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An enhanced deep learning-based framework for diagnosing apple leaf diseases
Chhaya Gupta1,2, Nasib Singh Gill1, Preeti Gulia3
1Department of Computer Science and Applications, Maharshi Dayanand University, Rohtak, India.
Scientific Reports
|November 12, 2025
Summary
This study presents E-YOLOv8, an efficient deep learning model for real-time apple leaf disease detection. It achieves high accuracy with significantly reduced computational cost, aiding sustainable agriculture.
Area of Science:
- Agricultural Science
- Computer Vision
- Machine Learning
Background:
- Accurate apple leaf disease identification is crucial for crop production and agricultural sustainability.
- Existing methods may lack efficiency or require substantial computational resources for real-time application.
Purpose of the Study:
- To introduce E-YOLOv8, a lightweight and improved version of YOLOv8, for real-time apple leaf disease detection.
- To enhance detection accuracy, especially for small lesions, while minimizing computational cost.
Main Methods:
- Developed E-YOLOv8 by integrating GhostConv and C3 fusion for efficient feature extraction.
- Incorporated CBAM attention and a custom FPN for improved multi-scale feature fusion.
- Conducted large-scale evaluations on apple leaf disease datasets and tested on edge devices.
Main Results:
- E-YOLOv8 achieved 93.9mAP0.5 with only 5.3 GFLOPs and 1.8 million parameters.
- Demonstrated a 33.9x reduction in computational cost compared to YOLOv8l.
- Outperformed recent state-of-the-art detectors in experiments.
Conclusions:
- E-YOLOv8 offers a highly accurate and computationally efficient solution for apple leaf disease detection.
- The model is suitable for real-time implementation on resource-limited edge devices in practical agricultural settings.
