Here we provide an updated image dataset and supporting data files, version 2, for the following primary article. Please refer to the primary article as well as the supporting data and updates provided here for all details.Wilf P, SL Wing, HW Meyer, J Rose, R Saha, T Serre, NR Cúneo, MP Donovan, DM Erwin, MA Gandolfo, E González-Akre, F Herrera, S Hu, A Iglesias, KR Johnson, TS Karim, X Zou. 2021. An image dataset of cleared, x-rayed, and fossil leaves vetted to plant family for human and machine learning. PhytoKeys 187: 93–128, doi:10.3897/phytokeys.187.72350The dataset version that corresponds exactly to the published article remains archived here as version 1 and is easily accessed by toggling the dataset version in this window.The total image-collection size is now 34,368, consisting of 30,252 images of cleared and x-ray leaves and 4,116 of fossils.Change list, version 1 to version 2:1) Addition of NMNS Cleared Leaf Database (4,076 images).The most significant change in version 2 is the addition of 4,076 images from the National Museum of Nature and Science (NMNS, Ibaraki, Japan) Cleared Leaf Database, made possible by the kind assistance of Dr. Atsushi Yabe, who is included here as a coauthor of version 2. The collection was made by Drs. Toshimasa Tanai of Hokkaido University and Kazuhiko Uemura of NMNS. More information on the NMNS Cleared Leaf Database and one-at-a-time image access are available at the website: https://www.kahaku.go.jp/research/db/geology-paleontology/cleared_leaf/index.php?lg=en . A prior publication using the database is Iwamasa and Noshita (2023), PLoS Comput Biol 19: e1010581.All taxonomic names for the NMNS specimens as given were vetted and updated to species level by Edward Spagnuolo (acknowledged for his kind assistance) and P. Wilf using the Taxonomic Names Resolution Service (TNRS) and other sources (we note that the names attached to the prior cleared and x-rayed leaf images carried over from version 1 remain vetted only to family level, although taxa of interest can be easily updated using TNRS and many other resources). The vetted names were then used to update the as-given NMNS filenames to the same alpha-sortable format as the prior images (Family_Genus_species_dataset_catalognumber.jpg) and integrated into the same family folders for maximum ease of use. We thank Ivan Rodríguez for his kind assistance with this step.2) Filename cleanup in all directories and updates to affected catalog files.Thousands of filenames and their associated catalog entries were improved by batch-removing all periods and spaces (i.e. "sp." and "sp. ", "cf.", "aff.", "x. ") and cross-checked for consistency.3) A small number of new fossils were added, namely 34 leaves from Dipterocarpaceae and other families from the Pliocene of Brunei (Wilf et al. 2022, PeerJ and online supplement) and seven leaves of Macaranga kirkjohnsonii from the Eocene Laguna del Hunco flora, Chubut, Argentina (Wilf et al. 2023, Am. J. Bot. and online supplement).4) The following nomenclatural updates were applied to the filenames of all affected images (fossils and extant) and related catalog entries:Adoxaceae to Viburnaceae.Vauquelinia coloradensis to Kageneckia coloradensis (after Denk et al. 2023).Vauquelinia lineara to Vauquelinia liniara (typo correction).Browniea, Camptotheca, and the five living Nyssaceae genera are all now categorized in Nyssaceae (some were in Cornaceae in v. 1).File annotationsThe version 2 files are provided here as zip archives, as follows. As noted above, the version 1 files remain available by toggling the database version.Extant_Leaves_A-E_v2.0.zipExtant_Leaves_F-O_v2.0.zipExtant_Leaves_P-Z_v2.0.zipFamilies A–E, F–O, and P–Z, respectively, of cleared and x-rayed leaf images (30,252 images).Florissant_Fossil_v2.0.zipFossil-leaf image collection from Florissant Fossil Beds National Monument (3,320 images).General_Fossil_v2.0.zipFossil-leaf image collection from several other sites (796 images).General_Fossil_uncropped_v2.0.zipReference set of most of the uncropped image versions for the General Fossil collection, for access to scale bars and other archival information not otherwise available digitally (see main article and supplements linked in item 3 above). Filenames are suffixed with "_uncropped" and may have minor differences in format from the cropped set.supplemental_data_v2.0.zipArchive containing three files:Master_inventory_leavesdb_v2.0Master inventory file listing all extant and fossil specimens.See details in the main article (esp. table 1) for how to look up additional specimen data, which are easily available on the Web for most of the collections using the catalog numbers listed in this inventory file (also see below). Please note that the catalog numbers listed here may be primary or secondary, as described in the main article (table 1). The "old_Family" field preserves legacy data that can assist in locating physical specimens in the collections, which usually retain their original taxonomic organization (see main text).The other two files are catalogs of specimen data not otherwise available on the Web (see main article).General_fossils_catalog_v2.0.csvSpecimen data for the "General fossil" image collection. As mentioned in the primary article, several fossils retain their generic names, even if they are known to be botanically incorrect in publications or in the opinion of the present authors and thus placed in scare quotes in the primary article. In this case, the listed family name is regarded as correct. Scare quotes cannot be used in filenames and are thus omitted.Wing_x-ray_catalog_v2.0.csvVoucher data for the Wing X-Ray image collection.Technical notes for the Wing x-rays:Catalog number field in the Master Inventory file = negative number + leaf number as listed in this file.Example: "Wing_199-001" in the Master Inventory = negative 199, leaf 1 here =Alphonsea arborea(Annonaceae) = primary voucher US 904529.Some typographical errors in this legacy catalog are left as-is, and identifications are not updated here. Vetted spellings and updated family and order assignments can be found by catalog number (= negative + leaf number) in the Master Inventory file. This file includes some additional records that did not meet criteria for the image dataset.

