Meta-Learning Guided Pruning for Few-Shot Plant Pathology on Edge Devices

TL;DR

This paper introduces a neural network pruning and few-shot learning method, DACIS, enabling efficient, accurate plant disease detection on low-power edge devices like Raspberry Pi.

Contribution

It proposes a novel pruning technique combined with meta-learning, achieving significant model compression while maintaining high accuracy for plant disease classification.

Findings

Reduced model size by 78%

Maintained 92.3% accuracy

Achieved 7 fps on Raspberry Pi 4

Abstract

Farmers in remote areas need quick and reliable methods for identifying plant diseases, yet they often lack access to laboratories or high-performance computing resources. Deep learning models can detect diseases from leaf images with high accuracy, but these models are typically too large and computationally expensive to run on low-cost edge devices such as Raspberry Pi. Furthermore, collecting thousands of labeled disease images for training is both expensive and time-consuming. This paper addresses both challenges by combining neural network pruning, removing unnecessary parts of the model, with few-shot learning, which enables the model to learn from limited examples. This paper proposes Disease-Aware Channel Importance Scoring (DACIS), a method that identifies which parts of the neural network are most important for distinguishing between different plant diseases, integrated into 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, with the compressed model running at 7 frames per second on a Raspberry Pi 4, making real-time field diagnosis practical for smallholder farmers.