Developed a deep learning model using TensorFlow and Convolutional Neural Networks to classify disease images of potato plants, including early blight, late blight, and overall plant health in agriculture. Model achieved an impressive accuracy of 97.8%, empowering farmers with precise treatment applications to enhance crop yield and quality.

This research presents a hybrid deep learning framework combining MobileNet V2 with LSTM, GRU, and Bidirectional LSTM for classifying various potato diseases. The study explores the performance of different architectures to determine the optimal configuration for accurate disease categorization.

Detect and classify potato diseases using a Convolutional Neural Network (CNN) with 100% accuracy an effective solution for agricultural health monitoring.

Potato Disease Detection is an AI-powered system that classifies potato leaf diseases using a deep learning model (CNN). It detects Healthy, Early Blight, and Late Blight conditions from images, providing a fast, automated, and accurate alternative to manual inspection. The system is built with Streamlit for a user-friendly interface and leverages

Plant disease detection mobile app

Potato Disease Classification with CNN

This repo contains deep learning projects for beginners.

Plant disease detection web app

Potato Leaf Disease using Machine Learning

PlantDiseaseDetector is a Jupyter Notebook-based tool that uses a deep learning model to identify potato leaf diseases (Early Blight, Late Blight, Healthy) from uploaded images. Powered by Streamlit, it offers an intuitive web interface for farmers and researchers to diagnose plant health quickly.

Potato leaf disease classification using deep learning computer vision and the PlantVillage dataset. Supports image upload and camera detection via Streamlit.

This project is a Potato Disease Detection Model that implement deep learning techniques to identify early and late blight in potato plants.

Deep Learning Project