Plant disease dataset github Plant Disease Classification Plant Disease Classification Building a CNN based classifier to classify regional plant diseases given the plant leaf image. , With random Jul 21, 2025 路 An automated system designed to help identify plant diseases by the plant’s appearance and visual symptoms using Image Processing and Convolutional Neural Networks. class 馃尶 Plant Disease Detection Web App 馃攳 | Detects crop diseases from leaf images using a CNN trained on the PlantVillage dataset. The dataset is divided into three parts as follows: train - 70,295 images divided Downsampled version of PalntVillage dataset. By learning the important features from the input images, CNNs can make accurate predictions about the presence of diseases A deep learning-based computer vision project that performs leaf segmentation and disease classification from plant images to support early diagnosis and crop health monitoring. It consists of 2,516 images with 8,732 labeled objects classified into 29 categories: This repository contains a machine learning project for classifying plant diseases using images of plant leaves. The CNN model consists of multiple convolutional layers, pooling layers, and fully connected layers. 馃 Built with Keras, OpenCV & Flask. AlexNet uses Rectified Linear Plant-Disease-Identification-using-CNN Plant Disease Identification Using Convulutional neural Network Here is how I built a Plant Disease Detection model using a Convolutional Neural Network . Developed a deep learning model for image-based detection of plant diseases. This repository is about an end to end implementation of deep learning cotton plant disease classification web application using flask. . But this isn’t what makes AlexNet special; these are some of the features used that are new approaches to convolutional neural networks: ReLU Nonlinearity. Oct 1, 2024 路 Download the plant-disease-classification. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects. PlantWild is currently the largest dataset containing wild plant disease images. The dataset for this project can be downloaded from: New Plant Diseases Dataset (Kaggle) This dataset consists of 87,900 images of leaves spanning 38 classes. The original dataset can be found on this github repo. We introduce descriptive prompts in our dataset to provide rich information in textual modality. May 19, 2025 路 This dataset is designed for building deep learning models that detect and classify plant diseases across various crop types. Tomato Plant Village dataset is a collection of images depicting various diseases affecting tomato plants. Each image in the dataset typically represents a tomato plant leaf exhibiting symptoms of different diseases, such as bacterial spot, early blight, late blight, leaf mold, septoria leaf spot, and others. The different versions of the dataset are present in the raw directory : color : Original RGB images grayscale : grayscaled version of the raw images segmented : RGB images with just the leaf segmented and color corrected. GitHub is where people build software. The database used was Corn Maize Leaf Disease, which contains 4188 images of corn plants with leaf diseases. The dataset is divided into three parts as follows: train - 70,295 images divided This project aims to build a Deep Learning-based Plant Disease Detection System that helps farmers and agricultural experts identify plant diseases quickly and accurately. The dataset is focused on plant disease arXiv. The Convolutional Neural Code build in Pytorch Framework. They produce oxygen, help regulate the climate, provide habitats for wildlife, protect water resources, prevent soil erosion, and provide us resources and medicine. A large dataset of cotton plant images infected with various diseases was collected and used to train and evaluate the CNN model. For every dataset, a file is linked to give more description. In which we are using convolutional Neural Network for classifying Leaf images into 39 Different Categories. This plant leaf disease detection project was developed using Python, Flask, TensorFlow, and NumPy. It includes the full pipeline for data preparation, model training, evaluation, Plant Disease Detection This project uses MobileNet and transfer learning to detect plant diseases across 38 different classes. 馃尡馃尦 Flora datasets is a curated list of datasets on plants, trees and forests to help the flora/forest-interested ML/AI community develop models. Using a dataset of images that includes both healthy plants and those affected by various diseases, the aim is to build an efficient image classification model A deep learning model to classify various diseases and deficiencies occurring in Banana plant using Convolutional Neural Networks(CNN). Designed for gardeners, farmers, and plant lovers, built. The results of the study showed that the model This project presents an intelligent plant disease detection system utilizing the fundamental architecture of Convolutional Neural Networks (CNN), incorporating layers such as Conv2D, MaxPooling, and Flattening. Dataset The dataset used in this project is the "New Plant Diseases Dataset (Augmented)". The PlantVillage Dataset We use a publicly available and quite famous, the PlantVillage Dataset. The model was trained over 3000+ datasets of plant leaf images, and it can now accurately identify 10+ different types of plant diseases. That is plant disease detection. Plant Disease Detection Using Image Processing and Machine Learning This MATLAB project implements a plant disease detection system using image processing and machine learning techniques. Our dataset enables researchers to evaluate their models and provides a valid foundation for the development and benchmarking of plant disease segmentation algorithms. 