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  Published Paper Details:

  Paper Title

Plant Leaf Disease Detection Using Image Processing

  Authors

  Onkar B Heddurshetti,  Hemalata R Zinage

  Keywords

Image processing, Segmentation, K-Mean clustering

  Abstract


Agricultural yield is something on which country's economy highly depends. This is one of the reason that plant disease detection is very important in agriculture field, as having plant diseases are quite common. If proper attention is not given in this area then it causes serious effects on plants and due to which respective product quality, quantity or productivity will reduce. Presently, the plant leaf disease can be detected physically by farmer by proper inspection. Farmers should go to the field and should identify leaves affected by the disease. Then we should find the solutions physically. This manual process of identifying plant leaf disease consumes more time and will give accuracy of about 60%. In the proposed system, a real time crop image capturing facility is provided and there is 99% accuracy in detection of disease. The information about the leaf disease can be sent to the farmers and climatic parameters such as moisture, PH, humidity, temperature etc. can be measured and monitored for the better growth of the plant. This process consumes less time to detect and gives 99% accuracy. This helps farmer to grow quality product.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2307631

  Paper ID - 241621

  Page Number(s) - f402-f406

  Pubished in - Volume 11 | Issue 7 | July 2023

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Onkar B Heddurshetti,  Hemalata R Zinage,   "Plant Leaf Disease Detection Using Image Processing", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.11, Issue 7, pp.f402-f406, July 2023, Available at :http://www.ijcrt.org/papers/IJCRT2307631.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


ISSN
ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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