Meta-Learning Guided Pruning for Few-Shot Plant Pathology on Edge Devices
This work addresses the challenge of limited computational resources for smallholder farmers in agricultural AI, though it is incremental as it combines existing techniques like pruning and meta-learning.
The paper tackles the problem of deploying plant disease detection on edge devices by introducing a pruning and meta-learning framework that reduces model size by 78% while maintaining 92.3% of original accuracy, achieving 7 FPS on a Raspberry Pi 4 for real-time field diagnosis.
A key challenge in agricultural AI is deploying disease detection systems in remote fields with limited access to laboratories or high-performance computing (HPC) resources. While deep learning (DL) models, specifically deep convolutional networks, achieve high accuracy in identifying plant pathologies from leaf imagery, their memory footprints and computational demands limit edge deployment on devices constrained by battery life, processing power, and connectivity, such as Raspberry Pi. Few-shot learning (FSL) paradigms offer a compelling solution to the data scarcity problem inherent in agricultural applications, where obtaining labeled samples for novel disease variants proves both costly and time-sensitive. This work introduces a framework combining pruning with meta-learning for agricultural disease classification, addressing the tension between generalization capability and deployment feasibility. The proposed approach combines a novel Disease-Aware Channel Importance Scoring (DACIS) mechanism with a three-stage Prune-then-Meta-Learn-then-Prune (PMP) pipeline. Experiments on PlantVillage and PlantDoc datasets demonstrate that the proposed approach reduces model size by 78\% while maintaining 92.3\% of the original accuracy. The compressed model achieves 7 frames per second (FPS) on a Raspberry Pi 4, enabling practical real-time field diagnosis for smallholder farmers.