Survey Paper on Tomato Crop Disease Detection and Pest Management

Survey Paper on Tomato Crop Disease Detection and Pest Management

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) and Tanuja Patankar (Pimpri Chinchwad College of Engineering, India)
Copyright: © 2019 |Pages: 9
DOI: 10.4018/IJAEC.2019070102

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.
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1. Introduction

Agriculture plays a decisive role in India’s economy. It is the major source of raw materials, food, fodder and also foreign exchange. Besides, it is the most common occupation which supports 58% of Indian population. It is the premier source of our national income. Vegetables are important constituents of Indian agriculture due to their short duration, high yield, nutritional richness and ability to generate on-farm and off-farm employment. Farmers have a wide range of choices regarding to crops they can cultivate based on climatic conditions. However, in order to produce quality yield, scientific farming methods need to be followed. Engineering intervention in the form of technology and tools facilitates in maximizing agricultural productivity and profitability on a sustainable basis. In India, tomato covers an area of 4.97 lakh hectares with an annual production of 86 lakh tons which is 8.5% of the total crop production indicating its popularity. It is an important protective food, because of the presence of the several nutritive values. In addition to this, it also plays a major role in Indian traditional cooking. It also tops the list of canned vegetables. However, this crop is threatened by many foliar and fruit diseases.

Indian farmers do not use modern technologies in their farms as most of the western farmers do. They use traditional technologies to do their farming. Government spend crores of rupees in spreading awareness about disease detection. Government establishes centers in rural areas of our country to help farmers primarily check quality of their crops. On the other side farmers have to do manual disease detection and then decide fertilizers for their crop. Sometimes crop diseases can be detected early and could be prevented in early stage, but lack of modern technology and knowledge among farmers prevent them from detecting diseases early. Most of the farmers depend on traditional laboratory methods to detect diseases. These methods are slow and does not reach to the farmers, therefore it is important to develop automatic systems which are available to them at any time, which are cost effective and easily usable.

Modern technologies have given human society the ability to produce enough food to meet the demand of more than 7 billion people. However, food security remains threatened by a number of factors including climate change, the decline in pollinators, plant diseases, and others. We have seen a variety of tomato pests and diseases which seriously affects the tomato yield, such as Tomato bacterial spot, Tomato early blight, Tomato late blight, Tomato leaf mold, Tomato septoria leaf spot, Tomato two spotted spider mite, Tomato target spot, Tomato mosaic virus, Tomato yellow leaf curl virus, Tomato gray spot. In order to effectively control the pests and diseases, it is important to make accurate identifications. However, the process of identification usually relies on experiences and manual identification, which is laborious, time consuming and error-prone.

To do the task of automatic disease analysis and pest detection, we have studied various methods such as Acoustic emission (Yang, GUO & ZHAO, 2009), k-means clustering (Mehra, kumar and gupta, 2016, Tete & Kamlu, 2012, 2017), convolution neural networks such as GoogleNet (Durmuú, Güneú & KõrcÕ, 2017), AlexNet (Mohanty, Hughes, & Salath, 2015), SqueezeNet (Mohanty et al., 2015), thresholding algorithm (Mehra et al., 2016, Tete & Kamlu, 2012), etc. In this review work we are going to present comparison of this methods involved. We have also studied about the pesticide managements and various stages of tomato life cycle which are presented at the end of this work.

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