Computer vision can predominantly be focused to design the strategies for the conservation of the plants. Previous decade’s trends and the current prevailing incidents with respect to global warming, forest fires, and famines act as potential indicators of how much nature is destroyed by human activities. Plants are vitally used in foodstuff, medicine, industry and as well for environmental protection. However, due to lack of resources and knowledge, it is difficult to recognize different plant species, plant diseases, etc. Nowadays modern equipment’s are being designed to address these issues. So considering the challenges, demands, we have constructed a database of different plants. The plants taken for study are the native plants of the Kashmir region of India. The climate of Kashmir remains chilling for a few months and pleasant for the rest of the year. Eight different plants namely Apple, Apricot, Cherry, Cranberry, Grapes, Peach, Pear, and Walnut are selected for the study based on their commercial and medicinal usage. The leaf is the primary object of reference taken for making the database, as they grow much earlier than fruits as well as the other plant parts. For each plant two types of leaves are selected, one healthy and the other diseased. Considering the natural conditions under which the farmers or the agriculturists have to work, the images are captured in broad daylight under the auto mode with the Nikon D-SLR digital camera with an ISO Speed = 100, Aperture = F/5.6, Flash = Not Fired, Shutter Speed = 1/640. All the images are captured by an 18-55 mm lens and are in .JPG format. The leaves are divided into two major classes A and B respectively. The two major classes were then divided into 16 sub classes i.e., eight healthy and eight diseased. The symbol “h” e.g., plant-name_h001 in the images represent healthy images and “d” i.e., plant-name_d001 represents the diseased images. The images are labeled, resized and classified into different classes. The class of healthy images comprises of a total of 1201 images and the diseased images constitute of a total of 935 images. Thus a total of 2136 images were selected from the captured images to sew up this database. Every little step towards a positive perspective marks the beginning of the era of growth with kindness.

Dataset Title: Plant Leaf Image Dataset

Description:
The Plant Leaf Image Dataset is a collection of high-quality images focusing on various plant leaves, aimed at supporting research and development in plant health monitoring, disease detection, and species identification. This dataset contains images that capture different plant species under varying conditions, allowing for diverse applications in agriculture, botany, and AI-based plant recognition.

