Potato Leaf Disease Detection Based on a Lightweight Deep Learning Model

Traditional methods of agricultural disease detection rely primarily on manual observation, which is not only time-consuming and labor-intensive, but also prone to human error. The advent of deep learning has revolutionized plant disease detection by providing more accurate and efficient solutions....

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Bibliographic Details
Published in:Machine learning and knowledge extraction 2024-12, Vol.6 (4), p.2321-2335
Main Authors: Chang, Chao-Yun, Lai, Chih-Chin
Format: Article
Language:English
Subjects:
Accuracy
Agricultural production
agriculture
Algorithms
Analysis
Artificial neural networks
Classification
Crop diseases
Crop production
Crops
Datasets
Deep learning
Disease control
Diseases
Error detection
Food supply
Human error
Lightweight
lightweight convolutional neural network
Machine learning
Medical diagnosis
Neural networks
Performance evaluation
Plant diseases
Plant layout
Potatoes
Real time
Support vector machines
transfer learning
Weight reduction
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Summary:Traditional methods of agricultural disease detection rely primarily on manual observation, which is not only time-consuming and labor-intensive, but also prone to human error. The advent of deep learning has revolutionized plant disease detection by providing more accurate and efficient solutions. The management of potato diseases is critical to the agricultural industry, as these diseases can lead to substantial losses in crop production. The prompt identification and classification of potato leaf diseases are essential to mitigating such losses. In this paper, we present a novel approach that integrates a lightweight convolutional neural network architecture, RegNetY-400MF, with transfer learning techniques to accurately identify seven different types of potato leaf diseases. The proposed method not only enhances the precision of potato leaf disease detection but also reduces the computational and storage demands, with a mere 0.40 GFLOPs and a model size of 16.8 MB. This makes it well-suited for use on edge devices with limited resources, enabling real-time disease detection in agricultural environments. The experimental results demonstrated that the accuracy of the proposed method in identifying seven potato leaf diseases was 90.68%, providing a comprehensive solution for potato crop management.
ISSN:2504-4990
2504-4990
DOI:10.3390/make6040114