A database of images of approximately 960 unique plants belonging to 12 species at several growth stages is made publicly available. It comprises annotated RGB images with a physical resolution of roughly 10 pixels per mm.

The relationship between the plants and the environment is multitudinous and complex. They help in nourishing the atmosphere with diverse elements. Plants are also a substantial element in regulating carbon emission and climate change. But in the past, we have destroyed them without hesitation. For the reason that not only we have lost a number of species located in them, but also a severe result has also been encountered in the form of climate change. However, if we choose to give them time and space, plants have an astonishing ability to recover and re-cloth the earth with varied plant and species that we have, so recently, stormed. Therefore, a contribution has been made in this work towards the study of plant leaf for their identification, detection, disease diagnosis, etc. Twelve economically and environmentally beneficial plants named as Mango, Arjun, Alstonia Scholaris, Guava, Bael, Jamun, Jatropha, Pongamia Pinnata, Basil, Pomegranate, Lemon, and Chinar have been selected for this purpose. Leaf images of these plants in healthy and diseased condition have been acquired and alienated among two separate modules.

Principally, the complete set of images have been classified among two classes i.e. healthy and diseased. First, the acquired images are classified and labeled conferring to the plants. The plants were named ranging from P0 to P11. Then the entire dataset has been divided among 22 subject categories ranging from 0000 to 0022. The classes labeled with 0000 to 0011 were marked as a healthy class and ranging from 0012 to 0022 were labeled diseased class. We have collected about 4503 images of which contains 2278 images of healthy leaf and 2225 images of the diseased leaf. All the leaf images were collected from the Shri Mata Vaishno Devi University, Katra. This process has been carried out form the month of March to May in the year 2019. The images are captured in a closed environment. This acquisition process was completely wi-fi enabled. All the images are captured using a Nikon D5300 camera inbuilt with performance timing for shooting JPEG in single shot mode (seconds/frame, max resolution) = 0.58 and for RAW+JPEG = 0.63. The images were in .jpg format captured with 18-55mm lens with sRGB color representation, 24-bit depth, 2 resolution unit, 1000-ISO, and no flash.

Further, we hope that this study can be beneficial for researchers and academicians in developing methods for plant identification, plant classification, plant growth monitoring, leave disease diagnosis, etc. Finally, the anticipated impression is towards a better understanding of the plants to be planted and their suitable management.

PlantDoc is a dataset for visual plant disease detection. The dataset contains 2,598 data points in total across 13 plant species and up to 17 classes of diseases, involving approximately 300 human hours of effort in annotating internet scraped images.

Waterwise plant information. Includes information like botanical names; water, climate, soil and light needs; level of maintenance required etc.

Basil/Tulsi Plant is harvested in India because of some spiritual facts behind this plant,this plant is used for essential oil and pharmaceutical purpose. There are two types of Basil plants cultivated in India as Krushna Tulsi/Black Tulsi and Ram Tulsi/Green Tulsi.Many of the investigator working on disease detection in Basil leaves where the following diseases occur 1) Gray Mold 2) Basal Root Rot, Damping Off 3) Fusarium Wilt and Crown Rot4) Leaf Spot5) Downy MildewThe Quality parameters (Healthy/Diseased) and also classification based on the texture and color of leaves. For the object detection purpose researcher using an algorithm like Yolo, TensorFlow, OpenCV, deep learning, CNNI had collected a dataset from the region Amravati, Pune, Nagpur Maharashtra state the format of the images is in .jpg.

The PLANTS Database provides standardized information about the vascular plants, mosses, liverworts, hornworts, and lichens of the U.S. and its territories

The Existing Hydropower Assets (EHA) Plant Database is a geospatially comprehensive point-level dataset containing locations and key characteristics of U.S. hydropower plants that are currently operational.

Fruit and vegetable plants are vulnerable to diseases that can negatively affect crop yield, causing planters to incur significant losses. These diseases can affect the plants at various stages of growth. Planters must be on constant watch to prevent them early, or infestation can spread and become severe and irrecoverable. There are many types of pest infestations of fruits and vegetables, and identifying them manually for appropriate preventive measures is difficult and time-consuming.This pretrained model can be deployed to identify plant diseases efficiently for carrying out suitable pest control. The training data for the model primarily includes images of leaves of diseased and healthy fruit and vegetable plants. It can classify the multiple categories of plant infestation or healthy plants from the images of the leaves.Licensing requirementsArcGIS Desktop — ArcGIS Image Analyst extension for ArcGIS ProArcGIS Enterprise — ArcGIS Image Server with raster analytics configuredArcGIS Online — ArcGIS Image for ArcGIS OnlineUsing the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS. Note: Deep leaning is computationally intensive, and a powerful GPU is recommended to process large datasets.Input8 bit, 3-band (RGB) image. Recommended image size is 224 x 224 pixels. Note: Input images should have grey or solid color background with one full leaf per image. OutputClassified image of the leaf with any of the plant disease, healthy leaf, or background classes as in the Plant Leaf Diseases dataset.Applicable geographiesThis model is expected to work well in all regions globally. However, results can vary for images that are statistically dissimilar to training data.Model architecture This model uses the ResNet50 model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an overall accuracy of 97.88 percent. The confusion matrix below summarizes the performance of the model on the validation dataset. Sample resultsHere are a few results from the model:Ground truth: Apple_black_rot / Prediction: Apple_black_rotGround truth: Potato_early_blight / Prediction: Potato_early_bightGround truth: Raspberry_healthy / Prediction: Raspberry_healthyGround truth: Strawberry_leaf_scorch / Prediction: Strawberry_leaf_scorch