3 - Healthy (1162 images). Models are trained on the preprocessed dataset which can be downloaded here. The system uses image processing techniques to analyze images of plants and determine whether they are infected with a disease. md Plant-Disease-Identification-using-CNN Image dataset containing different healthy and unhealthy crop leaves. It contains images of various plant diseases across different categories. Contribute to attaullah/downsampled-plant-disease-dataset development by creating an account on GitHub. The developed model is able to reco The Plant Pathology Challenge we have attended consists in training a model using images of the training dataset to accurately classify a given image from testing dataset into different diseased category or a healthy leaf; accurately distinguish between many diseases, sometimes more than one on a single leaf; deal with rare classes and novel symptoms; address depth perception—angle, light Dec 24, 2024 路 An AI-powered system for plant disease detection using CNNs and fertilizer recommendations via a Flask GUI. Preprocessed images, built a CNN model, trained it, evaluated its performance, and implemented it with Flask for user uploads. Contribute to AkhilaMadduri/TomatoDataset development by creating an account on GitHub. 馃尡 Plant Disease Detection using Deep Learning This project is an AI-powered system for detecting plant diseases from leaf images. 1. The trained model achieves an accuracy of approximately 90% on the validation set. Jan 13, 2025 路 A machine learning-based Plant Disease Detection System using CNN to classify 38 plant diseases from leaf images. plant-disease image-classification-dataset fine-grained-image-classification crop-diseases apple-leaf-disease Updated on Apr 16, 2024 The PlantVillage Dataset We use a publicly available and quite famous, the PlantVillage Dataset. 1 - Gray Leaf Spot (574 images). In the last two blog posts, we have already seen how deep learning and computer vision can help in recognizing different plant diseases effectively. Unsupervised Learning for Plant Disease Detection Data Our final dataset includes 5 sources: Plant Village Tomato Dataset PlantDoc Dataset Bing Image Search GAN Images Data Augmentation: The image augmentation algorithm was employed to increase the number of certain images due to the unbalanced dataset and to increase the robustness of the model. Here's a summary of key findings and trends from recent studies: Dataset Diversity: Researchers emphasize the importance of diverse datasets comprising images of both healthy and diseased The Plant Pathology Challenge we have attended consists in training a model using images of the training dataset to accurately classify a given image from testing dataset into different diseased category or a healthy leaf; accurately distinguish between many diseases, sometimes more than one on a single leaf; deal with rare classes and novel The dataset used for this project is the "PlantVillage" dataset, focusing on potato plant images. This repository contains the implementation of a plant disease classification model using PyTorch. The model was trained on a publicly available plant disease dataset and can recognize diseases in a variety of crops such as tomatoes, potatoes, apples, and others. Des The plant_disease_model. org e-Print archive The dataset is sourced from the PlantVillage dataset on Kaggle, and only the tomato data is utilized. The dataset we used is called "New Plant Disease" and it consists of 87,000 RGB images of healthy and diseased crop leaves. Accurate classification of diseases based on image analysis. The users can upload images of plant leaves and the model will predict the disease. Given that we were training a model to simultaneously predict plant species and disease. Training and evaluating state-of-the-art deep architectures for plant disease classification task using pyTorch. Using Convolution Neural Network Layers to detect plant diseases - Plant-DiseaseDetection/Libraries and Dataset at main · gg0298/Plant-DiseaseDetection Original datasets (no web-scraped or reused images) The datasets cover various agricultural computer vision tasks: Weed detection and classification (29 datasets) Disease and pest detection (9 datasets) Seedling and crop detection (6 datasets) Plant growth stage detection Phenotyping Various detection and counting tasks Figure 1: Dataset samples We extracted our dataset from the well known Plantvillage dataset, which contains nearly 5,000 image of 14 crop species and 26 diseases. The dataset used for training the models is the Tomato-Village dataset, which can be found on Kaggle. This project explores the potential of deep learning in early detection and diagnosis of plant diseases—an essential step for preventing widespread crop damage and ensuring food security. e. By analyzing leaf images, the model efficiently extracts spatial features and aids in early diagnosis and minimizing crop loss. An AI-powered tool using ResNet50 The dataset used for training and testing the models is obtained from the Plant-Village Dataset, which contains images of healthy apple leaves and leaves affected by diseases such as Apple Scab, Black Rot, and Cedar Apple Rust The system aims to achieve high accuracy in disease classification and provide a practical tool for farmers and Dataset Description: The "Plant Disease Classification Merged Dataset" contains images of plant leaves affected by a wide range of diseases, which are essential for training the plant disease classification model used in this application. This project aims to develop a robust plant disease detection system using advanced machine learning techniques, primarily leveraging YOLO for object detection. 