Citation

Babu, Tina and Nair, Rekha R. and Balusamy, Balamurugan and Khoh, Wee How and Nair Mogan, Jashila (2025) AgriFewNet—A Lightweight RGB Few-Shot Learning Model for Efficient Plant Disease Classification. Applied Sciences, 15 (23). p. 12787. ISSN 2076-3417

Text AgriFewNet—A Lightweight RGB Few-Shot Learning Model for Efficient Plant Disease Classification.pdf - Published Version Restricted to Repository staff only Download (64MB)

Abstract

The rapid growth of artificial intelligence (AI) has enabled efficient crop disease detection even in data-scarce agricultural settings. This study proposes AgriFewNet, a few-shot learning framework designed to improve classification accuracy using RGB imagery captured from publicly available datasets. The objective is to enable fast model adaptation to new disease classes using minimal labeled samples while maintaining high reliability in real-world conditions. AgriFewNet employs a hierarchical attention-enhanced ResNet-18 backbone incorporating dual spatial and channel attention to extract discriminative RGB features. A Model-Agnostic Meta-Learning (MAML) approach facilitates quick generalization to previously unexplored illness categories, while a prototype-based classifier guarantees compact representation learning. Using only RGB images, experiments on the PlantVillage and New PlantVillage datasets produced accuracies of 87.3% (1-shot), 94.8% (5-shot), and 97.1% (10-shot), surpassing leading few-shot baselines by as much as 7.9%. The findings show that AgriFewNet offers a resource-efficient and scalable method for intelligent crop monitoring, enhancing food security and precision agriculture.

Item Type:	Article
Uncontrolled Keywords:	Agricultural decision support
Subjects:	S Agriculture > S Agriculture (General)
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	06 Jan 2026 07:05
Last Modified:	06 Jan 2026 07:05
URII:	http://shdl.mmu.edu.my/id/eprint/15131

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