Plants Diseases Detection And Classification

## Overview

Plants Diseases Detection And Classification is a dataset for object detection tasks - it contains Plants annotations for 2,516 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).

This dataset contains symptoms and disease information. It contains total of 1325 symptoms covered with 391 disease.This dataset is refernced from website MedLinePlus. This dataset have training and testing dataset and can be used to train disease prediction algorithm . It is created on own for project disease prediction and do not involves any funding or promotional terms.

Durian Diseases

## Overview

Durian Diseases is a dataset for object detection tasks - it contains Durian Diseases annotations for 420 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).

These data contain case counts and rates for selected communicable diseases—listed in the data dictionary—that met the surveillance case definition for that disease and was reported for California residents, by disease, county, year, and sex. The data represent cases with an estimated illness onset date from 2001 through the last year indicated from California Confidential Morbidity Reports and/or Laboratory Reports. Data captured represent reportable case counts as of the date indicated in the “Temporal Coverage” section below, so the data presented may differ from previous publications due to delays inherent to case reporting, laboratory reporting, and epidemiologic investigation.

The authors of Cocoa Diseases (YOLOv4): Monilia & Phytophthora (Diseases in Cocoa Pods) claim that the total production of Theobroma cacao L cocoa in Colombia for 2018 exceeded 56 thousand tons, making it the second-highest in history despite a 6% reduction compared to 2017. The decrease in production was attributed to factors such as flowering flows, increased incidence of the Monilia disease, and floods caused by heavy rainfall early in the year. In response to the need for reliable inspection procedures to assess crop infections, the authors of the dataset developed a mobile application prototype that utilizes artificial intelligence techniques and image analysis to identify diseased cocoa pods.

Tea Leaf Plant Diseases

## Overview

Tea Leaf Plant Diseases is a dataset for classification tasks - it contains Tea Leaf Diseases annotations for 882 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 Diseases Database is a cross-referenced index of human disease, medications, symptoms, signs, abnormal investigation findings etc. This site provides a medical textbook-like index and search portal covering areas including: internal medical disorders, symptoms and signs, congenital and inherited disorders, infectious diseases and organisms, drugs and medications, common haematology and biochemistry investigation abnormalities.

fakewave07/plant-diseases-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

The term “rare diseases” is used to refer to diseases that occur in a relatively small number of people. They often involve specific problems because of the fact that they are rare. You can read more about this here.

The Central Registry of Rare Diseases is a database containing certain basic data of all Belgian patients with a rare disease. At present, data is collected only at the genetic centres, but this will later be extended to other centres in order to gain a complete overview. A registry is a valuable source of information for patients and patient organisations, care providers, researchers and authorities, and can contribute to improvements in several areas (including epidemiology, care policy, quality assurance, research, administration).

All the information on the data collection for this Central Registry of Rare Diseases can be found here. The data that are gathered contains the identification details of the genetic centre and the treating physician (NIHDI-numbers), some demographic data about the patient and details of the diagnosis:

• The demographic data that are gathered about the patient includes: encoded national register number, gender, date of birth, if applicable the date of death, and the zip code for the place of residence.
• In terms of the diagnosis, various data are requested:
  • the date of the first symptoms of the rare disease (defined as the date of the first consultation with a physician for this reason)
  • the date of the first consultation at the specialist healthcare institution
  • the date the current diagnosis was made
  • whether this diagnosis is provisional or definitive
  • the code for the disease within an existing disease classification system
  • the base on which the diagnosis was made (e.g. based on clinical symptoms, genetic test, biochemical test etc.)​.

For any audience, Orphanet ​is the reference portal for information about rare diseases and orphan drugs. The aim of Orphanet is to help to improve diagnosis, care and treatment of patients with a rare disease.

Explore our Facial Skin Diseases Dataset designed for object detection projects, featuring 188 labeled images of acne. Ideal for training.

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.

The dataset is composed of ultrasound images of the GB organ from inside the gastrointestinal tract. The dataset includes 9 classes according to anatomical landmarks. Each class represents a GB disease.

Rice Leaf Diseases with Boundary Box is a dataset for an object detection task. Possible applications of the dataset could be in the agricultural industry. The dataset consists of 470 images with 1956 labeled objects belonging to 3 different classes including BacterialBlight, BrownSpot, and RiceBlast

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 ‘ShrimpDiseaseImageBD’ is a dataset of images designed to help in the identification of shrimp diseases commonly found in aquaculture within Bangladesh using computer vision techniques. This dataset has the potential to play an important role in the aquaculture field, where detection of the shrimp disease with proper timing and precision are very important to monitor shrimp health and maintain economic security of farmers. The ShrimpDiseaseImageBD’ dataset contains images of different shrimp diseases collected in collaboration with local shrimp farms. The dataset will assist in developing and evaluating automated disease detection models and will prove to be of value to anyone working in the field of computer vision and aquaculture. Enriched with images of shrimp diseases, such a dataset holds significant potential to stimulate much research and practical application in automated health monitoring in aquaculture. The dataset is released to the public domain to encourage further research and development in this domain.

