Deep learning-based classification framework for precise plant disease identification
Abstract
The study introduces a hybrid Deep learning algorithm for precise plant disease identification and categorization. Leveraging Efficient-Net B0, VGG16, ResNet32, Alex-Net, and CNN, this model achieves a classification accuracy of 99.86%. The dataset includes 14 crops and 38 disease classes, with 87,867 images split into training (80%) and validation (20%). Efficient Net B0 emerges as the most effective architecture due to its superior accuracy and low loss. The classification process not only identifies plant diseases but also provides precise labels for further segmentation and treatment recommendations. This automated approach supports sustainable agriculture by optimizing resource use and improving disease management.
Fields of Study & Subjects
Smart Agriculture and AI
Plant Disease Management Techniques
Advanced Data and IoT Technologies
Publication details
- Journal / source
- Information and Communication Systems
- Publisher
- CRC Press
- Pages
- 647–652
- Publication date
- 10 March 2026
- Language
- EN
- Document type
- Book chapter