The dataset presented contains information collected by sensors installed at the entrances of water treatment plants in several municipalities in the Cauca department in Colombia. These sensors have measured hydrological and meteorological data essential to understanding the quality and quantity of water supply. Hydrological data includes water dissolved oxygen, conductivity, pH, and turbidity measurements. Additionally, meteorological data includes relative humidity, precipitation, and wind speed measurements. This dataset is useful for researchers, scientists, and water professionals looking to understand the behavior of water resources in the Cauca region. The collected data can be used to evaluate water quality, detect potential contaminants, and understand how weather conditions affect the water supply in the region. This dataset was compiled by the Aquarisc Project. For more information about the project, please visit the following link: http://anterior.cauca.gov.co/noticias/aquarisc-proyecto-de-la-gobernacion-del-cauca-busca-generar-uso-adecuado-y-conservacion-del

Here we present version 2.0 of the China Plant Trait Database, which contains information on morphometric, physical, chemical, photosynthetic and hydraulic traits from 1529 unique species in 140 sites spanning a diversity of vegetation types. Version 2 has five improvements compared to the previous version: (1) new data from a 4-km elevation transect on the edge of Tibetan Plateau, including alpine vegetation types not sampled previously; (2) inclusion of traits related to hydraulic processes, including specific sapwood conductance, the area ratio of sapwood to leaf, wood density and leaf turgor loss point; (3) inclusion of information on soil properties to complement the existing data on climate and vegetation (4) assessments of the reliability of individual trait measurements; and (5) inclusion of standardized checklists and templates for systematical field sampling and measurements. See detailed descriptions here: Wang, H., Harrison, S.P., Li, M. et al. The China plant trait database version 2. Sci Data 9, 769 (2022). https://doi.org/10.1038/s41597-022-01884-4

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

Original Title: Concentrations of plant growth promoting compounds in soils and hydroponics due to the interaction of plants and earthworms

Original Description: Concentrations of plant growth promoting compounds in soils and hydroponics due to the interaction of plants and earthworms

Data comprise phytohormone concentrations (plant growth hormones: adenosine, zeatin, isopentenyladenosine, indole-3-acetic acid and abscisic acid) measured during plant growth experiments in soil and hydroponic growth media in the presence and absence of earthworms (Lumbricus terrestris and Eisenia fetida respectively). Also presented are plant biomass, pH of the hydroponic solution and soil biological activity (concentration of Fluorescein diacetate - a measure of the hydrolytic capacity) at the end of the study. The study was funded by the NERC (Grant number NE/M000648/1). Mass spectrometry was carried out in The York Centre of Excellence in Mass Spectrometry; the centre was created thanks to a major capital investment through Science City York, supported by Yorkshire Forward with funds from the Northern Way Initiative, and subsequent support from EPSRC (EP/K039660/1; EP/M028127/1). Full details about this dataset can be found at https://doi.org/10.5285/809cd6e8-0615-45ff-b79b-6ba1ae474713

Source: https://ckan.publishing.service.gov.uk/dataset/concentrations-of-plant-growth-promoting-compounds-in-soils-and-hydroponics-due-to-the-interact
Last updated at https://ckan.publishing.service.gov.uk/dataset : 2020-03-06

The present Article , a plant base Miccell (PBMC) has been proposed as a new electrical energy source to power low power consumption devices such as a transmitter. The PBMC constitutes of a power management system that is connected to Cu-Zn electrode pairs which are embedded into the leaves of the Aloe Vera plants. The proposed power management system can perform a fully autonomous operation to harvest the electrical energy from the Aloe Vera plants to trigger a transmitter load to send signal periodically to the temperature and humidity sensor. This has been confirmed by performing the experiment under a real-life condition. The designed power management circuit, which consists of an energy storage system and a voltage regulation system, can store the minute energy harvested from the Aloe Vera plants and boost them into sufficient energy to power a transmitter load. The transmitter load is proven to be in operation as it sends an intermittent signal to the receiver circuit to activate a remote sensor to measure the surrounding temperature and humidity. Thus, it is experimentally proven in this paper that Aloe Vera plants can be used as an energy source to provide electrical energy and its combination with the proposed power management circuit can act as a plant base cell. The idea of the proposed plant as a battery source can provide significant benefits in IoT application especially in remote areas or dense forest where replacing battery or recharging battery is impossible. The proposed cell can also be employed for precision farming and environmental monitoring where plants are available in abundant.Proposes a design of a power management circuit that can harness, store and manage the electrical energy which is harvested from the leaves of (Aloe Vera) plants to trigger a transmitter load to power a remote sensor. In the present paper, we have proposed a power management circuit, which can harvest the electrical energy from the Aloe Vera plants and converts the plants into a plant-based cell (PBC) to activate a remote sensor via a wireless transmission.The power management circuit consists of two sections namely; an energy storage system that acts as an energy storage reservoir to store the energy harvested from the plants as well as a voltage regulation system which is used to boost and manage the energy in accordance to a load operation.