Key Features: - Diversity: The dataset includes images from multiple plant species, providing a broad range for identifying and categorizing plant types. - Image Quality: High-resolution images ensure that the leaf textures, colors, and unique patterns are clear, making the dataset suitable for machine learning tasks. - Potential Use Cases: The dataset can be used for building and training models for plant species identification, disease detection, leaf classification, and agricultural monitoring tools.

Applications:
This dataset is particularly valuable for AI practitioners and researchers focused on agriculture-related projects, especially for those developing models in plant recognition, disease classification, and monitoring plant health. With the right preprocessing techniques, it can also serve as a base for projects aiming to improve crop management, sustainability, and yield predictions.

Format:
- Image files in standard formats (e.g., JPG or PNG). - Organized into folders based on plant type or condition for easy access and utilization.

This dataset is ready for integration into machine learning pipelines for training and evaluation in various agriculture and plant-related AI applications.

This data set provides carbon (C), nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) concentrations in green and senesced leaves. Vegetation characteristics reported include species growth habit, leaf area, mass, and mass loss with senescence. The data were compiled from 86 selected studies in 31 countries, and resulted in approximately 1,000 data points for both green and senesced leaves from woody and non-woody vegetation as described in Vergutz et al (2012). The studies were conducted from 1970-2009. There are two comma-delimited data files with this data set.

Motivation: Leaf traits represent an important component of plant functional strategies, and those related to carbon fixation and nutrient acquisition form the leaf economics spectrum. However, observations of functional leaf traits are underrepresented in tropical regions in comparison with those in temperate areas. Brazil, a country with continental scale and vast biodiversity is a timely example, where many biomes are impacted by human activities and climate change. However, leaf traits relevant to understand vegetation responses to these impacts remain poorly quantified for many species found in the country. We compiled an extensive data set of four functional leaf traits for native woody species occurring in the Brazilian territory. In addition to trait observations, sampling dates and geo-references were compiled and climatic parameters and soil properties of each sampling site were extracted from several databases.

Main types of variables contained: The LT-Brazil data set contains 3479, 1216, 775, and 775 clean observations of leaf mass per area, leaf nitrogen (N) concentration per unit mass, leaf phosphorus (P) concentration per unit mass, and leaf N : P ratio, respectively, from native woody species, encompassing information of biome, vegetation, taxonomic data, geographical coordinates, climatic parameters, as well as soil properties.

Spatial location and grain: We compiled trait observations from 223 sites under native vegetation distributed in all main biomes (i.e., Amazônia, Caatinga, Cerrado, Mata Atlântica, Pampa, and Pantanal) across the Brazilian territory.

Time period and grain: The data represent information published and/or sampled during the last 25 years.

Major taxa and level of measurement: Our compilation was focused on trait data observed for native woody species, excluding monocots, palm trees, herbs, and hemiparasitic plants. Thus, 108, 478, and 1321 botanical families, genera, and species were included, covering c. 9% of the woody angiosperm flora of Brazil.

Software format: Data are provided as comma-separated value (.csv) files.

Source: The leaves were taken from plants in the farm of the University of Mauritius and nearby locations. Donors: Trishen Munisami trishen.munisami @ gmail.com Mahess Ramsurn ramsurn.mahess @ umail.uom.ac.mu Somveer Kishnah s.kishnah @ uom.ac.mu Sameerchand Pudaruth sameerchand.pudaruth @ gmail.com Data Set Information: - The leaves were placed on a white background and then photographed. - The pictures were taken in broad daylight to ensure optimum light intensity. Attribute Information: List of plant species: 1. Beaumier du perou 2. Eggplant 3. Fruitcitere 4. Guava 5. Hibiscus 6. Betel 7. Rose 8. Chrysanthemum 9. Ficus 10. Duranta gold 11. Ashanti blood 12. Bitter Orange 13. Coeur Demoiselle 14. Jackfruit 15. Mulberry Leaf 16. Pimento 17. Pomme Jacquot 18. Star Apple 19. Barbados Cherry 20. Sweet Olive 21. Croton 22. Thevetia 23. Vieux Garcon 24. Chocolate tree 25. Carricature plant 26. Coffee 27. Ketembilla 28. Chinese guava 29. Lychee 30. Geranium 31. Sweet potato 32. Papa

