Potato Disease Detection Project using Machine learning with Code, Documents and video Explanation

Established web app employs Python's Flask Framework for frontend structure, linking with a backend ML model to classify disease types in potato plants based on leaf images and the application of Convolutional Neural Networks.

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.

A deep learning web application built with TensorFlow, FastAPI, and LangChain that classifies potato leaf diseases and provides an AI-powered agricultural chatbot for farmers and researchers.

This project aims to develop an automated potato disease classification system using deep learning. By leveraging a Convolutional Neural Network (CNN), the model classifies high-resolution images of potato leaves into different categories, including healthy and diseased plants (early blight, late blight). The system is deployed using Flask and proc

This project is focusing on agricultural revolution based on modern agrotechnology.

Potato Disease Classification done by using deep-learning and for the sake of knowing various diseases caused to Potato plant and for quick remedial action. Link of the website 👇

R scripts developed for the phd thesis "Applications of machine learning to understand and predict potato blackleg".

PyTorch deep learning model for potato disease classification. Implements custom CNN and transfer learning (ResNet50, EfficientNet-B0) to identify Early Blight, Late Blight, and healthy potato leaves with 95%+ accuracy.

Dataset for Potato Disease Classification

Uses Tensorflow to train a model to detect Rice and Potato plant Diseases

A Random Forest model for predicting incidence of potato blackleg at the landscape-scale.

Potato leaf disease detection using a CNN

I developed this portfolio using CNN to train a model to detect Rice and Potato plant Diseases. To see the website visit

Multi-headed CNN for simultaneous potato/tomato classification (99.9% accuracy) and quality assessment (98.5% accuracy). Features Grad-CAM explainability, Streamlit interface, and 30% parameter reduction. Built for precision agriculture with real-time crop monitoring.

Flask Application for Detecting Disease in a Potato Leaf

Potato Disease Classification using TensorFlow is a project designed to identify three types of potato plant health: Early Blight, Healthy, and Late Blight. This machine learning model employs convolutional neural networks (CNNs) to analyze images, aiding farmers in early disease detection and crop protection.

Deep learning–based image classification system for detecting potato leaf diseases using CNN and TensorFlow.