December 6, 2025Open Access

AgriFewNet—A Lightweight RGB Few-Shot Learning Model for Efficient Plant Disease Classification

Key Points

Classification accuracy in few-shot learning reached 97.1% using RGB images from public datasets.
Few-shot learning framework enables adaptation to new disease classes with minimal labeled samples.
AgriFewNet employs a hierarchical attention-enhanced ResNet-18 with dual attention mechanisms for feature extraction.
Study highlights potential for precision agriculture through efficient crop disease monitoring.

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.

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