12 crop species also have images of healthy keywords = {Artificial intelligence, Deep convolutional neural networks, Deep learning, Dropout, Image augmentation, Leaf diseases identification, Machine learning, Mini batch, Training epoch, Transfer learning}, Feb 6, 2025 路 This project implements a Convolutional Neural Network (CNN)-based deep learning model for detecting plant diseases from leaf images. Introduction We curate an in-the-wild multimodal plant disease recognition dataset PlantWild with the largest number of disease classes. The dataset is organized into three main splits: train/ val/ test/ Each of these directories contains subfolders for different crops, and each crop folder further contains labeled Motivated by this observation, we propose an in-the-wild multimodal plant disease recognition dataset, PlantWild, which contains the largest number of disease classes but also text-based descriptions for each disease. The model is based on a fine-tuned ResNet-18 architecture for image classification tasks of the plantvillage dataset. Ensure the dimension of the image passed to the network is 100x100x3 You can modify all hyperparameters in main. ipynb notebook from the GitHub repository and run it locally. It takes less time and is easy to use. The project includes a Convolutional Neural Network (CNN) model trained on the PlantVillage dataset and a Streamlit web application for user interaction. User-friendly interface for uploading leaf images and viewing the diagnosis results. Although researches has been done to detect weather a plant is healthy or diseased using Deep Learning and with the help of Neural Network, new techniquies are still being discovered. The images span 14 crop species: Apple, Blueberry, Cherry, Corn, Grape, Orange, Peach, Bell Pepper, Potato, Raspberry, Soybean, Squash, Strawberry, Tomato. The dataset is organized into training and validation sets. Includes real-time predictions, supplementary files, demo video, and dataset links for seamless deployment. The architecture consists of eight layers: five convolutional layers and three fully-connected layers. Machine learning-based plant disease detection, particularly focusing on rice plants, reveals a growing body of research aimed at addressing agricultural challenges and enhancing crop management practices. The description of the dataset is: 0 - Common Rust (1306 images). This project aims to develop a method for detecting plant diseases using CNNs by analyzing leaf images. In this study, a model was developed for the classification of plant leaf diseases from the leaf images using EfficientNet B3 deep learning architecture. The website provides an intuitive interface for users to upload images of plant leaves and receive real-time disease predictions, along with information on disease types and potential treatments. Contribute to kruthi-sb/leaf_disease_detection development by creating an account on GitHub. Automated detection of plant leaf diseases. The workflow includes data preprocessing, feature extraction, non-negative matrix factorization, fuzzy clustering, and model training. js and HTML5 Canvas. The dataset comes from Kaggle's New Plant Diseases Dataset and is organized into train, validation, and test sets. This dataset has been made using the popular PlantVillage and PlantDoc datasets, and is available in Kaggle. class (1): Early Blight. This dataset is designed for detecting plant diseases from leaf images. Plant Disease is necessary for every farmer so we are created Plant disease detection using Deep learning. This repository contains a Convolutional Neural Network (CNN) model built with TensorFlow and Keras to classify plant diseases using the PlantVillage Dataset. Plant Disease Detection This dataset is recreated using offline augmentation from the original dataset. Install the necessary dependencies and ensure the dataset is properly downloaded and loaded. Utilizing top CNN models, we empower farmers with early diagnosis tools. The dataset was published by crowdAI during the "PlantVillage Disease Classification Challenge". - razamehar/plant-disease-detection-using-YOLO Feb 3, 2025 路 The dataset chosen for model training is the "Plants Diseases Detection and Classification" dataset from Roboflow, licensed under Creative Commons. Different data accurately classify a given image from testing dataset into different diseased category or a healthy leaf; accurately distinguish between many diseases, sometimes more than one on a single leaf; deal with rare classes and novel symptoms; address depth perception—angle, light, shade, physiological age of the leaf; Incorporate expert knowledge in identification, annotation, quantification, and Introduction This project explores the use of machine learning, specifically deep learning, for detecting and classifying plant diseases. We use the PlantVillage dataset [1] by Hughes et al. Model Architecture: The model is based on the Xception architecture, a powerful deep learning model. Against this background, we present PlantDoc: a dataset for visual plant disease detection. Android application for detecting and