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 👇

Flask Application for Detecting Disease in a Potato Leaf

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

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

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

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.

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

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

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

Dataset for Potato Disease Classification

Potato leaf disease detection using a CNN

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.

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.

Data and script for a potato metagenomic analysis