This dataset contains 3 main feature classes. See the detailed description of each feature class in the individual metadata files below:
MNDNR Native Plant Communities
DNR NPC and Land Cover - EWR
DNR NPC and Land Cover - Parks and Trails

The Antarctic Plant Database is a database of the plant collections held in the British Antarctic Survey's herbarium (international code AAS). This contains over 50,000 plant specimens from Antarctica, the sub-Antarctic Islands and surrounding continents (especially Fuegia and Patagonia). Over 2000 species are represented, comprising predominantly mosses, liverworts and lichens with smaller collections of vascular plants, macro-algae and macro-fungi.

Juliekyungyoon/plant-kaggle-seg-data dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset Plant

## Overview

Dataset Plant is a dataset for object detection tasks - it contains Plant annotations for 234 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).

Plant Disease Prediction Dataset

Context

Plant diseases cause significant agricultural losses worldwide. Early prediction of disease outbreaks can help farmers take preventive measures. This synthetic dataset simulates environmental conditions that might lead to fungal infections in plants.

Content

The dataset contains 10,000 samples representing environmental measurements from different farm locations with the following features:

• temperature: Measured in degrees Celsius
• humidity: Measured as percentage
• rainfall: Measured in millimeters
• soil_pH: Acidity/alkalinity measurement
• disease_present: Binary label (0 = healthy, 1 = diseased)

The relationships between environmental factors and disease presence are complex and non-linear, mimicking real biological systems.

Source

Dataset generated for educational purposes based on general agricultural research.

Potential Uses

• Binary classification practice
• Feature importance analysis
• Understanding feature interactions
• Testing model robustness
• Imbalanced classification techniques

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">

The data file contains occurrence data based on historical observations and records between 1651 and 2004. Ten plant species have been studied : Alnus incana (L.) Moench, 1794 ; Buddleja davidii Franch., 1887 ; Castanea sativa Mill., 1798 ; Helianthus tuberosus L., 1753 ; Impatiens glandulifera Royle, 1833 ; Prunus cerasifera Ehrh., 1784 ; Prunus laurocerasus L., 1753 ; Reynoutria japonica Houtt., 1777 ; Robinia pseudoacacia L., 1753 ; and Spiraea japonica L.f., 1782. The data file is the result of a geo-historical study conducted over five months on the invasive plants species's introduction and distribution in Occitania (France), carried out within the framework of the EI2P-VALEEBEE project (Invasive species and pollinators, between constraints and potentials). Historical sources have been consulted during 2020 in order to find the oldest elements about the ten species. Each data corresponds to an historical observation or mention on one of the ten species of the study mainly on Metropolitan French territory since their introduction. Without an historical analysis, it is difficult to understand the current local distribution dynamics of invasive plant species, especially when some of them have been introduced on Metropolitan French territory for several centuries. All the interest of these occurrence data is to bring an historical depth allowing us to apprehend the local distribution of the ten species of the study over time. This can be allowed thanks to the record of several elements on their places of introduction, the comments from authors and observers on their abundance, and elements on the historical context of introduction. More generally, this historical data file is part of a multidisciplinary approach proposed by the members of EI2P project whose objective is to better take into account the ecological socio-cultural and economic issues raised by the issue of invasive alien plants.

This work was endorsed by the CNRS/INEE Zone Atelier Pyrénées Garonne (ZA PYGAR). The Zones Ateliers network (RZA) is recognized by ALLENVI, as an eLTER (European Long-Term Ecological Research).

A data paper explains precisely this dataset: Claudel M, Lerigoleur E, Brun C, Guillerme S (2022) Geohistorical dataset of ten plant species introduced into Occitania (France). Biodiversity Data Journal 10: e76283. https://doi.org/10.3897/BDJ.10.e76283

[NOTE: PLEXdb is no longer available online. Oct 2019.] PLEXdb (Plant Expression Database) is a unified gene expression resource for plants and plant pathogens. PLEXdb is a genotype to phenotype, hypothesis building information warehouse, leveraging highly parallel expression data with seamless portals to related genetic, physical, and pathway data. PLEXdb (http://www.plexdb.org), in partnership with community databases, supports comparisons of gene expression across multiple plant and pathogen species, promoting individuals and/or consortia to upload genome-scale data sets to contrast them to previously archived data. These analyses facilitate the interpretation of structure, function and regulation of genes in economically important plants. A list of Gene Atlas experiments highlights data sets that give responses across different developmental stages, conditions and tissues. Tools at PLEXdb allow users to perform complex analyses quickly and easily. The Model Genome Interrogator (MGI) tool supports mapping gene lists onto corresponding genes from model plant organisms, including rice and Arabidopsis. MGI predicts homologies, displays gene structures and supporting information for annotated genes and full-length cDNAs. The gene list-processing wizard guides users through PLEXdb functions for creating, analyzing, annotating and managing gene lists. Users can upload their own lists or create them from the output of PLEXdb tools, and then apply diverse higher level analyses, such as ANOVA and clustering. PLEXdb also provides methods for users to track how gene expression changes across many different experiments using the Gene OscilloScope. This tool can identify interesting expression patterns, such as up-regulation under diverse conditions or checking any gene’s suitability as a steady-state control. Resources in this dataset:Resource Title: Website Pointer for Plant Expression Database, Iowa State University. File Name: Web Page, url: https://www.bcb.iastate.edu/plant-expression-database [NOTE: PLEXdb is no longer available online. Oct 2019.] Project description for the Plant Expression Database (PLEXdb) and integrated tools.

Explore our Plant Disease Image Dataset, featuring a diverse collection of labeled images for developing and testing machine learning models in agriculture.