classifying banana plant diseases using on-device TensorFlow Lite (TFLite) and YOLO model integration. It uses a Convolutional Neural Network (CNN) trained on the PlantVillage Dataset to classify various plant diseases with high accuracy. Feb 26, 2024 路 Plant Disease Detection using PlantDoc and YOLOv5. The software accurately detects plant diseases, aiding farmers and agricultural professionals. Through the use of image annotation, data preprocessing, and model training, we were able to achieve very good accuracy in detecting and identifying various plant leaf diseases. This project leverages AI and deep learning to identify plant diseases from images, helping farmers take early action and ensure sustainable agriculture. By leveraging computer vision and deep learning models, the system analyzes images of plant leaves to classify and detect 馃尡 Project Overview Plant diseases significantly impact global food production, leading to economic losses for farmers. Our solution utilizes a Python Convolutional Neural Network model for accurate plant disease diagnosis By training the model on a diverse dataset of plant images, it learns to recognize disease patterns Users can upload plant images, which are preprocessed and passed through the trained CNN model Here we have used the plant village dataset. Additionally, I want to add more features like plant nutritional deficiency recognition which can help provide insight into pesticide free and biological treatment of plants. and total size is 152 MB. In containes images of 17 fungal diseases, 4 bacterial diseases, 2 mold (oomycete) diseases, 2 viral disease, and 1 disease caused by a mite. Additionally, the repository may include visualizations PlantDiseaseNet PlantDiseaseNet: Convolutional Neural Network Ensemble for Plant Disease and Pest Detection The Turkey-PlantDataset called as Turkey Plant Diseases & Pests Dataset was obtained from academics working in the field of plant protection at the Agricultural Faculty of Bingol and Inonu Universities in Turkey. Utilizing the integrated datasets from Plant Village and Plant Doc, the project features advanced object detection and instance segmentation models, including YOLOv8m, YOLOv8l, Faster-RCNN, RetinaNet This project is an approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. All images are 256*256 in resolution. The dataset consists of images of different varieties of bana This repository contains two models for the detection of plant diseases using deep learning techniques, specifically leveraging the VGG16 architecture. The CNNs are proficient in handling large datasets and can dynamically learn new features from them in a supervised manner. Dataset of tomato plant diseases. Plant Disease Detection is one of the mind boggling issue that exits when we talk about using Technology in Agriculture. Transfer learning was applied to fine-tune a pre-trained CNN model for the specific classification task. This code implements a Convolutional Neural Network (CNN) to classify plant diseases using the PlantVillage dataset. This repository includes the official implementation of the paper: Data-centric Annotation Analysis for Plant Disease Detection: Strategy, Consistency, and Performance. Contribute to lzoran/plant-disease-dataset development by creating an account on GitHub. The model is trained on images of plant leaves to identify various diseases. The dataset is downloaded from Kaggle. Authors and affiliations: Jiuqing Dong 1, Jaehwan Lee1, 2, Alvaro Fuentes 1,2, Sook Yoon 3,, Mun Haeng Lee 4, Dong Sun Park 1,2, 1 Department of Electronic Engineering, Jeonbuk National University, Jeonju, South Korea 2 Core Transforming agriculture with AI: Explore our GitHub for advanced plant disease detection. In this post, we will march on a much more challenging problem. consists of about 87,000 healthy and unhealthy leaf images divided into 38 categories by species and disease. It leverages a dataset of plant leaf images to classify various plant diseases using Support Vector Machines (SVM) and K-Nearest Neighbors (KNN) classifiers. Capable of handling various types of plant diseases. The lack of availability of sufficiently large-scale non-lab data set remains a major challenge for enabling vision based plant disease detection. To the best of our knowledge, PlantSeg is the largest plant disease segmentation dataset containing in-the-wild images. Collect public plant disease recognition datasets. Features include a Streamlit web app for image upload, preview, and prediction. This dataset consists of about 76K rgb images of healthy and diseased crop leaves which is categorized into 33 different classes. The images are categorized into 38 different classes and 14 different plants, and the dataset is divided into an 80:20 ratio of training and validation data. We will use the PlantDoc dataset for plant disease detection Contribute to honglin1226/Tobacco-Plant-Disease-Dataset development by creating an account on GitHub. The system can detect 38 different plant conditions across multiple crop types including tomatoes, apples, corn, grapes, potatoes, and Apr 20, 2025 路 The project uses the "New Plant Diseases Dataset" from Kaggle, which contains augmented images of healthy and diseased plant leaves. - GitHub - Yashithaw/Tomato-Disease-Classification: This repository contains a deep learning model built using TensorFlow for classifying tomato diseases. py README. NOTE: We are working over 3 data sets: (Train, Valid, and Test) data set containing 38 classes of different leaves This Plant Disease Recognition System leverages state-of-the-art deep learning techniques to automatically identify and classify plant diseases from leaf images. Plants and forests are essential for our planet and for humanity. The system will analyze plant images captured through smartphones or dedicated cameras to detect visible symptoms of diseases. The PlantVillage dataset consists of 61,486 healthy and unhealthy leaf images divided into 39 categories by species and disease. This project aims to only collect the public dataset to recognize plant disease because the community can not verify the performance on the private ones, although they have information and contributions. 