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.

Dataset

This dataset was created by Shivani Pore

Released under Database: Open Database, Contents: Database Contents

Contents

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.

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.

The dataset contains 3D point cloud data of a synthetic plant with 10 sequences. Each sequence contains 0-19 days data at every growth stage of the specific sequence.

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">

• In our surroundings, there are various types of plants. In our daily lives, we use products derived from these plants in many ways. We depend on a vast kingdom of plants to meet all the needs of life, including food, clothing, shelter, education, and healthcare. We have already gained a lot of knowledge about this relationship. We have seen or heard that when children have a cold or cough at home, they are given a mixture of tulsi (holy basil) leaves and a few drops of honey. As a result, their cold and cough subside, and they feel relieved. If someone suddenly gets a cut on any part of their body, washing the wound with the juice of the aloe vera leaf or applying a poultice of durba grass can be beneficial. This helps stop bleeding, and within two to three days, the wound dries up, and the person recovers. The plants in our environment that are used for the relief or cure of diseases are called medicinal plants. • At one time, Bangladesh was rich in medicinal plants. Fields, riverbanks, roadsides, and forests were abundant with numerous medicinal plants. Due to the increase in population, the diverse use of land has risen. Additionally, due to ignorance, negligence, and lack of attention, the primary habitat of these medicinal plants, natural forest land, has decreased. Consequently, valuable tree resources like these have been diminished. Many species have already become extinct. Despite this, our country still has a sufficient number of medicinal plants scattered across remote and less-explored areas. We are not familiar with all of them. It is crucial for us to be aware of these medicinal plants, recognize them, and understand their uses and properties. As a result, we can play a significant role in the holistic well-being of the general population in our country by contributing to the management and cure of various diseases. • This medicinal plant identification dataset would likely consist of a collection of images and associated metadata related to various medicinal plants. The dataset would serve as a resource for developing and training machine learning models for the automatic identification and classification of medicinal plants. • Six distinct kinds of medicinal plants are shown in this large dataset, which can be used to develop machine vision-based techniques: Arjun Leaf, Curry Leaf, Marsh Pennywort Leaf, Mint Leaf, Neem Leaf, and Rubble Leaf. • In reality, 1380 images of medicinal plants were initially collected from the field. Subsequently, to increase the quantity of data points, various image processing techniques were applied, such as shifting, flipping, zooming, shearing, brightness enhancement, and rotation, resulting in a total of 9660 augmented images derived from the original images.

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

Dataset

This dataset was created by ANSHUL VERMA

Contents

This dataset comprises the various supervised learning tasks considered in the agro-nt paper. The task types include binary classification,multi-label classification, regression,and multi-output regression. The actual underlying genomic tasks range from predicting regulatory features, RNA processing sites, and gene expression values.

These data were analyzed for the publication: Plant Density Recommendations and Plant Nutrient Status for High-Tunnel Tomatoes in Virginia. Authors: Emmanuel Torres-Quezada, Ricardo José Gandini-Taveras File: Tomato_Plant_Data Variables include: Plant height (inches), Unmarketable fruit number, Unmarketable fruit weight (lb), Marketable fruit number, Marketable fruit weight (lb), Maximum temperature (Fahrenheit), minimum temperature (Fahrenheit), Mean temperature (Fahrenheit), Rain (inches), N (%), P (%), K (%), S (%), Ca (%), Mg (ppm), Zn (ppm), Fe (ppm), Mn (ppm), Cu (ppm), B (ppm), Na (ppm), Al (ppm), Mo (ppm).

Here are a few use cases for this project:

1. Agricultural Monitoring and Disease Management: Farmers and agricultural organizations can use the "disease cotton plant" computer vision model to monitor large-scale cotton fields for any signs of disease. Early detection and management can help in controlling the spread and minimizing the impact of diseases on the cotton crop.

2. Smart Crop Insurance: Insurance companies can provide policyholders with tailored protection by using the computer vision model to assess the health of cotton plants in a certain area. By identifying affected plants, they can offer insurance plans that accurately reflect the risk posed by diseases and other factors.

