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">

This dataset is recreated using offline augmentation from the original dataset. The original dataset can be found on this github repo. This dataset consists of about 87K rgb images of healthy and diseased crop leaves which is categorized into 38 different classes. The total dataset is divided into 80/20 ratio of training and validation set preserving the directory structure. A new directory containing 33 test images is created later for prediction purpose.

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

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.

Labeled House plant images suitable for training and evaluating computer vision and deep learning models.

Indoor Plant Disease Dataset

## Overview

Indoor Plant Disease Dataset is a dataset for classification tasks - it contains Objects annotations for 758 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 authors of the DiaMOS Plant: A Dataset for Diagnosis and Monitoring Plant Disease contribute to the evolving field of foliar disease classification and recognition through the utilization of machine and deep learning concepts. It has 3505 images of pear fruit and leaves affected by four diseases: slug leaf, spot leaf, curl leaf, and healthy leaf. The study offers valuable guidelines for the research community to select and construct further datasets.

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).

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

DOI:: @misc{shuvo_kumar_basak_2025, title={Chili Plant Dataset HUSD BD}, url={https://www.kaggle.com/dsv/13490739}, DOI={10.34740/KAGGLE/DSV/13490739}, publisher={Kaggle}, author={Shuvo Kumar Basak}, year={2025} } Shuvo Kumar Basak. (2025). Chili Plant Dataset HUSD BD [Data set]. Kaggle. https://doi.org/10.34740/KAGGLE/DSV/13490739 https://www.kaggle.com/datasets/shuvokumarbasak4004/chili-plant-dataset-husd-bd/data

Other & More ::

https://www.kaggle.com/datasets/shuvokumarbasak4004/chili-plant-disease-detection

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15768793%2F8110edcfd0ec890bce93518d1cb0b0c0%2Fresults.png?generation=1761330604040633&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15768793%2F53f3e3afe28fece31ff8b0e3298e6311%2FScreenshot%20(237).png?generation=1761329936302562&alt=media" alt=""> ![https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15768793%2F1e1e4fa347ba688144bdb783688add86%2FScreenshot%20(239).png?generation=1761330674283520&alt=media" alt="">] Dataset Name: Chili Plant Dataset HUSD BD Total Classes: 4 Image Size: 640 × 640 Labeling Tool: Roboflow Supported Models: YOLOv8, YOLOv9, YOLOv11, YOLOv12,TensorFlow,Multiclass Object Detection Type: 6

The full form of HUSD (Healthy, Unhealthy, Seed, Dry). Annotated using Roboflow for high-precision bounding boxes. The Chili Plant Dataset (HUSD BD) is a newly collected agricultural image dataset focused on chili plants. It is designed to support deep learning and computer vision applications such as plant health analysis, disease detection, and growth monitoring. 🧩 Technical Details

Format: Multiclass object detection

Optimized for: YOLO and TensorFlow training pipelines

Suitable for: Transfer learning, image classification, and agricultural AI research

🎯 Applications

Chili plant disease detection

Crop health monitoring

Smart agriculture and AI-based farming systems

Academic and research projects in deep learning and agriculture.

**More Dataset:: **

https://www.kaggle.com/shuvokumarbasak4004

https://www.kaggle.com/shuvokumarbasak2030

…………………………………..Note for Researchers Using the dataset………………………………………………………………………

This dataset was created by Shuvo Kumar Basak. If you use this dataset for your research or academic purposes, please ensure to cite this dataset appropriately. If you have published your research using this dataset, please share a link to your paper. Good Luck.

[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.

The ‘Rice Plant Image Dataset’ is a high quality RGB image dataset (in .jpg format) of the rice (Oryza sativa) plant of Bangladesh. Rice is the staple crop of the Asia-Pacific region, so its production is crucial. The collection includes a variety of rice plant and crop properties such as form, color, texture, and physiological aspects. This dataset is a valuable resource for researchers and developers working on computer vision and agriculture. It can be used to train and test algorithms— image classification, segmentation, and object detection— along with crop monitoring, yield prediction, disease detection, and precision agriculture. Students and farmers can also use it as an educational resource.

Plant species cover-abundance and presence observed in multi-scale plots. Plant species and associated percent cover in 1m2 subplots and plant species presence in 10m2 and 100m2 subplots are reported from 400m2 plots. Archived plant vouchers and foliar tissue support the data and additional analyses.

HIGH LIGHT

The dataset was first introduced and used in VPGS: Enhanced 3D Gaussian Splatting for Accurate Virtual Plant Reconstruction and Rendering

  Dataset Description

Since there is currently no dedicated dataset for plant 3D reconstruction, we have specifically collected plant scenes from other publicly available 3D datasets and supplemented them based on the standards of these datasets. This dataset is designed for 3D reconstruction using 3D GS or NeRF. It consists of… See the full description on the dataset page: https://huggingface.co/datasets/nowornever/Plant.

Plant stress identification dataset comprising RGB and thermal images of five tropical fruit plants Custard apple, Guava, Mango, Lemon, and Sapodilla. Useful for researchers working in the field of plant stress analysis to develop deep learning based models for the plant stress classification task.

A high-quality dataset focused on waste recycling plants, useful for AI applications such as automated waste sorting, environmental monitoring, object detection, and industrial automation. Created using GTS.AI’s compliant data collection, diverse demographic sampling, and multilayer quality control workflows.

This ornamental plant dataset consists of around 11K training data and 2.9K validation data, consisting of 29 different class categories. The training data consists of 400 images in each category, and the validation data consists of 100 images in each. The image size is 240x240 pixels

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

ZZ Plant

## Overview

ZZ Plant is a dataset for classification tasks - it contains Plants FqMy annotations for 432 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).