description: Plant community areas are areas appropriate for specific communities of plants, aka "phytocoenosis" or "phytocenosis". The plant database and plant community areas are used in the San Francisco Plant Finder website (http://sfplantfinder.org) which allows to you search for an address or click on a map and view details of plants recommended for that location. You can also see the plants that relate to these different areas in the plant finder data: https://data.sfgov.org/d/vmnk-skih; abstract: Plant community areas are areas appropriate for specific communities of plants, aka "phytocoenosis" or "phytocenosis". The plant database and plant community areas are used in the San Francisco Plant Finder website (http://sfplantfinder.org) which allows to you search for an address or click on a map and view details of plants recommended for that location. You can also see the plants that relate to these different areas in the plant finder data: https://data.sfgov.org/d/vmnk-skih

This dataset (TRECflora.csv) and accompanying metadata file (TRECflora-metadata.doc) were first presented in the 2018 Proceedings of the Florida State Horticultural Society and updated April 2019 and Dec 2022.

Abstract. The Tropical Research and Education Center (TREC) is the southernmost campus of the University of Florida and is in Miami-Dade County, FL. TREC is dedicated to research, teaching, and extension in natural resources, agriculture, and ornamental, vegetable, fruit, and biofuel crops. As part of this mission, a large diversity of cultivated plants was established at TREC, while also preserving native pine rockland and rockdale hammock. The mission of TREC was recently expanded to include agroecology, which investigates the production, diversity, and resilience of agricultural systems relative to surrounding areas. Identifying TREC plant species is a first step in establishing this context for agricultural systems. Although many partial lists of vascular plant species have been made for TREC, there has been no comprehensive list. We report a comprehensive inventory of plant species diversity completed over the last decade (TRECflora.csv). Each plant species was identified with botanical keys and confirmed with online specimens or a previous report from a trusted source. The inventory currently has 707 species including 240 native; 237 established, non-native; 190 non-established, cultivated; and 40 unknowns. We gathered detailed information for each species identified, including plant use, general habitat, weediness, specific location, and identification sources other than the authors. This data will support agroecological research into the effects of plant diversity and surrounding areas on crop physiology and production.

Dataset

This dataset was created by Aryan Chaudhary 24

Released under CC0: Public Domain

Contents

The dataset contains aerial agricultural images of a potato field with manual labels of healthy and stressed plant regions. The images were collected with a Parrot Sequoia multispectral camera carried by a 3DR Solo drone flying at an altitude of 3 meters. The dataset consists of RGB images with a resolution of 750×750 pixels, and spectral monochrome red, green, red-edge, and near-infrared images with a resolution of 416×416 pixels, and XML files with annotated bounding boxes of healthy and stressed potato crop.

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">

Traits of Plants in Canada (TOPIC) Open Access provides an access to TOPIC datasets as data become publicly available. The database is organized in two separate modules: 1) The Literature review module aims to consolidate data gathered from the scientific literature. 2) The Empirical measurements module brings together datasets of observations collected in the field, laboratory, or greenhouse.
The Traits of Plants in Canada (TOPIC) database acts as a hub for centralizing knowledge on plant functional traits in Canada. Under the Canadian Trait Network, this database allows the integration of trait data from large, disconnected scientific sources to facilitate research on plant and forest ecology, community ecology and forest sustainability. Following international standards, the database ensures that the datasets are properly documented and archived, facilitating their re-use and discoverability.

**Please cite TOPIC open as follows: ** Aubin, I., Boisvert-Marsh, L., Munson, A.D. 2021. Traits of plants in Canada (TOPIC) Open access - Traits des plantes au Canada (TOPIC) ouvert. doi: https://doi.org/10.23687/bb14c6bf-75f7-4ff2-b97e-689fa768905c

**And TOPIC as follows: ** Aubin, I, Cardou, F., Boisvert‐Marsh, L., Garnier, E., Strukelj, M, Munson, A.D. 2020. Managing data locally to answer questions globally: The role of collaborative science in ecology. Journal of Vegetation Science. 31: 509–517.