Automated Pest and Disease Detection Using Indian Crop Image Data

Objective: To develop and validate an image-based system for early detection of pests and diseases in Indian crops to aid farmers and agri-businesses.

Indian agriculture suffers significant losses each year due to late or inaccurate detection of pests and diseases. Many smallholder farmers lack access to expert guidance, and manual scouting is labor-intensive and inconsistent. There is an urgent need for scalable solutions that leverage technology for early detection.

The team will collect and use crop images from Indian datasets, apply machine learning and computer vision techniques to identify common pests and diseases, and build a user-friendly prototype suitable for field deployment. The methodology involves data preprocessing, model development, and field validation.

Deliverables include an annotated dataset, trained image classification model, prototype mobile/web interface, and a summary analysis of accuracy and usability. The team will also benchmark results against existing manual detection and propose operational recommendations.

This project can significantly improve yield protection, reduce input costs, and inform agri-business decisions related to risk management and extension services. The solution will empower farmers and help stakeholders prioritize interventions based on real-time insights.