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-0.1.0.png" alt="Visualization" width="500px">

Overview

The PlantDoc dataset was originally published by researchers at the Indian Institute of Technology, and described in depth in their paper. One of the paper’s authors, Pratik Kayal, shared the object detection dataset available on GitHub.

PlantDoc is a dataset of 2,569 images across 13 plant species and 30 classes (diseased and healthy) for image classification and object detection. There are 8,851 labels. Read more about how the version available on Roboflow improves on the original version here.

And here's an example image:

https://i.imgur.com/fGlQ0kG.png" alt="Tomato Blight">

Fork this dataset (upper right hand corner) to receive the raw images, or (to save space) grab the 416x416 export.

Use Cases

As the researchers from IIT stated in their paper, “plant diseases alone cost the global economy around US$220 billion annually.” Training models to recognize plant diseases earlier dramatically increases yield potential.

The dataset also serves as a useful open dataset for benchmarks. The researchers trained both object detection models like MobileNet and Faster-RCNN and image classification models like VGG16, InceptionV3, and InceptionResnet V2.

The dataset is useful for advancing general agriculture computer vision tasks, whether that be health crop classification, plant disease classification, or plant disease objection.

Using this Dataset

This dataset follows Creative Commons 4.0 protocol. You may use it commercially without Liability, Trademark use, Patent use, or Warranty.

Provide the following citation for the original authors:

@misc{singh2019plantdoc,
  title={PlantDoc: A Dataset for Visual Plant Disease Detection},
  author={Davinder Singh and Naman Jain and Pranjali Jain and Pratik Kayal and Sudhakar Kumawat and Nipun Batra},
  year={2019},
  eprint={1911.10317},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

About Roboflow

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Developers reduce 50% of their code when using Roboflow's workflow, automate annotation quality assurance, save training time, and increase model reproducibility.

Plant disease is a deviation from the normal state of a plant that disrupts or alters its vital functions. Plant diseases can lead to significant yield losses, with estimated global potential losses of up to 16%. As a result, studying plant diseases and developing methods to diagnose and control them is an essential area of research in plant pathology.

The proper identification of plant diseases is crucial for effective control measures, as without them, control efforts can be ineffective and a waste of resources. Image processing algorithms have been developed to detect plant diseases by analyzing the color features of the infected leaves. One such algorithm involves using the K-means method for color segmentation and the Gray-Level Co-Occurrence Matrix (GLCM) for disease classification. This method of vision-based plant disease detection has shown promising results and has the potential to be an efficient and effective tool for disease diagnosis.

To understand the relationship between plant diseases and yield loss, it is necessary to consider the epidemiology of the disease, the physiology of the crop, the yield development, the damage mechanisms involved, and the effect of management practices. By integrating this information, it is possible to improve our understanding of the relationship between plant diseases and crop loss. However, it is important to note that yield loss studies are resource-intensive and can be difficult to interpret, as crops are rarely affected by only one pest or pathogen at a time.

In conclusion, the detection of plant diseases is an important aspect of agriculture, as it is essential for effective disease control and management. Image processing algorithms have shown promising results in the detecdetectingseases, and the integration of various aspects of plant physiology, disease epidemiology, and management practices can help increase our understanding of the relationship between plant diseases and crop loss. The goal of plant pathology research is to reduce yield losses and develop integrated pest management strategies based on economic thresholds, which can be achieved through a better understanding of the relationship between plant diseases and crop loss.

Indian Medicinal plant datasets is a repository that consists of medicinal plants images. The images are captured with varying background without any environment constraints

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.

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.

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.

This dataset is a collection of 13,597 images of cotton leaf surfaces acquired with a hand-held microscope to develop deep learning models to classify images based on leaf hairiness and assist Cotton breeders in their variety selection efforts.

These images were collected from 27 genotypes grown across 2 seasons (2019-2020 and 2020-2021), 2 sites (Australian Cotton Research Institute, -30.21, 149.60, Narrabri, NSW, Australia and CSIRO Black Mountain Laboratories, -35.27, 149.11, Canberra, Australian Capital Territory, Australia) and two growth conditions (Field and Glasshouse). Genotypes have been anonymized to protect germplasm Intellectual Property.

Note 1: This dataset was released with our HairNet paper (Rolland et al 2022, see link below). At the time of publishing Rolland, V., Farazi, M.R., Conaty, W.C. et al. HairNet: a deep learning model to score leaf hairiness, a key phenotype for cotton fibre yield, value and insect resistance. Plant Methods 18, 8 (2022). https://doi.org/10.1186/s13007-021-00820-8, this dataset was called 'Cotton leaf surface image dataset to build deep learning models for leaf hairiness trait (2019-2021)'. It has since being renamed 'CotLeaf-1: Cotton Leaf Surface Images dataset 2019-21'.

