Deep Learning-Based Web Application for Crop Disease Detection Using CNN and Streamlet

Deep Learning-Based Web Application for Crop Disease Detection Using CNN and Streamlet

Sushma B, Benaka Raj,

1Assistant Professor, Computer Science Department, JSS College of Arts Commerce and Science Ooty-Road, Mysuru.

2U.G. Student, Computer Science Department, JSS College of Arts Commerce and Science Ooty-Road, Mysuru

Abstract - Crop diseases can cause significant yield losses and threaten food security if not diagnosed and treated in time. In this paper, we present a real-time web application for crop disease detection using deep learning techniques, primarily Convolutional Neural Networks (CNNs). The application allows farmers to upload leaf images through a Streamlet interface, which are then analyzed by a trained CNN model to detect diseases. The system provides fast, accurate predictions along with suggestions for treatments, promoting smart and sustainable farming. For scalability and maintainability, it makes use of a modular backend, picture preprocessing methods, and the Plant Village dataset. Performance is evaluated using accuracy, precision, recall, and user feedback.

Key Words:  Crop Disease Detection, Convolutional Neural Network, Deep Learning, Streamlit, Smart Farming, Plant Village, AI in Agriculture