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

Published: 6 Jun 2019 | Version 1 | DOI: 10.17632/hb74ynkjcn.1
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Description of this data

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.

Experiment data files

  • Alstonia Scholaris (P2)
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    • diseased
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    • healthy
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  • Arjun (P1)
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    • diseased
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    • healthy
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  • Bael (P4)
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    • diseased
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  • Basil (P8)
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    • healthy
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  • Chinar (P11)
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    • diseased
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    • healthy
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  • Gauva (P3)
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    • diseased
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    • healthy
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  • Jamun (P5)
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    • diseased
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    • healthy
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  • Jatropha (P6)
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    • diseased
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    • healthy
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  • Lemon (P10)
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    • diseased
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    • healthy
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  • Mango (P0)
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    • diseased
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    • healthy
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  • Pomegranate (P9)
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    • diseased
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    • healthy
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  • Pongamia Pinnata (P7)
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    • diseased
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    • healthy
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Latest version

  • Version 1

    2019-06-06

    Published: 2019-06-06

    DOI: 10.17632/hb74ynkjcn.1

    Cite this dataset

    Chouhan, Siddharth Singh; Kaul, Ajay; Singh, Uday Pratap; Jain, Sanjeev (2019), “A Database of Leaf Images: Practice towards Plant Conservation with Plant Pathology”, Mendeley Data, v1 http://dx.doi.org/10.17632/hb74ynkjcn.1

Statistics

Views: 15591
Downloads: 8929

Institutions

Shri Mata Vaishno Devi University

Categories

Pathology, Agricultural Plant, Leaf Area, Leaf Studies, Plantation

Licence

CC BY 4.0 Learn more

The files associated with this dataset are licensed under a Creative Commons Attribution 4.0 International licence.

What does this mean?
You can share, copy and modify this dataset so long as you give appropriate credit, provide a link to the CC BY license, and indicate if changes were made, but you may not do so in a way that suggests the rights holder has endorsed you or your use of the dataset. Note that further permission may be required for any content within the dataset that is identified as belonging to a third party.

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