Note 2: if you intend to use this dataset in conjunction with CotLeaf-2, CotLeaf-X or AnnCoT datasets, then use the CotLeaf-1 Json file attached to the CotLeaf-2 collection (see link below).

See below for related Datasets and Publications.

Lineage: Genotype Selection: A total of 27 Gossypium hirsutum Cotton genotypes were selected based on their known leaf hairiness. Genotypes were anonymised to protect germplasm intellectual property. Various combinations of these genotypes were grown at two different Australian sites (Narrabri, New South Wales & Canberra, Australian Capital Territory), in the field or controlled glasshouse environment, and over two years (2019-2020 and 2020-2021). For details refer to Rolland et al 2021 (link attached to this submission).

Field experiments - Narrabri Seed of selected genotype were planted on Oct. 21 2019 and Nov. 6 2020, at planting density of 10 - 12 plants m-2 in rows spaced at 1 m. Each genotype was grown in a single 13 m row. The soil of the site is a uniform grey cracking clay. Nitrogen was applied as anhydrous ammonia approximately 12 weeks before planting at a rate of 200 kg N ha-1. Plants were furrow irrigated every 10 to 14 d (approximately 1 ML ha-1 applied at each irrigation) from December through to March, according to crop requirements. Each experiment was managed according to its individual requirements for irrigation and pest control, with all plots receiving the same management regime.

Glasshouse experiments - Narrabri Plants were grown in temperature-controlled glasshouses. About 15 seeds of each genotype were sown in 8 L plastic pots filled with soil on Sept. 6 2019 and Nov. 2 2020, respectively. The soil was obtained from cotton fields as above. To improve the nutrient status of the potting mix 10 g of MULTIgro® basal fertiliser was dissolved into the soil before planting. A 10 mm layer of sand was added to the surface of the pots to reduce surface evaporation and assist in seedling emergence. Once emerged seedlings had reached the three-leaf stage, pots were thinned down to two plants per pot. Plants were grown at 18 °C night and 32 °C during the day, under natural light conditions.

Glasshouse experiment - Canberra Plants were grown in temperature-controlled glasshouses. Eight seeds of selected genotypes were sown in 5 L plastic pots filled with potting mix on Nov. 30 2020. The pots were filled with a 60:40 compost:perlite soil mix. Osmocote® Exact Standard 3-4M was sprinkled on the top layer of soil before flowering. Two weeks after sowing, pots were thinned down to two plants per pot. Plants were grown at 18 °C night and 28 °C during the day, under natural light conditions.

Leaf selection and harvesting: Leaves were numbered in ascending number from the tip of the main stem, with the first fully opened leaf called leaf one. Leaves 3 and 4 from ten individual plants were harvested by cutting their petiole in a proximal position. Harvested leaves were placed in paper bags and imaged within the same day. In the 2019-2020 glasshouse experiment, a few plants died or had a missing leaf, in which case there may be genotypes for which leaves 3 and 4 were harvested from less than 10 plants.

Leaf imaging: Single leaves were imaged at a magnification of about 31x with a portable AM73915 Dino-lite Edge 3.0 microscope equipped with a RK-04F folding manual stage and connected to a digital tablet running DinoCapture 2.0. Images were captured on the abaxial side of the leaf, along the 3 central mid-veins. An average of 3 to 5 images were captured in a proximal to distal fashion along each one of the 3 mid-veins, yielding a total of about 9 to 15 images per leaf. The exact angle of the mid-vein in each image was not fixed. However, either end of the mid-vein was always cut by the left and right borders of the field of view, and never by the top and bottom ones.

Context

Of the three major crops – rice, wheat and maize – rice is by far the most important food crop for people in low- and lower-middle-income countries. Although rich and poor people alike eat rice in low-income countries, the poorest people consume relatively little wheat and are therefore deeply affected by the cost and availability of rice.

In many Asian countries, rice is the fundamental and generally irreplaceable staple, especially of the poor. For the extreme poor in Asia, who live on less than $1.25 a day, rice accounts for nearly half of their food expenditures and a fifth of total household expenditures, on average. This group alone annually spends the equivalent of $62 billion (purchasing power parity) on rice. Rice is critical to food security for many of the world’s poor people.

~ Quote from ricepedia.org

Content

This dataset contains 120 jpg images of disease infected rice leaves. The images are grouped into 3 classes based on the type of disease. There are 40 images in each class.

Classes

• Leaf smut
• Brown spot
• Bacterial leaf blight

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1086574%2F440c2d8d39025fc8be9929836686cbc1%2Frice_leaves.png?generation=1582347404740337&alt=media" alt="">

This dataset is associated with the following paper: Detection and Classification of Rice Plant Diseases

The authors gathered these leaves from a rice field in a village called Shertha in Gujarat, India.

