A novel hybrid meta-heuristic-enabled ensemble learning model with deep feature extraction for crop yield prediction with heuristic ensemble yield

S. Vijaya Bharathi and A. Manikandan

International Journal of Information and Decision Sciences, 2025, vol. 17, issue 1, 1-31

Abstract: The fundamental goal of this study is to build and use heuristic-based ensemble learning for improved agricultural production prediction. The squirrel tunicate swarm algorithm (STSA), a hybrid squirrel search algorithm (SSA) and tunicate swarm algorithm (TSA), extracts deep features using the optimised convolutional neural network (O-CNN). The datasets for agricultural production prediction are obtained from public sources, and deep features are extracted using an optimised convolutional neural network (O-CNN). Following that, the optimum deep features are exposed to heuristic-based ensemble learning using three distinct classifiers: linear regression (LR), support vector regression (SVR), and long-short-term-memory (LSTM) regression. The suggested STSA is utilised to calibrate the ensemble learning's three classifiers. When comparing the predicted performance of the developed model to that of other procedures, the proposed Heuristic ensemble yield framework beats previous techniques.

Keywords: novel crop yield prediction; deep feature extraction; optimised convolutional neural network; heuristic-based ensemble learning; squirrel tunicate swarm algorithm. (search for similar items in EconPapers)
Date: 2025
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