• Rakhonde Vinith
• K. Srujan Raju
• Nutanakanti Bhaskar

The Automated Pest Detection project is a web application designed to assist farmers in identifying plant diseases and providing suitable solutions. The application utilizes a Convolutional Neural Network (CNN) model trained on a dataset comprising five classes for disease detection and 24 classes for disease classification. By leveraging image classification techniques, the app enables users to upload images of plants or crops that they suspect may be affected by diseases. Once an image is uploaded, it is sent to the server for analysis. The CNN classifier model is applied to the image, which then detects the presence of any disease and classifies it into the appropriate category. The identified disease is then displayed to the user along with recommended solutions to address the issue. The web application is built using Streamlit, a popular Python library for creating interactive web apps. Streamlit allows for seamless integration of the trained model and provides a user-friendly interface for farmers to easily upload images and receive disease detection results. This application empowers farmers to quickly identify plant diseases, facilitating timely intervention and promoting crop health. By automating the process of pest detection and disease classification, this project offers an efficient and accessible solution to support farmers in their efforts to monitor and maintain the health of their plants and crops.

Automated Pest Detection, Image Classification, Convolutional Neural Network (CNN), Disease Classification, Crop Management.

"Automated Pest Detection using Image Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 7, page no.b50-b56, July-2023, Available :http://www.jetir.org/papers/JETIR2307108.pdf

"Automated Pest Detection using Image Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 7, page no. ppb50-b56, July-2023, Available at : http://www.jetir.org/papers/JETIR2307108.pdf