Citation

Prajapati HB, Shah JP, Dabhi VK. Detection and classification of rice plant diseases. Intelligent Decision Technologies. 2017 Jan 1;11(3):357-73, doi: 10.3233/IDT-170301.

UCI Machine Learning Repository https://archive.ics.uci.edu/ml/datasets/Rice+Leaf+Diseases#

Acknowledgements

Many thanks to the research team at the Department of Information Technology, Dharmsinh Desai University for making this dataset publicly available.

Inspiration

• Build a dataset like this that includes more types of rice leaf diseases. Collect samples of both healthy and disease infected rice leaves from a farming community. Label the dataset using information from local farmers or from plant pathologists.
• Build a model to automatically classify rice leaf diseases.
• Deploy your model as a Tensorflow.js web app so it can be accessed from anywhere in the world.
• Plantix is an excellent example of an impactful agtech mobile app. This video has more info.

Header image by HoangTuan_photography on Pixabay.

Image datasets play a crucial role across diverse fields, including computer vision, machine learning, medical research, and social sciences. These datasets serve as a valuable resource, providing rich visual information that enables researchers, developers, and professionals to train and validate their models, algorithms, and theories. In the agricultural domain, a specific image dataset focused on sugarcane leaf diseases holds significant importance. Such datasets offer researchers, agronomists, and farmers a valuable tool to identify, classify, and study various leaf diseases affecting sugarcane crops. By analyzing these images, experts can develop more accurate disease detection algorithms and early warning systems, facilitating prompt disease management and preventing widespread crop damage and yield loss. Additionally, a comprehensive dataset allows for the exploration of disease patterns, environmental factors, and potential mitigation strategies, thereby advancing research and improving overall crop management practices to ensure the health and productivity of sugarcane crops. The Sugarcane Leaf Dataset consists of 7134 high-resolution images of sugarcane leaves stored in JPEG format, with dimensions of 768 × 1024 pixels. Categorized into 12 distinct classes, including 10 disease categories, a healthy leaves category, and a dried leaves category, the dataset covers a wide range of common sugarcane leaf diseases, ensuring easy access and identification of specific disease samples. These images were collected through extensive field surveys, capturing different angles and stages of the diseases and guaranteeing a comprehensive representation of visual characteristics. With their high-quality resolution of 72 dots per inch (dpi), the images in the dataset provide clear and detailed visual representation of the sugarcane leaf samples.

Here are a few use cases for this project:

1. Precision Agriculture: Farmers and agricultural specialists can use the Cotton Plant Disease Prediction model to analyze images of their cotton plants, identify diseases in real-time, and optimize the use of pesticides, fungicides, and other treatments to boost crop health and increase yields.

2. Smart Greenhouses: Integrate the model into smart greenhouse systems, enabling automated monitoring of cotton plants throughout their growth cycles. By identifying diseases and stress factors early, greenhouse managers can address issues promptly, improving plant health and overall productivity.

3. Agricultural Research: Scientists and researchers studying cotton diseases and agronomy can utilize this model to quickly analyze large volumes of images, identify trends, and test the effectiveness of various treatment methods.

4. Educational Resources: Educators in agricultural and botanical fields can use the Cotton Plant Disease Prediction model to create interactive learning materials, helping students better understand cotton diseases, their symptoms, and treatment options.

5. Plant Disease Early Warning Systems: By integrating the model with satellite or aerial images, governments and agricultural organizations can quickly assess the prevalence of diseases in large-scale cotton fields, providing early alerts to farmers and initiating targeted interventions to prevent the spread of diseases and minimize crop loss.

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.

Together with the Agriculture University, we compiled a database of plant images and omics data. The dataset contains images of four distinct plant maladies, including powdery mildew, rust, leaf spot, and blight, as well as gene expression and metabolite data. Using a high resolution camera in a controlled environment at the facility of the Agriculture University of Peshawar, we captured 8,000 images of plants, with 2,000 images for each disease type. Each image was labeled with the disease type corresponding to it. The images were preprocessed by resizing them to 224x224 pixels and standardizing the pixel values. The dataset was divided into 70:15:15 training, validation, and testing sets, correspondingly. In addition to collecting images of the same plants, we also collected gene expression and metabolite data. We extracted RNA from the plant leaves using a commercial reagent and sequenced it on an Illumina HiSeq 4000 platform. The average length of the 100 million paired-end readings obtained was 150 base pairs. The unprocessed reads were trimmed with Trimmomatic and aligned with STAR against the reference genome. We counted the number of reads that mapped to each gene using featureCounts, and then identified differentially expressed genes between healthy and diseased plants using the DESeq2 package in R. Using gas chromatography-mass spectrometry (GC-MS), we gathered additional metabolite information. Using a methanol-water extraction protocol, we extracted metabolites from the plant leaves and analyzed the extracts using GC-MS. We obtained 500 metabolite characteristics, including amino acids, organic acids, and sugars.If you use the dataset mentioned here, please make sure to give credit to the researchers by citing their paper titled 'Deep Learning for Plant Bioinformatics: An Explainable Gradient-Based Approach for Disease Detection.'ReferenceShoaib, M., Shah, B., Sayed, N., Ali, F., Ullah, R., & Hussain, I. (2023). Deep learning for plant bioinformatics: an explainable gradient-based approach for disease detection. Frontiers in Plant Science, 14(October), 1–17. https://doi.org/10.3389/fpls.2023.1283235

