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Enhanced Leaf Disease Segmentation Using U-Net Architecture for Precision Agriculture: A Deep Learning Approach
Gurpreet Singh1, Asma A Al-Huqail2, Ahmad Almogren3
1Chitkara University Institute of Engineering and Technology Chitkara University Punjab India.
Food Science & Nutrition
|July 15, 2025
Summary
This study introduces a U-Net deep learning model for accurate leaf disease identification through image segmentation. The model precisely identifies diseased plant areas, aiding precision agriculture.
Area of Science:
- Computer Vision
- Plant Pathology
- Machine Learning
Background:
- Leaf diseases significantly impact crop yields.
- Accurate and early disease detection is crucial for effective management.
- Traditional methods often lack precision and scalability.
Purpose of the Study:
- To develop a deep learning model for precise leaf disease segmentation.
- To evaluate the U-Net architecture for identifying diseased leaf tissue.
- To enhance agricultural practices through automated disease identification.
Main Methods:
- Utilized the U-Net convolutional neural network (CNN) architecture.
- Trained and validated the model on a "Leaf Disease Segmentation" dataset with annotated images.
- Employed image preprocessing, augmentation, and regularization techniques.
- Used the Adam optimizer with a learning rate of 0.001.
Main Results:
- Achieved 99.70% training accuracy and 98.99% validation accuracy.
- Demonstrated high precision in segmenting diseased leaf regions at the pixel level.
- Showcased strong generalization capabilities on unseen data.
- Outperformed traditional image processing techniques.
Conclusions:
- The U-Net model offers a robust and accurate solution for leaf disease segmentation.
- Deep learning approaches are highly effective for plant disease identification.
- This technology has significant potential for real-world applications in precision agriculture.
