Plant Disease Identification Using Convulutional Neural Network

AI powered plant disease detection and assistance platform currently available as an App and API.

Disease classification on different plants with using Machine Learning and Convolutional Neural Networks.

Brazilian Agricultural Research Corporation (EMBRAPA) fully annotated dataset for plant diseases. Plug and play installation over PiP.

API for the plant disease recognition artificial inteligence project which I collaborated on as a member of Team fort in the NaijaHacks hackaton.

☘️ A beautiful Plant App UI written in Java for android with examples.

Dataset used in "Efficient Identification of Apple Leaf Diseases in the Wild Using Convolutional Neural Networks".

Plant Disease Detection

A project to train and evaluate different dnn models for plant disease detection problem, tackle the problem of scarce real-life representative data, experiment with different generative networks and generate more plant leaf image data and implement segmentation pipeline to avoid miss-classification due to unwanted input

Image processing code for blob detection and feature extraction in MATLAB. Paper Reference: Detecting jute plant disease using image processing and machine learning. Find the full text here: http://ieeexplore.ieee.org/document/7873147/

Android app for plant disease identification using ML

Source code for the paper "Reliable Deep Learning Plant Leaf Disease Classification Based on Light-Chroma Separated Branches".

Source code for the paper "Color-aware two-branch DCNN for efficient plant disease classification".

A project to detect plant disease using leaf images and to calculate the percentage of leaf affected by the disease using OpenCV.

plant leaf diseases prediction using four different trained models named pytorch, TensorFlow, Keras and fastai

Computer Vision Live detection of plant diseases

Bangkit Capstone Project

This is a plant disease recognition machine learning model

A curated collection of 45 high-quality RGB image datasets for computer vision in agriculture. Features datasets for weed detection, disease identification, and crop monitoring, focusing on natural field scenes. Part of our GIL 2025 survey paper.