Survey Paper on Tomato Crop Disease Detection and Pest Management

Abhijeet Somnath Gurle, Sankalp Nitin Barathe, Roshan Shankar Gangule, Shubham Dipak Jagtap and Tanuja Patankar
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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

International Journal of Applied Evolutionary Computation (IJAEC), 2019, vol. 10, issue 3, 10-18

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.

Date: 2019
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