The Global Spectra-Trait Initiative (GSTI) aims to generate generalizable spectra trait models using reflectance data to predict leaf traits associated with the photosynthesis capacity of leaves. It comprises a synthesized dataset of leaf trait data, input datasets and code. Leaf traits include the maximum carboxylation rate of rubisco (Vcmax), the maximum electron transport rate (Jmax), the dark respiration, as well as the prediction of leaf nitrogen, leaf mass per area (LMA), and leaf water content (LWC). The dataset comprises >7500 paired observations from around 400 species from a broad range of biomes. This dataset comprises a zip file of the GSTI GitHub repository (https://github.com/plantphys/gsti), the synthesized database (.csv) and database metadata files.

Plant Leaf

## Overview

Plant Leaf is a dataset for classification tasks - it contains Leaf annotations for 665 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 data set provides global leaf area index (LAI) values for woody species. The data are a compilation of field-observed data from 1,216 locations obtained from 554 literature sources published between 1932 and 2011. Only site-specific maximum LAI values were included from the sources; values affected by significant artificial treatments (e.g. continuous fertilization and/or irrigation) and LAI values that were low due to drought or disturbance (e.g. intensive thinning, wildfire, or disease), or because vegetation was immature or old/declining, were excluded (Lio et al., 2014). To maximize the generic applicability of the data, original LAI values from source literature and values standardized using the definition of half of total surface area (HSA) are included. Supporting information, such as geographical coordinates of plot, altitude, stand age, name of dominant species, plant functional types, and climate data are also provided in the data file. There is one data file in comma-separated (.csv) format with this data set and one companion file which provides the data sources.

There are two datasets and one table uploaded in this platform under the title "MED117_Medicinal Plant Leaf Dataset & Name Table". A folder is created with title "MED 117 Leaf Species". Inside this two sub folders with titles " Raw leaf image set of medicinal plants_v2" and "Segmented leaf set using UNET segmentation" are created. Raw leaf image set consists of leaf images of 117 medicinal plants found in Assam. All the samples are collected by visiting different (Govt, Public and Private) medicinal gardens situated in different places of Assam and some other general places where they are mostly found. Videos of 10 to 15 seconds duration were taken for two to three leaves of every species on a white background and video recording was done using a SLR Canon Camera. Individual videos were segregated into image frames and thus were able to get around 77,700 jpg image frames from the videos. The Raw leaf image set consists of folders with scientific name and common name within bracket. Second folder with title "Segmented leaf set using UNET segmentation" consists of 115 medicinal plant species with their segmented leaf image samples using UNET segmentation technique. Here two species are excluded from the original dataset due to small unpredictable size of the samples, so total 115 subfolders inside the segmented folder is achieved. Thirdly a table in doc format with title "Medicinal Plant Name Table" is uploaded and it includes Scientific name, Common name and Assamese name of the plants listed in the folders in the same sequence. The whole contribution is absolutely original and new, collected from different sources then processed for segmentation and prepared the table by discussing with taxonomy experts from Botany department of Gauhati University, Guwahati, Assam. India.

