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78 datasets found
  1. T

    plant_leaves

    • tensorflow.org
    Updated Dec 16, 2022
  2. m

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

    • data.mendeley.com
    Updated Jun 6, 2019
    + more versions
  3. R

    PlantDoc Object Detection Dataset

    • public.roboflow.com
    zip
    Updated Aug 8, 2023
    + more versions
  4. P

    New Plant Diseases Dataset Dataset

    • paperswithcode.com
    Updated Jun 28, 2019
    + more versions
  5. m

    Indian Medicinal Leaves Image Datasets

    • data.mendeley.com
    Updated May 5, 2023
    + more versions
  6. CotLeaf-1: Cotton Leaf Surface Images dataset 2019-21

    • data.csiro.au
    • researchdata.edu.au
    Updated Apr 15, 2024
  7. Plant images

    • kaggle.com
    zip
    Updated Oct 17, 2023
  8. R

    Cotton plant disease prediction Dataset

    • universe.roboflow.com
    zip
    Updated Nov 15, 2022
  9. m

    Sugarcane Leaf Image Dataset

    • data.mendeley.com
    Updated Jul 11, 2023
    + more versions
  10. m

    Indonesian Herb Leaf Dataset 3500

    • data.mendeley.com
    Updated Jan 27, 2022
    + more versions
  11. P

    A Dataset of Multispectral Potato Plants Images Dataset

    • paperswithcode.com
    Updated May 8, 2019
    + more versions
  12. i

    Data from: Naturalized Plants in Japan ~Seed-Image database~

    • integbio.jp
    Updated Jun 17, 2013
  13. Leaves Healthy or Diseased

    • kaggle.com
    Updated Jun 22, 2021
  14. f

    Plant RNA-Image Repository

    • figshare.com
    zip
    Updated Nov 19, 2023
    + more versions
  15. Maize whole plant image dataset

    • zenodo.org
    Updated Jan 24, 2020
  16. Leaves: India’s Most Famous Basil Plant Leaves Quality Dataset

    • ieee-dataport.org
    Updated Dec 22, 2020
  17. m

    An Image Dataset of Citrus Fruit and Leaves for Detection and Classification...

    • data.mendeley.com
    • narcis.nl
    Updated May 27, 2019
  18. D

    Multispectral Potato Plants Images Dataset

    • datasetninja.com
    Updated Oct 3, 2023
  19. Dragon Fruit Plant Image Dataset

    • ieee-dataport.org
    Updated May 2, 2024
  20. D

    Maize Whole Plant Image Dataset

    • datasetninja.com
    Updated Oct 5, 2017
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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

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16 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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