Wheat Plant Diseases

## Overview

Wheat Plant Diseases is a dataset for object detection tasks - it contains Diseases annotations for 937 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Here are a few use cases for this project:

1. Agriculture Management: The model can be employed by farms or agricultural services to track plant health, identify disease outbreaks early, and take preventative action swiftly thereby increasing crop yield and quality.

2. Home Gardening: DIY gardeners can use this model to diagnose and treat diseases in their home gardens, maintaining the health of their plants and helping them flourish.

3. Plant-based Research: Researchers studying plant diseases can utilize this model to automatically classify and monitor disease progression in their subjects, saving time and increasing the accuracy of their studies.

4. Environmental Impact Studies: Environmental agencies can use this model to evaluate the effects of pollutants or climate change on plant health, noting any increases in disease prevalence and effect on local vegetation.

5. Educational Purposes: This "plant diseases" model could be used in the educational sector, helping students studying botany or related fields to more easily understand and identify various plant diseases.

Explore 87,000+ RGB images of crop health and diseases across 38 classes. Ideal for machine learning, precision agriculture.

Plant leave disease dataset https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset/data.

khushwant04/Plant-Disease-Dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

The PlantVillage dataset consists of 54303 healthy and unhealthy leaf images divided into 38 categories by species and disease.

NOTE: The original dataset is not available from the original source (plantvillage.org), therefore we get the unaugmented dataset from a paper that used that dataset and republished it. Moreover, we dropped images with Background_without_leaves label, because these were not present in the original dataset.

Original paper URL: https://arxiv.org/abs/1511.08060 Dataset URL: https://data.mendeley.com/datasets/tywbtsjrjv/1

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('plant_village', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/plant_village-1.0.2.png" alt="Visualization" width="500px">

Sugarcane Plant Diseases

## Overview

Sugarcane Plant Diseases is a dataset for object detection tasks - it contains Disease In Plants annotations for 968 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Description: 👉 Download the dataset here This dataset comprises high-resolution images of potato plants, capturing various conditions including early blight, late blight, and healthy leaves. Curated specifically for research and development, it serves as a valuable resource for creating and testing image recognition models aimed at accurate disease detection and classification. The dataset is designed to facilitate advancements in agricultural diagnostics, enabling more efficient and precise… See the full description on the dataset page: https://huggingface.co/datasets/gtsaidata/Potato-Plant-Diseases-Data.

The application of Artificial Intelligence (AI) has been evident in the agricultural sector recently. The main goal of AI in agriculture is to improve crop yield, control crop pests/diseases, and reduce cost. The agricultural sector in developing countries faces severe in the form of disease and pest infestation, the knowledge gap between farmers and technology, and a lack of storage facilities, among others. To help address some of these challenges, this work presents crop pests/disease datasets sourced from local farms in Ghana. The dataset is presented in two folds; the raw images which consists of 24,881 images ( 6,549-Cashew, 7,508-Cassava, 5,389-Maize, and 5,435-Tomato) and augmented images which is further split into train and test set consists of 102,976 images (25,811-Cashew, 26,330-Cassava, 23,657-Maize, and 27,178-Tomato), categorized into 22 classes. All images are de-identified, validated by expert plant virologists, and freely available for use by the research community.

Here are a few use cases for this project:

1. Agricultural Health Monitoring: Farmers and agronomists can use this model to monitor crop health in their fields. The model can help to spot diseases early, allowing for timely treatment and potentially saving large portions of crop yields.

2. Smart Gardening Applications: Home and community gardeners can use this model via a mobile app to monitor the health of their plants. The app can alert the gardener when it detects signs of disease, enabling early intervention.

3. Plant Nursery Management: Plant nurseries can use this model to ensure that they're selling healthy plants and to quickly isolate and treat plants that show signs of disease. This can help reduce the spread of plant diseases within the nursery.

4. Agricultural Research: Researchers can use this model to support studies on plant diseases. Early and accurate detection can aid experiments and the development of new disease control measures.

5. Education and Training: The model can be used as an educational tool for students and professionals learning about plant pathology or horticulture. By visualizing the symptoms of different diseases, users can gain a better understanding of plant health issues.

