Self-training variants using PyTorch
-
Updated
Jun 22, 2024 - Python
Self-training variants using PyTorch
Downsampled version of PalntVillage dataset
Using YOLOv8 and Detectron2 models, this project automates the detection of plant diseases from image data to facilitate early diagnosis and treatment.
Plant disease detection on PlantVillage dataset using EfficientNetV2-B0
Source code for the paper "Color-aware two-branch DCNN for efficient plant disease classification".
Source code for the paper "Reliable Deep Learning Plant Leaf Disease Classification Based on Light-Chroma Separated Branches".
Leaffliction - Leaf Disease Recognition using Computer Vision
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.
This repository contains code for the PhD thesis: "A Study of Self-training Variants for Semi-supervised Image Classification" and publications.
Crop Disease Classification Training Model. This is a one part of the entire project. The complete project is a plant and pest detection mobile app using machine Learning algorithms and computer vision
End to End Image Classification using CNN on PlantVillage Dataset.
Методы ML в задачах детектирования и классификации болезней листьев томатов
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.
Add a description, image, and links to the plantvillage-dataset topic page so that developers can more easily learn about it.
To associate your repository with the plantvillage-dataset topic, visit your repo's landing page and select "manage topics."