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A novel efficient eggplant disease detection method with multi-scale learning and edge feature enhancement
Hao Sun1,2, Rui Fu1, Dae-Ki Kang2
1Shandong Facility Horticulture Bioengineering Research Center, Weifang University of Science and Technology, Weifang, China.
Frontiers in Plant Science
|October 6, 2025
Summary
A new eggplant disease detection network enhances edge features using multi-scale learning, improving accuracy in smart agriculture. This advanced model achieves high performance on complex backgrounds, offering an effective solution for crop disease identification.
Area of Science:
- Agricultural technology
- Computer vision
- Plant pathology
Background:
- Accurate crop disease detection is crucial for smart agriculture.
- Challenges include diverse disease scales, indistinct edge features, and complex backgrounds.
- Existing methods struggle with these complexities, impacting detection effectiveness.
Purpose of the Study:
- To develop an advanced eggplant disease detection network.
- To enhance edge feature extraction and multi-scale learning capabilities.
- To improve detection accuracy, especially in challenging agricultural environments.
Main Methods:
- Proposed a "backbone-neck-head" architecture for disease detection.
- Introduced the Multi-scale Edge Information Enhance (CSP-MSEIE) module for scale-specific feature extraction.
- Integrated Multi-source Interaction Module (MSIM) and Dynamic Interpolation Interaction Module (DIIM) for enhanced multi-scale representation and complex background handling.
- Developed the Multi-scale Context Reconstruction Pyramid Network (MCRPN) for spatial feature reconstruction and context extraction.
Main Results:
- The proposed model achieved significant improvements on eggplant disease datasets.
- mAP50 increased by 4.7% and mAP50-95 by 7.2% compared to baseline methods.
- The model demonstrated high efficiency with a Frames Per Second (FPS) of 270.5.
Conclusions:
- The developed network effectively addresses challenges in eggplant disease detection.
- Edge feature enhancement and multi-scale learning are key to improving accuracy.
- This provides a robust and efficient solution for smart agriculture applications.
