Statistical Division of the Department of Agriculture Cooperation and Farmers Welfare, Government of India. The feature engineering is further optimized by a hybrid model comprising a convolutional neural network (CNN) for learning hierarchical representations and identifying relevant attributes from the complex dataset and long short-term memory (LSTM) for temporal aspects. Finally, a random forest (RF) regressor provides the benefits of ensemble learning, which merges multiple decision trees to remove bias, and variance and improve prediction accuracy. From the results, it is observed that the proposed hybrid model outperforms existing state-of-the-art standalone and hybrid models with the highest coefficient of determination (R2) and lowest mean square error (MSE) of 0.989 and 13613 respectively. The reliable and efficient hybrid model can aid farmers and policymakers in making informed decisions related to rice yield prediction leading to sustainable agricultural practices.