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100+ datasets found
  1. T

    plant_leaves

    • www.tensorflow.org
    • tensorflow.google.cn
    Updated Nov 1, 2019
  2. m

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

    • data.mendeley.com
    Updated Jun 6, 2019
  3. New Plant Diseases Dataset

    • www.kaggle.com
    zip
    Updated Nov 18, 2018
  4. z

    Maize whole plant image dataset

    • zenodo.org
    Updated Oct 5, 2017
  5. m

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

    • data.mendeley.com
    • www.narcis.nl
    • +1more
    Updated Sep 5, 2019
  6. PlantVillage Dataset

    • www.kaggle.com
    • www.tensorflow.org
    • +2more
    zip
    Updated Oct 30, 2018
  7. i

    Leaves: India’s Most Famous Basil Plant Leaves Quality Dataset

    • ieee-dataport.org
    • search.datacite.org
    Updated Dec 22, 2020
  8. Plant segmentation

    • www.kaggle.com
    zip
    Updated May 27, 2019
  9. P

    A Dataset of Multispectral Potato Plants Images Dataset

    • paperswithcode.com
    Updated Jun 13, 2021
  10. PlantVillage Dataset

    • www.kaggle.com
    zip
    Updated Sep 1, 2019
  11. Power Plant Satellite Imagery Dataset

    • figshare.com
    pdf
    Updated Aug 16, 2017
  12. R

    PlantDoc Object Detection Dataset

    • public.roboflow.com
    zip
    Updated Mar 2, 2020
  13. P

    Plant Seedlings Dataset Dataset

    • paperswithcode.com
    Updated Feb 17, 2021
  14. Aberystwyth Leaf Evaluation Dataset

    • zenodo.org
    • explore.openaire.eu
    • +1more
    pdf, zip
    Updated Nov 23, 2016
  15. m

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

    • data.mendeley.com
    Updated May 28, 2019
  16. Hops Classification

    • www.kaggle.com
    zip
    Updated May 5, 2020
  17. AI validated plant observations from social media: Flickr images from...

    • zenodo.org
    • explore.openaire.eu
    • +1more
    csv, txt
    Updated Oct 21, 2019
  18. Collection of Different Category of Leaf Images

    • www.kaggle.com
    zip
    Updated Mar 4, 2022
  19. d

    Image data of Nanmaohu Park vascular plant specimen, RL0050

    • doi.org
    • gigadb.org
    • +2more
    Updated Jan 25, 2019
  20. PlantifyDr Dataset

    • www.kaggle.com
    zip
    Updated Feb 16, 2021
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Siddharth Singh CHOUHAN (2019). A Database of Leaf Images: Practice towards Plant Conservation with Plant Pathology [Dataset]. http://doi.org/10.17632/hb74ynkjcn.1

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

4 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Jun 6, 2019
Authors
Siddharth Singh CHOUHAN
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

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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