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Journal of Intelligent Systems and Internet of Things

Volume 17 , Issue 1 , PP: 39-56, 2025 | Cite this article as | XML | Html | PDF | Full Length Article

Multi-Disease Recognition in Tea Plants By Evaluating the Performance of Yolo Models

Malathi K. 1 * , Mohanasundaram N. 2 , Santhosh R. 3 , Manikandan B. 4 , Parthasarathy V. 5 , Saravana Kumar G. 6

  • 1 Research Scholar, Department of Computer Science and Engineering, Karpagam Academy of Higher Education, Coimbatore, India - (itismemohan@gmail.com)
  • 2 Professor, Department of Computer Science and Engineering, Karpagam Academy of Higher Education, Coimbatore, India - (maluvijic@gmail.com)
  • 3 Professor, Department of Computer Science and Engineering, Karpagam Academy of Higher Education, Coimbatore, India - (santhoshrd@gmail.com)
  • 4 Professor, Department of Computer Science and Engineering, Karpagam Academy of Higher Education, Coimbatore, India - (reserchwork411@gmail.com)
  • 5 Professor, Department of Computer Science and Engineering, Karpagam Academy of Higher Education, Coimbatore, India - (karpagam.publication@gmail.com)
  • 6 Professor, Department of Computer Science and Engineering, Karpagam Academy of Higher Education, Coimbatore, India - (csrd411@gmail.com)

    • Doi: https://doi.org/10.54216/JISIoT.170104

    Received: December 10, 2024 Revised: February 01, 2025 Accepted: March 10, 2025
    Abstract

    This work explores the innovative application of integrated pest management (IPM) strategies in the control of the Tea Looper Caterpillar and the Tea Leaf Hopper, utilizing the YOLO algorithm for real time pest detection. IPM is essential for sustainable agriculture, aiming to reduce chemical pesticide usage through a combination of biological, cultural, and technological methods. The combination of artificial intelligence and machine learning into IPM practices has shown promising results, particularly in identifying and monitoring pest populations in tea plantations. This study reviews existing literature on the impact of various pests on tea crops and highlights the significance of using advanced algorithms for effective pest management. Notably, the implementation of the YOLO algorithm demonstrated an impressive accuracy rate of 97% in detecting these pests, displaying its potential to enhance pest control efforts. By focusing on the tea green leafhopper and looper caterpillars, the research aims to provide insights into sustainable pest control methods that minimize environmental impact. The findings underscore the potential of AI-driven technologies in enhancing agricultural productivity while promoting ecological balance. This project ultimately contributes to the ongoing discourse on sustainable agricultural practices and the role of technology in addressing pest-related challenges in tea cultivation.

    Keywords :

    Buzura suppressaria , Jaasid , YOLO , Empoasca flavescens , Guen , Hyposidra talaca

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    Cite This Article As :
    K., Malathi. , N., Mohanasundaram. , R., Santhosh. , B., Manikandan. , V., Parthasarathy. , Kumar, Saravana. Multi-Disease Recognition in Tea Plants By Evaluating the Performance of Yolo Models. Journal of Intelligent Systems and Internet of Things, vol. , no. , 2025, pp. 39-56. DOI: https://doi.org/10.54216/JISIoT.170104
    K., M. N., M. R., S. B., M. V., P. Kumar, S. (2025). Multi-Disease Recognition in Tea Plants By Evaluating the Performance of Yolo Models. Journal of Intelligent Systems and Internet of Things, (), 39-56. DOI: https://doi.org/10.54216/JISIoT.170104
    K., Malathi. N., Mohanasundaram. R., Santhosh. B., Manikandan. V., Parthasarathy. Kumar, Saravana. Multi-Disease Recognition in Tea Plants By Evaluating the Performance of Yolo Models. Journal of Intelligent Systems and Internet of Things , no. (2025): 39-56. DOI: https://doi.org/10.54216/JISIoT.170104
    K., M. , N., M. , R., S. , B., M. , V., P. , Kumar, S. (2025) . Multi-Disease Recognition in Tea Plants By Evaluating the Performance of Yolo Models. Journal of Intelligent Systems and Internet of Things , () , 39-56 . DOI: https://doi.org/10.54216/JISIoT.170104
    K. M. , N. M. , R. S. , B. M. , V. P. , Kumar S. [2025]. Multi-Disease Recognition in Tea Plants By Evaluating the Performance of Yolo Models. Journal of Intelligent Systems and Internet of Things. (): 39-56. DOI: https://doi.org/10.54216/JISIoT.170104
    K., M. N., M. R., S. B., M. V., P. Kumar, S. "Multi-Disease Recognition in Tea Plants By Evaluating the Performance of Yolo Models," Journal of Intelligent Systems and Internet of Things, vol. , no. , pp. 39-56, 2025. DOI: https://doi.org/10.54216/JISIoT.170104

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