This project uses deep convolutional neural networks to identify and classify crop pests based on images. Built with Flask, PyTorch, and MongoDB, the system includes a web-based interface for farmers and agricultural officers to view pest types, suggested pesticides, and submit queries. Designed for scalability and offline or online image capture, it supports rapid, accurate, and user-friendly pest detection for sustainable agriculture.
Pest Classification and Detection System
Pest Classification and Detection System
This project uses deep convolutional neural networks to identify and classify crop pests based on images. Built with Flask, PyTorch, and MongoDB, the system includes a web-based interface for farmers and agricultural officers to view pest types, suggested pesticides, and submit queries...
Organization
https://github.com/nikh-iam
Deployment Model
Edge
Agricultural Production Step
Pest management
End User
Developers
Category
Used for
Crops
TRL
6-7
Community Engagement
Low
Number of Forks
3
Country
Universal
Disclamer
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