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.

✔️ ✔️ Cite: H. Sharma, D. Padha and N. Bashir, "D-KAP: A Deep Learning-based Kashmiri Apple Plant Disease Prediction Framework," 2022 Seventh International Conference on Parallel, Distributed and Grid Computing (PDGC), Solan, Himachal Pradesh, India, 2022, pp. 576-581, doi: 10.1109/PDGC56933.2022.10053334.

😄 We have considered three commonly found diseases viz. apple scab, apple rot and alternaria leaf blotch only. Most of the data is collected from the orchards of Kashmir valley for educational and research purpose. We collected around 419 images of infected and healthy leaves. Most of the data collection is done during the months of May, June and July when maximum number of diseases are present on plants. All the images were captured manually using digital camera and mobile phones of different brands. All the image samples have been put into distinct labeled folder.😃

This dataset contains materials to reproduce Figure 5 that shows plant representations at different development stages (one to eight weeks after sowing) for top (a) and (b) side images, together with time courses of the number of pixels corresponding to plants extracted from side and top views (c). The following materials are available:

1. `Image dataset`: raw image dataset of side and top RGB images of a single plant that can be used in the segmentation pipeline (https://github.com/openalea/eartrack)

2. `Segmented image dataset`: output images of the segmentation pipeline

3. `Image analysis features`: A csv file contatining all image analysis features from the image dataset provided above

4. 'FIG5 dataset': small dataset of segmented images for building Figure 5a,b

(1) In agriculture, plant diseases are primarily responsible for the reduction in production which causes economic losses. In plants, citrus is used as a major source of nutrients like vitamin C throughout the world. However, ‘Citrus’ diseases badly effect the production and quality of citrus fruits.

(2) The computer vision and image processing techniques have been widely used for detection and classification of diseases in plants.

(3) The dataset contains an image gallery of healthy and unhealthy citrus fruits and leaves that could be usable for the researchers to prevent plants from diseases using advanced computer vision techniques. The disease targeted in the data sets are the Blackspot, Canker, Scab, Greening, and Melanose.

(4) The dataset contains 759 images of healthy and unhealthy images for both Citrus fruits and leaves collectively. Each image contains 256 * 25 dimensions with 72 dpi resolution.

(5) All images were acquired from the Sargodha region, a tropical area of Pakistan under the supervision of Dr. Basharat ALi Saleem, Endeavour Executive Fellow Curtin University · Horticulture Research Laboratory Postharvest Australia · Bentley

(6) All images were annotated manually by the domain expert Dr. Basharat ALi Saleem to represent their every class such as : For Citrus fruits (Black Spot, Canker, Greening, Scab, and healthy with total number of 150 images ), For Citrus Leaves (Black Spot, Canker, Greening, Melanose, and healthy with total number of 609 image)

(6) Further details can be found in the associated publications with the dataset.

The Multispectral Potato Plants Images dataset contains aerial images of potato crops and can be utilized for training machine learning models for crop health assessment in precision agriculture applications. It comprises 360 RGB image patches of size 750×750 pixels in JPG format, divided into a train subset of 300 images and a test subset of 60 images. These image patches were extracted from the aerial high-resolution images through cropping, rotating, and resizing operations.

Context

Med Herb Lens is a non-profit project that utilises Artificial Intelligence (Deep Learning - Tensorflow) to assist medicinal herbalists and traditional healers and physicians in automatically recognizing and classifying medicinal plants.

Content

The images are manually captured by mobile devices. Each category/herbal plant in the dataset is named using a numerical ID. You can find the English, Vietnamese, and Scientific names in this Google Sheet.

At the moment, there are only 7+ classes with just around 100 images each. More plants and images will be added soon at a later date when we have more data.

Acknowledgements

This dataset is contributed by the herbal plant lovers community. You can help contribute to our dataset as well by downloading our official Android app at https://play.google.com/store/apps/details?id=com.uri.lee.dl, and start uploading your own images from there. Thank you for your support!

For any issue with this dataset, please send an email to admin@medherblens.ml