Multi-Objective Optimization for a Robust Deep Learning Model in Plant Image Classification: A Review with Trends, Challenges, and Future Directions

Annapriya Selvam; Mukil Alagirisamy; Fatemeh Ghyetasi; Waweru Njeri; Veerendra Dakulagi

doi:10.48084/etasr.17289

Authors

Annapriya Selvam School of Computing (SOC), Asia Pacific University of Technology & Innovation, Kuala Lumpur, Malaysia
Mukil Alagirisamy School of Engineering (SOE), Asia Pacific University of Technology & Innovation, Kuala Lumpur, Malaysia
Fatemeh Ghyetasi Department of Pharmaceutical Technology, Faculty of Pharmacy, University Malaya, Malaysia
Waweru Njeri Electrical & Electronic Engineering Department, Dedan Kimathi University of Technology, Nyeri, Kenya
Veerendra Dakulagi Department of CSE (Data Science), Guru Nanak Dev Engineering College, Bidar, Karnataka, India

Volume: 16 | Issue: 3 | Pages: 35644-35651 | June 2026 | https://doi.org/10.48084/etasr.17289

Received: 1 January 2026 | Revised: 23 January 2026 and 12 February 2026 | Accepted: 15 February 2026 | Online: 3 May 2026

Corresponding author: Mukil Alagirisamy

Abstract

This paper presents a comprehensive review of Multi-Objective Optimization (MOO) techniques for the enhancement of Deep Learning (DL)-based plant image classification by jointly addressing competing objectives such as classification accuracy, inference speed, generalization capability, and computational efficiency. Unlike existing research that treats DL architectures or optimization strategies in isolation, this work systematically integrates MOO principles with state-of-the-art DL models across the entire learning pipeline, including feature selection, hyperparameter tuning, and neural architecture search. Key evolutionary and hybrid MOO algorithms - such as NSGA-II, MOEA/D, MOPSO, and SPEA2 – were critically analyzed with respect to their applicability in agricultural imaging tasks. Practical challenges were further highlighted, arising from environmental variability, dataset imbalance, and field deployment constraints. By synthesizing current trends, identifying research gaps, and outlining future directions, this study positions MOO as a promising paradigm for developing robust, resource-aware, and field-deployable AI systems for precision agriculture.

Keywords:

detection, classification, Deep Learning (DL) model, hybrid approaches, plant disease

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