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

Authors: S. Vijaya Bharathi; A. Manikandan

Addresses: Scholar Computer Science, Periyar University, Tamil Nadu 636011, India ' Muthayammal Memorial College of Arts and Science, Tamil Nadu 637408, India

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.

DOI: 10.1504/IJIDS.2025.144259

International Journal of Information and Decision Sciences, 2025 Vol.17 No.1, pp.1 - 31

Received: 01 Mar 2022
Received in revised form: 01 Jun 2022
Accepted: 10 Jun 2022
Published online: 04 Feb 2025 *