Meta-Learning Guided Pruning for Few-Shot Plant Pathology on Edge Devices
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911689865166848 |
|---|---|
| author | Uddin, Mohammed Mudassir Alam, Shahnawaz Pasha, Mohammed Kaif Rehman, Dr Tasneem Bano Taranum, Dr Fahmina Begum, Afroze |
| author_facet | Uddin, Mohammed Mudassir Alam, Shahnawaz Pasha, Mohammed Kaif Rehman, Dr Tasneem Bano Taranum, Dr Fahmina Begum, Afroze |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_02353 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Meta-Learning Guided Pruning for Few-Shot Plant Pathology on Edge Devices Uddin, Mohammed Mudassir Alam, Shahnawaz Pasha, Mohammed Kaif Rehman, Dr Tasneem Bano Taranum, Dr Fahmina Begum, Afroze Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Meta-Learning Guided Pruning for Few-Shot Plant Pathology on Edge Devices |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2601.02353 |