PLANT SPECIES AND DISEASE DETECTION

Authors

  • Nayan V Bhandari, Prajna N, Likith R, Nishith R B.E Students, Department of Computer Science & Engg. Global Academy of Technology, Bangalore
  • Dr. Anitha K Associate Professor, Department of Computer Science & Engg.Global Academy of Technology, Bangalore

Keywords:

Machine Learning, Support Vector Machine, Convolutional Neural Network, Long Short-Term Memory, Principal Component Analysis

Abstract

This survey paper examines the current state of the art in plant species and disease detection using machine learning techniques. The paper explores various approaches, including image-based, and analyses their strengths and weaknesses. Additionally, the paper investigates the challenges associated with data collection and annotation, as well as the performance metrics used to evaluate the accuracy of detection models. The paper concludes with a discussion on the potential future directions of this field, including the integration of emerging technologies such as drone-based imaging and edge computing.

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Published

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How to Cite

Nayan V Bhandari, Prajna N, Likith R, Nishith R, & Dr. Anitha K. (2023). PLANT SPECIES AND DISEASE DETECTION. EPRA International Journal of Research and Development (IJRD), 8(5), 97–100. Retrieved from http://www.eprajournals.net/index.php/IJRD/article/view/2012