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Survey Paper on Tomato Crop Disease Detection and Pest Management

Author

Listed:
  • Abhijeet Somnath Gurle

    (Pimpri Chinchwad College of Engineering, India)

  • Sankalp Nitin Barathe

    (Pimpri Chinchwad College of Engineering, India)

  • Roshan Shankar Gangule

    (Pimpri Chinchwad College of Engineering, India)

  • Shubham Dipak Jagtap

    (Pimpri Chinchwad College of Engineering, India)

  • Tanuja Patankar

    (Pimpri Chinchwad College of Engineering, India)

Abstract

India is an agricultural country and most of peoples wherein about 70% depends on agriculture. So, disease detection is very important research topic. There are many species of tomato diseases and pests, the pathology of which is complex. Crop diseases are a major threat to crop production, but their identification remains difficult in many parts of India due to the lack of the necessary infrastructure. It is difficult and error-prone to simply rely on manual identification. Recent advances in computer vision made possible by deep learning has made the way for automatic disease detection. In this article, the authors have analysed a method of disease detection and pest management using a convolution neural networks (CNN), k-means clustering, and acoustic emission.

Suggested Citation

  • Abhijeet Somnath Gurle & Sankalp Nitin Barathe & Roshan Shankar Gangule & Shubham Dipak Jagtap & Tanuja Patankar, 2019. "Survey Paper on Tomato Crop Disease Detection and Pest Management," International Journal of Applied Evolutionary Computation (IJAEC), IGI Global, vol. 10(3), pages 10-18, July.
    • Handle: RePEc:igg:jaec00:v:10:y:2019:i:3:p:10-18
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