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100+ datasets found
  1. T


    Updated May 18, 2021
  2. m

    A Database of Leaf Images: Practice towards Plant Conservation with Plant...

    Updated Jun 6, 2019
  3. z

    Maize whole plant image dataset

    Updated Oct 5, 2017
  4. New Plant Diseases Dataset

    Updated Nov 18, 2018
  5. f

    Power Plant Satellite Imagery Dataset

    Updated Aug 16, 2017
  6. m

    PlantaeK: A leaf database of native plants of Jammu and Kashmir

    • +1more
    Updated Sep 5, 2019
  7. m

    Data from: A Citrus Fruits and Leaves Dataset for Detection and...

    Updated May 28, 2019
  8. Poisonous plants images

    Updated Feb 10, 2019
  9. m

    Dataset of Tomato Leaves

    Updated May 27, 2020
  10. d

    Lunar Plants

    • +1more
    csv, zip
    Updated Feb 15, 2021
  11. r

    Australian Plant Image Index

    Updated Oct 21, 2020
  12. m

    Medicinal Leaf Dataset

    • +1more
    Updated Oct 22, 2020
  13. Plant Pathology 2020 - Preprocessed Images

    Updated May 18, 2020
  14. z

    PlantVillage Disease Classification Challenge - Color Images

    Updated Mar 21, 2018
  15. Aberystwyth Leaf Evaluation Dataset

    • +1more
    pdf, zip
    Updated Nov 23, 2016
  16. Naturalized Plants in Japan ~Seed-Image database~

    Updated Jun 17, 2013
  17. i

    IAP example data set 1107BA_Corn_JPEG

    Updated Apr 12, 2016
  18. Plant semantic segmentation

    Updated Apr 28, 2021
  19. i

    Aerial Imagery of PV Plants for boundary detection

    Updated May 30, 2020
  20. d

    Image data of Nanmaohu Park vascular plant specimen, RL0050

    Updated Jan 25, 2019
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Siddharth Singh CHOUHAN (2019). A Database of Leaf Images: Practice towards Plant Conservation with Plant Pathology [Dataset].

A Database of Leaf Images: Practice towards Plant Conservation with Plant Pathology

2 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated Jun 6, 2019
Dataset provided by
Mendeley Data
Siddharth Singh CHOUHAN

Attribution 4.0 (CC BY 4.0)
License information was derived automatically


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.

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