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Attention-Based Recurrent Neural Network for Plant Disease Classification.
Sue Han Lee1, Hervé Goëau2,3, Pierre Bonnet2,3
1Swinburne University of Technology Sarawak Campus, Kuching, Malaysia.
Frontiers in Plant Science
|December 31, 2020
Summary
A new Recurrent Neural Network (RNN) approach accurately detects plant diseases by focusing on infected regions. This method improves upon Convolutional Neural Networks (CNNs) for robust disease classification and early detection in agriculture.
Area of Science:
- Agricultural Science
- Computer Vision
- Machine Learning
Background:
- Plant diseases significantly impact global food security and agricultural economies.
- Early detection and classification of plant diseases are crucial for effective control measures.
- Convolutional Neural Networks (CNNs) show promise for plant disease classification from RGB images but have limitations.
Purpose of the Study:
- To develop a novel Recurrent Neural Network (RNN) based technique for automatic plant disease detection and classification.
- To address limitations of CNNs in focusing on relevant diseased plant parts and ignoring irrelevant background information.
- To improve the robustness and generalization ability of plant disease classification systems.
Main Methods:
- Development of a Recurrent Neural Network (RNN) model for automated localization of infected plant regions.
- Extraction of relevant features from identified infected areas for disease classification.
- Experimental validation and comparison against classical Convolutional Neural Network (CNN) approaches.
Main Results:
- The RNN-based approach demonstrated superior robustness and generalization capabilities compared to traditional CNN models.
- The RNN model effectively focused on and accurately located infectious disease symptoms in plants.
- Experimental results confirmed the approach's ability to classify diseases across different crop species and domains.
Conclusions:
- The proposed RNN technique offers a more effective method for plant disease detection and classification.
- This approach enhances the ability to identify and locate plant pathogens, contributing to improved agricultural practices.
- The study highlights the potential of RNNs in developing advanced, automated systems for crop disease management.