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".

Using YOLOv8 and Detectron2 models, this project automates the detection of plant diseases from image data to facilitate early diagnosis and treatment.

Методы ML в задачах детектирования и классификации болезней листьев томатов

Plant disease detection on PlantVillage dataset using EfficientNetV2-B0

This repository contains code for the PhD thesis: "A Study of Self-training Variants for Semi-supervised Image Classification" and publications.

This model learns all the features of 48 different kinds of plants (Healthy and diseased) from the PlantVillage Dataset, and identifies the type of disease and the plant when you input any image in the model.

A state-of-the-art hybrid deep learning ensemble that combines the strengths of Convolutional Neural Networks (CNNs) and Tansformers for intelligent plant disease detection and real world agricultural applications.

Self-training variants using PyTorch

This repository contains an implementation of a CNN which predicts the disease that a tomato plant has based on a picture of one of its leaves. Images were obtained from the PlantVillage dataset.

Downsampled version of PalntVillage dataset

End to End Image Classification using CNN on PlantVillage Dataset.

Leaffliction - Leaf Disease Recognition using Computer Vision

A plant disease detector utilizing deep learning and CNNs to detect which disease a plant has.

Deep Learning Project