2 - Blight (1146 images). However, they This dataset only contained 2,598 images with 13 classes of plant species and 17 classes of diseases. The dataset includes color, segmented, and grayscale images of healthy and unhealthy plants. It includes images of healthy potato leaves and leaves affected by Early Blight and Late Blight. Detect plant leaf diseases instantly with the AI Leaf Disease Detector, a smart web app powered by TensorFlow. Using computer vision and deep learning techniques, the model classifies different plant diseases and can assist farmers in early disease diagnosis. Current Deep Learning and CNN research have resulted in the availability of multiple CNN designs, making automated plant disease identification viable rather than traditional visual inspection-based disease detection. py to trian from the scratch (i. - MAmudha/Tomato-leaf-disease-dataset Overview: The Chili Leaf Disease Detection project aims to develop a system for automatically detecting diseases in chili plant leaves using machine learning and computer vision techniques. The dataset has been sourced from Kaggle, and this repository includes a Jupyter Notebook to streamline the workflow for training, evaluating, and visualizing plant disease detection models. The dataset utilized in this plant disease detection saga was sourced from Kaggle's PlantVillage dataset, providing a rich tapestry of leaf images for training and validation. Here is my approach for Detecting weather a plant leaf is healthy or unhealthy by utilising This repository contains resources and datasets for detecting plant diseases using machine learning and deep learning techniques. Access notebooks, datasets, and a Jun 21, 2022 路 GitHub is where people build software. By providing early About our work focused on the detection and identification of plant leaf diseases using the YOLO v4 architecture on the Plant Village dataset. The plant images span the following 14 Files master Kaggle code with dataset Plant Disease Detection Using Convolutional Neural Network. The app provides real-time detection, health analysis, and disease prevention guidance through an intuitive mobile interface. The PlantVillage dataset contains 54,304 images. We choose to work with 9,000 images on Tomato leaves, our dataset contains samples for 5 types of Tomato diseases in addtion to healthy leaves, 6 classes in total as follow: class (0): Bacterial Spot. For Training we are using Plant village dataset. The plant images span the following 14 About Developed a plant disease detection software using Python and the PlantVillage Dataset from Kaggle. Plant-Disease-Monitoring-Expert-with-Supplements This project aims to create an automated plant disease monitoring system powered by image recognition and machine learning. All relevant code and data sets are included in the repository. The datasets having 60930 images was used to train the models using transfer learning approach. Contribute to honglin1226/Tobacco-Plant-Disease-Dataset development by creating an account on GitHub. 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 It utilizes CNN models trained on a diverse dataset of plant images to accurately classify and predict the presence of diseases in various crop species. Upload or capture a leaf photo to identify plant diseases, pests, or nutrient deficiencies — and export professional PDF/CSV reports directly from your browser. When using Deep Learning Methods, the dataset serves one of the most crucial roles in disease prediction. docx PlantDiseaseDetection. In the future, I would like to add more plant types and diseases to my dataset. pkl is a Convolutional Neural Network (CNN) that has been trained to detect diseases in various plant crops based on leaf images. The dataset contains 3 folders with 1951 train images, 106 test images and 253 validation images Jan 16, 2023 路 Recognizing plant disease can lead to faster treatment which can result in better yields. It supports research in precision agriculture and automated plant health diagnostics. Each class denotes a combination of the plant the leaf is from and the disease (or lack thereof) present in the leaf. Contribute to samsil2/Plant_Disease_Detection_Dataset_kaggle development by creating an account on GitHub. A deep learning CNN model to predict diseases in plants using the famous AlexNet architecture AlexNet. Dataset Link is in My Blog Section. Plant diseases can have a significant economic impact by reducing crop yield and quality. This project aims to develop a system for detecting plant diseases using Python programming language. The dataset consists of about 54,305 images of plant leaves collected under controlled environmental conditions. Load the trained weights in to the model to classify the input image. ujhahu srw judy eciz jfopmhm rfbuoh ujdbc qyxxu pbmxvj yuqgdb fcsg qtfkvx ktmvnp vodb drxkwz