AI-Enabled Image Classification System for Crop Disease Monitoring
Students & Supervisors
Student Authors
Supervisors
Abstract
Disease in crops is a threat to global food production. Farmers in developing nations still visually inspect crops for signs of infection. The symptoms leading to delayed intervention and yield loss are often slow, inconsistent, manual process which cannot detect symptoms early on. In this study, we will try to detect crop diseases using image classification techniques based on deep learning. The model is trained on leaf images dataset scrapped from the internet. The images are pre-processed and augmented to include alterations in lighting, color, background noise and leaf texture. A convolutional neural network has been designed to categorize healthy leaves and multiple disease types. The model performed very efficiently with good accuracy and strong generalization on different types of leaves during testing. The system has great potential for use in real farming applications, especially when integrated into mobile applications or smart-farming applications that can work in the field. By having such deployment, farmers get instant diagnostic feedback, allowing for faster decisions, less unnecessary chemical usage, and ultimately helping to protect crop productivity. Overall, the article shows that AI-based approaches can provide a continuous plant health monitoring solution that is accessible, low-cost, and energy-efficient helping the efforts for sustainability and resilience in agriculture.
Keywords
Publication Details
- Type of Publication:
- Conference Name: Gazipur Agricultural University International Conference 2025
- Date of Conference: 12/12/2025 - 12/12/2025
- Venue: Gazipur Agricultural University
- Organizer: Gazipur Agricultural University