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LEVERAGING ARTIFICIAL INTELLIGENCE FOR EARLY DETECTION OF PLANT DISEASES

Teesha Ahuja

11-15

Vol 16, Jul-Dec, 2022

Date of Submission: 2022-07-28 Date of Acceptance: 2022-09-13 Date of Publication: 2022-09-28

Abstract

We are all aware that India is the most important agricultural country in the world. A wide variety of crops are grown there, and frequently, these crops are destroyed by diseases that settle on the leaves of the plants. To address this problem, we have proposed a model that will detect diseases on the leaves of plants and spray the necessary medication for them.

References

  1. Kulkarni, Anand & K, Ashwin. (2012). Applying image processing technique to detect plant diseases. International Journal of Modern Engineering Research (IJMER) 2. 3661-3664.
  2. Vishnu S, A. Ranjith Ram (2015), Plant Disease Detection Using Leaf Pattern: A Review, IJISET - International Journal of Innovative Science, Engineering & Technology, Vol. 2 Issue 6, June 2015.
  3. Pawan P. Warne, Dr S. R. Ganorkar (2015), Detection of Diseases on Cotton Leaves Using K Mean Clustering Method, International Research Journal of Engineering and Technology (IRJET), Vol 2, Issue 4.
  4. Pranjali. B. Padol, Anjali A. Yadav (2016), SVM Classifier based Grape Leaf Disease Detection, 2016 Conference on Advances in Signal Processing (CASP), doi: 10.1109/casp.2016.7746160
  5. N. Krithika, Dr. A. Grace Selvarani (2017), An Individual Grape Leaf Disease Identification Using Leaf Skeletons and KNN Classification, 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), doi: 10.1109/ICIIECS.2017.8275951
  6. Jianping Fan, Ning Zhou, Jinye Peng, Ling Gao., (2015), Hierarchical Learning of Tree Classifiers for Large-Scale Plant Species Identification. IEEE Transactions on Image Processing, Doi: 10.1109/ICOSC.2015.7050838
  7. Muammwe Turkoglu, D. Hanbay, (2019), Plant Recognition system based on Deep Features and Color-LBP method, 2019 27th Signal Processing and Communications Applications Conference (SIU) doi: DOI:10.1109/SIU.2019.8806592
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