3. Agricultural Consultancy Services: Expert consultants can use the "disease cotton plant" computer vision model to advise farmers on the best methods to prevent, manage, and treat diseases affecting their cotton crops. The model can also be used for training and capacity building in disease management among extension officers and local community.

4. Research and Development: Researchers can use the model as a tool to study various aspects of cotton plant diseases, their patterns, and their impact on crop yield. This information can be valuable for creating new treatment strategies and understanding how diseases spread to improve future prevention measures.

5. Supply Chain Management: Companies dealing with cotton production can use the computer vision model to ensure the quality of sourced raw cotton material. By identifying diseased plants earlier in the supply chain, businesses can maintain a high-quality product and prevent the spread of diseases to other areas of production.

This API provides data on electric fuel consumption, fuel consumption, and net generation at the plant level. Annual, quarterly, and monthly data available. Based on Form EIA-906, Form EIA-920, and Form EIA-923 data.

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.

This dataset lists details relating to Plant activities.

This unique and huge data set contains plant information for the Himalaya Uplands; it consists of 164,360 records. This database is implemented in MS ACCESS following ABCD 1.2. It describes Asian plant species related to the Tibetan Plateau, Central Asia. Data have been collected for over 50 years, and in over 11 countries (e.g. Afghanistan, Pakistan, Bhutan, China,India, Kazakhstan, Kyrgyztan, Myanmar, Nepal, Russia, Tajikistan, Turkmenistan, Uzbekistan), covering over 220 national regions. Taxonomic information for this region is diverse and not well studied. However, the database follows ICBN taxonomy matched with ITIS and consists of over 5,562 unique species entries. From these, ITIS has 996 species listed. Over 2,200 collectors from all over the world contributed to this dataset, which mostly was compiled and maintained by the author for over 20 years. This database covers 21,869 localities. virtually all sites are georeferenced with latitude and longitude (2 decimals; geographic datum of WGS84), and 6,668 of such unique locations are found in the HUP database. This dataset has altitude information provided by the fieldworker.

In 2010-2013, the U. S. Fish and Wildlife Service (USFWS) partnered with Utah State University to conduct invasive plant prioritization workshops and inventories on selected National Wildlife Refuges across the United States. The purpose of these workshops and subsequent inventories was to inform and improve the process of planning and implementing invasive plant inventories or early detection. These workshops highlighted the need for an objective, transparent and documented process for deciding which invasive plant species should be a focus of inventory or early detection (and ultimately management) and where. A result of this partnership is the Invasive Plant Inventory and Early Detection Prioritization Tool (IPIEDT) and associated user's guide. The tool is a Microsoft Access database (2010 or later) that utilizes site-specific knowledge and harnesses existing invasive plant information (invasive species risk rankings) to identify priority species and areas for inventory or early detection. The tool produces a ranked list of areas and invasive plant species to consider for inventory or early detection. Once the location and abundance of priority invasive plants are understood, this information can be used to decide what specific management strategies should be employed and where. Interior 1 Regional and Refuge staff used the database developed by Utah State and the Pacific Southwest Region to prioritize invasives species at the Eastern Shore of Virginia. The attached products are the result of that prioritization.

The Established Invasive Plant Species Monitoring database is the main storage location for all data related to the Established Invasive Plant Species Monitoring Protocol. It contains spatial information for transects that were surveyed, along with the species detected along the transect, and the species cover class. There is a front end user interface MS Access file and a back end MS Access file that contains the data tables. The Pacific Island Network Inventory and Monitoring (I&M) Program collected data on the status of established invasive plant species in the mangrove community of American Memorial Park (AMME), in the wet forest and subalpine plant communities of Hawaii Volcanoes National Park (HAVO) and Haleakala National Park (HALE) and in the wet forests and coastal community of Kalaupapa National Historical Park (KALA). Specifically, crews collected data on nonnative species richness, frequency and cover along 250, 500 and 1000 meter long belt transects.

jibrand/plant-dataset-JSONL dataset hosted on Hugging Face and contributed by the HF Datasets community