Description: 👉 Download the dataset here This dataset offers an extensive collection of images and corresponding labels representing a wide array of plant diseases. Carefully curated from publicly available sources, it serves as a valuable resource for developing and evaluating machine learning models, particularly in the realms of image classification and plant disease detection. Dataset Composition: • Images: The dataset comprises high-quality images organized by plant species and disease… See the full description on the dataset page: https://huggingface.co/datasets/gtsaidata/Plant-Disease-Image-Dataset.

Plant Diseases Detection System

## Overview

Plant Diseases Detection System is a dataset for object detection tasks - it contains Aaaaaa annotations for 1,532 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The web-sourced dataset consists of plant leaf images collected from online platforms, primarily through sources like Google Images, to capture a wide range of real-world scenarios and environmental conditions. Unlike controlled laboratory datasets, these images feature diverse backgrounds, lighting variations, and different stages of plant diseases, representing how diseases appear in natural agricultural settings. The dataset includes multiple plant species and disease types, augmenting existing datasets by adding greater variability. This diversity aims to improve model robustness and generalization, enabling more accurate disease detection across varying agricultural environments.

Plant Disease

## Overview

Plant Disease is a dataset for object detection tasks - it contains Plant Diseases annotations for 3,400 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

Here are a few use cases for this project:

1. Precision Agriculture: Farmers can use the Leaf_Diseases_Detection model to identify diseases affecting their grapevines or potato plants, enabling them to treat specific areas of their crops with targeted pesticides and fungicides, saving time, money, and resources.

2. Plant Health Monitoring: Researchers, agricultural specialists, or home gardeners can monitor the health of their plants by regularly scanning the leaves with the model to detect early signs of diseases and intervene before the infections spread to other plants.

3. Smart Greenhouses: Integrating the Leaf_Diseases_Detection model into automated greenhouse systems can optimize the growing environment and minimize the spread of diseases by continuously monitoring plant health status and taking necessary actions, such as adjusting humidity, temperature, or watering schedules.

4. Plant Disease Education: Institutions offering horticulture and plant pathology courses can leverage the Leaf_Diseases_Detection model to aid students in learning how to diagnose common plant diseases based on visual symptoms, improving hands-on learning experiences.

5. Agri-tech Innovations: Companies in the agricultural technology sector can integrate the model into their solutions, such as drones or farm management software, to offer value-added services like automatic plant disease detection and alert features to their clients.

Manually collected image dataset of sugarcane leaf disease. It has mainly five categories in it. Healthy, Mosaic, Redrot, Rust and Yellow disease. The dataset has been captured with smart phones of various configuration to maintain the diversity. It contains total 2569 images including all categories. This database has been collected in Maharashtra, India. The database is balanced and contains good variety. The image sizes are not constant as it originates form various capturing devices. All images are in RGB format.

The Cornell Plant Pathology Herbarium (CUP) is a large research collection of preserved fungi and other organisms that cause plant diseases. CUP is the fourth largest mycological herbarium in North America. We hold about 400,000 fungus and plant disease specimens, including over 8000 type specimens. Our main geographic strength is northeastern North America, but we also hold important collections from the tropics including the Caribbean, Mexico, South America, Southeast Asia and Macaronesia. Our collections include many rare fungal exsiccati as well as many authors' herbaria (Atkinson, Durand, Fairman, Gremmen, Honey, Korf, Stewart, Welch). The CUP Photograph Collection supplements our specimens and comprises roughly 60,000 historical, scientific photographs of mushrooms, agricultural practices, plant diseases, and portraits of scientists.

Explore the Sugarcane Leaves Disease Dataset with 19,926 images of healthy and diseased sugarcane leaves, including Bacterial Blight.

Description

Dataset from the Plant Pathology 2021 (FGVC8) Challenge. ' For Plant Pathology 2021-FGVC8, we have significantly increased the number of foliar disease images and added additional disease categories. This year’s dataset contains approximately 23,000 high-quality RGB images of apple foliar diseases, including a large expert-annotated disease dataset. This dataset reflects real field scenarios by representing non-homogeneous backgrounds of leaf images taken at different… See the full description on the dataset page: https://huggingface.co/datasets/timm/plant-pathology-2021.