Outdoor Plant

## Overview

Outdoor Plant is a dataset for classification tasks - it contains Plant annotations for 300 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 database contains plant distribution records based on research and literature data.

Dataset

This dataset was created by Alex Olariu

Contents

It contains the following files:

The Invasive Plant Atlas of the MidSouth (IPAMS) is a project of the Geosystems Research Institute (GRI), Mississippi State University. The Invasive Plant Atlas of the MidSouth (IPAMS) will provide information on the biology, distribution, and best management practices for forty weedy plant species. Outreach and extension activities include developing training programs for volunteers to identify and report invasive species using IPAMS, developing an efficient Early Detection and Rapid Response (EDRR) system for invasive plants, developing best management practices workshops, and developing an online mapping system. Research activities include conducting systematic regional vegetation surveys to assess the distribution of key invasive plants, developing models for predicting the occurrence of target species based on land use and cover, and evaluate the relative effectiveness of professional versus volunteer surveys. IPAMS is a unique tool for early detection and rapid response. Trained volunteers regularly check areas for invasive species. When they encounter an invasive species, they record the location by GPS, the plant and other information about the plant population. On the IPAMS Web site, they input this information and alert researchers and government officials of the new infestation. This gives those decision-makers more time in taking appropriate steps and treating an infestation early, before it spreads further.

This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 United States License.

# Data origins
The dataset is originally hosted at PlantVillage Disease Classification Challenge.
We use the modified version in this github repository to do controlled experiments.
We only use the raw color images dataset and delete the unconventional characters in the classes directory name and `.csv` filenames.

# Directory explanation
The `80-20` direcotry has multiple `.txt` files which contain the training (~80%), validation(~10%) and testing (~10%) datasets instances filenames and the corresponding label indexes. The validation dataset quantity is `5430` in all data separation. In our experiment code (not included in this archive), the validation and testing dataset are merged together.

# Data usage
## Replicate our experiments
We have used this dataset in writing our paper. The reference information can be seen at https://gitlab.com/huix/leaf-disease-plant-village.

### Steps
1. `cd` to the direcotry (e.g. `/home/usrname/plantvillage_deeplearning_paper_dataset`) that contains the `color` directory.
2. run `python change_filename_prefix.py --prefix /home/usrname/plantvillage_deeplearning_paper_dataset` to modify the prefix path (which is `/home/h/plantvillage_deeplearning_paper_dataset` in our former generated datasets).
3. Fin. You can use our opens ource codes repository to do the later experiments.

## Generate your own training/validation/testing datasets
This data separation generating code isn't included in the dataset archive, it is in our open source code. Please see our open source code repository for the detailed information.
If you have any questions, you can contact the author through email.
The email address is a QR code in the archive.

The Global Power Plant Database is a comprehensive, open source database of power plants around the world. It centralizes power plant data to make it easier to navigate, compare and draw insights for one’s own analysis. The database covers approximately 35,000 power plants from 167 countries and includes thermal plants (e.g. coal, gas, oil, nuclear, biomass, waste, geothermal) and renewables (e.g. hydro, wind, solar). Each power plant is geolocated and entries contain information on plant capacity, generation, ownership, and fuel type. It will be continuously updated as data becomes available.

Dataset

This dataset was created by Sanam Peeyush

Contents

This dataset consists of 4,397 insect species associated with 679 native plant species, 120 archaeophytes, and 234 neophytes from the Database of Insects and their Food Plants (DBIF). The DBIF details approximately 60,000 interactions between phytophagous insect (and mite) species and plants recorded in Great Britain over the last century, based on a wide variety of sources, including entomological journals and field guides. The data here represents a reduced subset of the full DBIF (13,277 interactions), only including interactions resolved to the species level (insect species x associated with host plant species y), records that have been expertly verified as reliable and included in previous large-scale analyses (Ward 1988; Ward & Spalding 1993; Ward et al. 1995; Ward et al. 2003), and records that are certain to have occurred in Great Britain. Any records originating from captive breeding studies are excluded. Finally, only plants with associated phylogenetic data and native status are included. Host plant distribution size is also included, in addition to a quantification of the distinctiveness of the insect communities found on a subset of the non-native plants. This work was supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability.

Dataset

This dataset was created by Soubhik Sutradhar

Contents

1. The horticulture industry places a high value on money plants because of their hardiness and aesthetic attractiveness. Nevertheless, several illnesses might have a substantial negative influence on their well-being and output, making cultivation difficult. For a therapy to be effective, leaf diseases must be accurately and quickly identified. High-resolution photos of money plant leaves were taken at the Savar demonstration site in Dhaka, Bangladesh, and are included in this dataset. The photos are divided into three different classes: Manganese Toxicity (72 images), Bacterial Wilt Disease (66 images), and Healthy (175) images. These classes represent both damaged and healthy leaves. The dataset has 313 photos in total. Comprehensive comments that describe the nature and severity of the condition are included with every photograph. For accurate and trustworthy model training and validation, this data is essential. The information also contains metadata that records the location and surrounding circumstances at the time the photograph was taken. Understanding the environmental factors influencing the prevalence of disease and enhancing the accuracy of predictive models require this contextual information.
2. At the moment, there are a lot of potential deep learning and computer vision techniques to handle these kinds of categorization and detection problems.
3. To create deep learning techniques, an extensive money plant disease dataset is provided. The subject matter expert from an agricultural institute collaborated with us to construct the classifications for this dataset.
4. From the Savar demonstration place in Dhaka, Bangladesh, a total of 313 photos depicting Bacterial Wilt Disease (66), Healthy (175), and Manganese Toxicity (72) were collected. Then, using methods like flipping, width shifting, height shifting, brightening, rotating, shearing, and zooming, 15,000 augmented images are made from these original photos in order to increase the quantity of data sets.