Tomato Crop Disease Detection and Pest Management

Authors

  • Tanuja Patankar, Dr. Sonali Patil, Abhijeet Gurle

Abstract

Agriculture field plays a vital role in India as most of the population depends on it. To strengthen the agriculture field research on various topics related to that field is very important. Leaf disease is one of the major area where lot of work need to be done. This paper proposes an idea to detect tomato disease using tomato leaf and suggests the pesticide as a remedy. The proposed system provides tomato diseases detection and pesticide management application. Farmers can capture an image of tomato leaf and then application detects the accurate disease and suggests pesticide accordingly. The system also provides alerts if certain tomato disease is spreading in an area due to various environmental or ecological factors. The proposed system is developed using convolution neural network (CNN) for disease detection. The Plant Village Dataset and Rashtriya Chemical Fertilizers (RCF) Datasets are used to validate the proposed system. These two datasets includes colored and grayscale images of Village Dataset and Colored Images of RCF Dataset. The overall average accuracy achieved by considering above three types of images is 94.66%.

Keywords: Tomato diseases, Leaf images, Deep Learning, CNN, Pest Management.

Downloads

Published

2020-05-18

Issue

Section

Articles