The dataset is systematically organized into two main directories within the "Root" folder: "Raw Images" and "Annotated Diseased Shrimp Images." The "Raw Images" directory contains 1149 images, divided into four subfolders: "Healthy" (403 images), "BG" (198 images), "WSSV" (328 images), and "BG_WSSV" (220 images). Each folder categorizes the images according to the health status or disease type, providing a structured arrangement for easy access. In the "Annotated Diseased Shrimp Images" directory, the images are organized into three subfolders named "BG," "WSSV," and "BG_WSSV", each containing two additional subfolders named "Images" and "Labels." The "Images" subfolder includes the actual shrimp images, while the "Labels" subfolder holds annotation data for the corresponding images. This structured organization facilitates efficient navigation and supports effective use in machine learning model training by providing both raw and annotated data in a clear and accessible format.

The dataset comprises total of 8432 images of Tamarind, categorized into Healthy and Unhealthy groups. Each category is further divided into three subcategories: Shelled (Tamarind pods with shells removed), Unshelled (Tamarind pods with intact shells), and Mixed (both shelled and unshelled Tamarind pods). The dataset includes images of both single and multiple Tamarind pods across these subcategories, providing a comprehensive resource for research in agriculture, food science, and machine learning applications aimed at Tamarind health assessment, disease detection, and quality evaluation.

Analysis of ‘Cardiovascular diseases dataset (clean)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/aiaiaidavid/cardio-data-dv13032020 on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Description of the data set

This data set is a cleaned up copy of cardio_train.csv which can be found at:

https://www.kaggle.com/sulianova/cardiovascular-disease-dataset

The original data set has been analyzed with Excel, correcting negative values, and removing outliers.

A number of features in the dataset are used to predict the presence or absence of a cardiovascular disease.

Below is a description of the features:

AGE: integer (years of age)
HEIGHT: integer (cm) 
WEIGHT: integer (kg)
GENDER: categorical (1: female, 2: male)
AP_HIGH: systolic blood pressure, integer
AP_LOW: diastolic blood pressure, integer 
CHOLESTEROL: categorical (1: normal, 2: above normal, 3: well above normal)
GLUCOSE: categorical (1: normal, 2: above normal, 3: well above normal)
SMOKE: categorical (0: no, 1: yes)
ALCOHOL: categorical (0: no, 1: yes)
PHYSICAL_ACTIVITY: categorical (0: no, 1: yes)

And the target variable:

CARDIO_DISEASE: categorical (0: no, 1: yes)

--- Original source retains full ownership of the source dataset ---

In the field of agriculture, the advancement of early treatment methods for plant leaf diseases can be greatly improved by utilizing precise and rapid automatic detection techniques. However, two common challenges arise in real-world scenarios: first, identifying different severity stages of diseases, and second, detecting multiple pathogens simultaneously affecting a single plant leaf. Unfortunately, a major hurdle in this research area is the scarcity of publicly available datasets containing images captured under varying conditions. To tackle this issue, we introduce a dataset called CoSEV for cotton diseased leaf images.

The CoSEV dataset consists of 496 filtered images of cotton leaves, captured both in controlled conditions and real-field settings using a smartphone camera. After applying augmentation techniques, the total number of images reaches 1186, encompassing a diverse range of situations, including the presence of multiple stresses co-occurring on a single leaf and the progression of disease severity. The dataset has been meticulously organized into 5 classes, with 7 categories representing different levels of cotton curl severity and coexisting diseases. The dataset is uploaded both for detection and classification.

To assess the effectiveness of the CoSEV dataset, we conducted experiments using various state-of-the-art detection models. These models were thoroughly analyzed to evaluate their performance in accurately identifying and classifying the different diseases and severity stages present in the dataset.

The purpose of the investigation is to obtain information on the occurrence of infectious diseases to assess the development of the epidemiological situation in the Czech Republic, to monitor the health status of the population and to manage the provision of health care.

The EPIDAT program was developed in 1991 to ensure mandatory reporting, registration and analysis of the occurrence of infections, which follows on from ISPO (Information System of Transmissible Diseases). From 1993 to 2017, EPIDAT was used nationwide at all hygiene stations as the basis of local, regional and national infectious disease surveillance.

Statistical unit of inquiry: The statistical unit is the selected infectious disease. Confirmed disease, suspected disease, carriage, death are reported. Individual cases are statistically monitored according to ICD-10. Certain serious infectious diseases monitored by other separate information systems and registries are not the subject of reporting. These are tuberculosis disease (dg. A15–A19), infections transmitted mainly through sexual contact (dg. A50–A64) and human immunodeficiency virus disease HIV (dg. B20–B24). Acute respiratory infections (ARI) and influenza-like illness (ILI) also have a separate information system. In the EPIDAT information system, some diseases listed in other chapters IV, X and XX are also monitored. ICD-10, which is related to infectious diseases.

Disease-Specific Faces (DSF) database is used to research the phenotype and genotype of the diseases.Disease-Specific Face images collected from:♦ Professional medical publications♦ Professional medical websites♦ Medical Forums♦ Hospitalswith definite diagnostic results.The database is updated every three months.If you would like to use DSF database, please send email to genex.tw@gmail.com.