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

• URL: http://arxiv.org/abs/2601.02353v1
• Date: Mon, 05 Jan 2026 18:55:05 GMT
• Title: Meta-Learning Guided Pruning for Few-Shot Plant Pathology on Edge Devices
• Authors: Shahnawaz Alam, Mohammed Mudassir Uddin, Mohammed Kaif Pasha,
• Abstract summary: Farmers in remote areas need quick and reliable methods for identifying plant diseases.<n>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.<n>This paper addresses both challenges by combining neural network pruning with few-shot learning, which enables the model to learn from limited examples.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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