The datasets provide LEAF data and calculations for predicting environmental release of inorganic constituents of potential concern (COPCs) through leaching to groundwater and surface water bodies. Differences in environmental parameters such as pH and liquid solid ratio are included to help understand changes in the mobility of COPCs based on the environmental management conditions over time that will affect environmental release. This dataset is associated with the following publications: Garrabrants, A., D. Kosson, K. Brown, D. Fagnant, G. Helms, and S. Thorneloe. Demonstration of the Use of Test Results from the Leaching Environmental Assessment Framework (LEAF) to Develop Screening-Level Leaching Assessments. WASTE MANAGEMENT. Elsevier Science Ltd, New York, NY, USA, 121: 226-236, (2021). Garrabrants, A., D. Kosson, K. Brown, D. Fagnant, G. Helms, and S. Thorneloe. Methodology for Scenario-Based Assessments and Demonstration of Treatment Effectiveness using the Leaching Environmental Assessment Framework (LEAF). JOURNAL OF HAZARDOUS MATERIALS. Elsevier Science Ltd, New York, NY, USA, 406: 124635, (2021).

Dataset containing 9372 RGB images of weeds with the number of leaves counted. The images are collected in fields across Denmark using Nokia and Samsung cell phone cameras; Samsung, Nikon, Canon and Sony consumer cameras; and a Point Grey industrial camera.

A dataset of images containing leaves from 15 tree classes.

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.Using 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. Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.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 architectureThis 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

This global data set of photosynthetic rates and leaf nutrient traits was compiled from a comprehensive literature review. It includes estimates of Vcmax (maximum rate of carboxylation), Jmax (maximum rate of electron transport), leaf nitrogen content (N), leaf phosphorus content (P), and specific leaf area (SLA) data from both experimental and ambient field conditions, for a total of 325 species and treatment combinations. Both the original published Vcmax and Jmax values as well as estimates at standard temperature are reported. The maximum rate of carboxylation (Vcmax) and the maximum rate of electron transport (Jmax) are primary determinants of photosynthetic rates in plants, and modeled carbon fluxes are highly sensitive to these parameters. Previous studies have shown that Vcmax and Jmax correlate with leaf nitrogen across species and regions, and locally across species with leaf phosphorus and specific leaf area, yet no universal relationship suitable for global-scale models is currently available. These data are suitable for exploring the general relationships of Vcmax and Jmax with each other and with leaf N, P and SLA. This data set contains one *.csv file.

No description is available. Visit https://dataone.org/datasets/farshid25.57.1 for complete metadata about this dataset.

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 26, 2019.

Database of leaf senescence to collect SAGs, mutants, phenotypes and literature references. Leaf senescence has been recognized as the last phase of plant development, a highly ordered process regulated by genes called SAGs. By integrating the data from mutant studies and transgenic analysis, they collected many SAGs related to regulation of the leaf senescence in various species. Additionally, they have categorized SAGs according to their functions in regulation of leaf senescence and used standard criteria to describe senescence associated phenotypes for mutants. Users are welcome to submit the new SAGs.

The LT-Brazil data set contains observations of leaf mass per area, leaf N and P concentration per unit mass, and leaf N:P ratio from native woody species across the Brazilian territory, encompassing information of biome, vegetation, taxonomic data, geographical coordinates, climatic parameters, as well as soil properties. We compiled data from several geographical coordinates in native vegetation distributed across all biomes (i.e., Amazônia, Caatinga, Cerrado, Mata Atlântica, Pampa, and Pantanal) found in Brazil. Our compilation was focused on native woody plants (i.e., trees, shrubs, subshrubs, and lianas), excluding monocots, palm trees, herbs, and hemiparasitic plants. The compiled data set covers c. 9% of woody angiosperm species of Brazil. Unidentified or mixed species were also considered when met our eligibility criteria. Contributions to expand this database can be performed through our repository at GitHub (https://github.com/emariano-git/lt-brazil.git). Major versions of the LT-Brazil data set will also be made available via the TRY Plant Trait Database (https://www.try-db.org).

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

EWUBD: Pumpkin Leaf Dataset

## Overview

EWUBD: Pumpkin Leaf Dataset is a dataset for classification tasks - it contains Pumpkin Leaf Dataset annotations for 2,000 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).