This dataset consists of 4502 images of healthy and unhealthy plant leaves divided into 22 categories by species and state of health. The images are in high resolution JPG format.

There are no files with label prefix 0000, therefore label encoding is shifted by one (e.g. file with label prefix 0001 gets encoded label 0).

Note: Each image is a separate download. Some might rarely fail, therefore make sure to restart if that happens. An exception will be raised in case one of the downloads repeatedly fails.

Dataset URL: https://data.mendeley.com/datasets/hb74ynkjcn/1 License: http://creativecommons.org/licenses/by/4.0

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('plant_leaves', 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_leaves-0.1.1.png" alt="Visualization" width="500px">

Dataset

This dataset was created by AdityaMalhotra1412

Contents

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.

Pl@ntNet-300K is an image dataset aimed at evaluating set-valued classification. It was built from the database of Pl@ntnet citizen observatory and consists of 306146 images, covering 1081 species. We highlight two particular features of the dataset, inherent to the way the images are acquired and to the intrinsic diversity of plants morphology:
i) The dataset exhibits a strong class imbalance, meaning that a few species represent most of the images.
ii) Many species are visually similar, making identification difficult even for the expert eye.
These two characteristics make the present dataset a good candidate for the evaluation of set-valued classification methods and algorithms. Therefore, we recommend two set-valued evaluation metrics associated with the dataset (top-K and average-K) and we provide the results of a baseline approach based on a resnet50 trained with a cross-entropy loss. The full description of the dataset can be found in (to be provided soon).

The scientific publication (NEURIPS 2022) describing the dataset and providing baseline results can be found here: https://openreview.net/forum?id=eLYinD0TtIt

Utilities to load the data and train models with pytorch can be found here: https://github.com/plantnet/PlantNet-300K/

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

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.

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.

A dataset of 61,486 images of plant leaves and backgrounds, with each image labeled with the disease or pest that is present. The dataset was created by researchers at the University of Wisconsin-Madison and is used for research in machine learning and computer vision tasks such as plant disease detection and pest identification.

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

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.

Dataset of images of dragon fruit plants, collected from different media and taken from a dragon fruit field in Rio Branco, Brazil, with a total of 600 images classified among 300 photos of sick plants, with fish eyes among others and 300 photos of healthy plants. For many of the photos, a simple smartphone camera was used to capture the images.

• 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.

This dataset contains the raw and processed images from a low-cost high-throughput plant phenotyping (HTP) system, as well as the raw and processed images that were manually acquired for comparison. The HTP images were automatically and wirelessly acquired for entire benches of plants with a system composed of a Raspberry Pi and eight GoPro cameras. The entire file system of each GoPro camera was copied directly into a subfolder of finalGoProImages (numbered by camera). The raw HTP images were processed by correcting for lens distortion, computing the "greenness index" for each individual pixel, and filtering out extreme high and low values. These processed HTP images were then saved in the "greenness" subfolder of finalGoProImages. The manually acquired images in the finalDSLR folder each represent an individual plant from one of five time points during the same greenhouse experiment. The raw manually acquired images were processed in the same manner as the raw HTP images by computing the greenness index for each individual pixel and filtering out extreme high and low values. The two tab-delimited text files include the number of green pixels and mean greenness index for each HTP (greennessGoProTable2.txt) and manually acquired (greennessDSLRTable2.txt) image.

Images and data for the study "Plant image identification application demonstrates high accuracy in Northern Europe"

Details: Jaak Pärtel, Meelis Pärtel, Jana Wäldchen, Plant image identification application demonstrates high accuracy in Northern Europe, AoB PLANTS, Volume 13, Issue 4, August 2021, plab050, https://doi.org/10.1093/aobpla/plab050

The data table displays Flora Incognita's identification results together with species and observations characteristics. All (3199) used images are included.

The study was conducted in two parts: database and field study.

Database study images have been taken from eBiodiversity database (https://elurikkus.ee/en) under Creative Commons Attribution 4.0 International (CC BY 4.0) licence (https://creativecommons.org/licenses/by/4.0/). Please cite the original source for the images as well when using the dataset.

Field study images were taken by Jaak Pärtel in 2020 in field conditions from different habitats across Estonia.

The Lentil Plant Disease Image Dataset is a meticulously collected, organized, augmented, and preprocessed collection of high-resolution images designed to support research in plant pathology and machine learning. This dataset includes images of lentil plants affected by three common diseases—Ascochyta Blight, Lentil Rust, and Powdery Mildew—as well as healthy plants.

Captured using a Samsung M31 smartphone, the images encompass various growth stages and lighting conditions, ensuring a comprehensive representation of symptoms. Data augmentation techniques, including rotation, width/height shift, shear, zoom, horizontal flipping, and brightness adjustment, have been applied to enhance the dataset's variability and robustness.

The dataset is divided into training, testing, and validation sets with an 80-10-10 split, and it is meticulously labeled and organized. This makes it an invaluable resource for researchers and practitioners in the fields of computer science, artificial intelligence, computer vision, machine learning, deep learning, and agriculture.

Dataset Card for "plant-images"

More Information needed

This dataset consists of high-resolution visible-spectrum (RGB) and thermal infrared (TIR) images of two vineyards (Vitis vinifera L.) with varieties of Mouhtaro and Merlot, which was captured by Unmanned Aerial Vehicle (UAV) carrying TIR and RGB sensors three times in a cultivation period. The RGB and TIR images are used initially for the vineyards' canopy isolation from the soil and the detection of the leaves' stomatal closure, considering the temperature values differences of the plant's canopy, to generate RGB images with pseudo-coloring of the stressed areas in vineyards' canopy. The dataset contains 1659 raw TIR images and 596 RGB images with pseudo-coloring where plants' stressed areas exist, aligned, and cropped based on the TIR images' Field of View (FOV).

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 potato crop is found at fourth as a major crop in the world, after rice, wheat, and maize. Nonetheless, in Ethiopia, the yield per unit area of potato is very low compared to other countries. There are a plethora of reasons, one of them is the disease. The major disease, which affects the major potato production area is Late Blight, according to researchers the disease caused 100% crop loss on the unimproved local cultivar and 67.1% on a susceptible variety.

Not to take early Late Blight disease management would destroy the whole farm within a few days. For decades many researchers have experimented on plant disease detection and classification using computer vision via different approaches and algorithms. Many researchers used traditional machine learning algorithms which require a handcrafted feature extraction to detect a given image. Besides, the data collected were under a laboratory setup which makes it less reliable while testing in real cultivation farms captured images of the potato.

Heterogeneous image datasets were collected from (Holeta, Ethiopia) potato farm with the help of two plant pathologists. The dataset correctly labeled with two classes as ‘Healthy’ and ‘Late Blight’, and the image has variety meaning some of the images captured with less noisy background image and others with a highly noisy environment. Under ‘Late Blight’ class 63 images were collected and under ‘Healthy’ class 363 images were collected. Finally, the prepared dataset could be used for different researches that aimed at plant disease detection and classification.