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AI based real time disease diagnosis in plants using deep learning driven CNNs.
D Devarajan1, Randa Allafi2, Marwa Obayya3
1Department of Electronics and Communication Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu, India, 611002. devarajand@ymail.com.
Scientific Reports
|January 6, 2026
Summary
This study introduces a Deep Learning framework for real-time plant disease diagnosis. The AI model accurately identifies diseases from images, enabling faster, more reliable crop health management and reducing yield loss.
Area of Science:
- Agricultural Science
- Computer Science
- Artificial Intelligence
Background:
- Traditional plant disease diagnosis is slow, labor-intensive, and prone to human error.
- Current methods are unsuitable for large-scale crop systems requiring real-time, accurate diagnostics.
- Early detection is crucial for maximizing crop yield and minimizing losses.
Purpose of the Study:
- To develop and validate a real-time plant disease diagnosis system using deep learning.
- To improve the speed, accuracy, and scalability of plant disease detection.
- To support precision agriculture and sustainable plant health management.
Main Methods:
- Implementation of a Plant Disease Diagnosis using Deep Learning (PDD-DL) framework.
- Utilizing Convolutional Neural Networks (CNNs) for automated analysis of plant images.
- Model validation on common crops with potential for retraining on diverse disease classes.
Main Results:
- The PDD-DL model achieved high performance metrics: 98.32% accuracy, 97.85% precision, 98.14% recall, and 97.99% F1-score.
- Real-time inference speed of 42.6 ms per image demonstrates system efficiency.
- The model effectively differentiates between healthy and diseased plants.
Conclusions:
- Deep learning, specifically CNNs, offers a faster, more trustworthy, and scalable alternative to traditional plant disease diagnosis.
- The developed framework enhances accuracy and speed in diagnosing plant diseases.
- This technology aids in precision agriculture and promotes sustainable crop management practices.
