Recommendation of Pesticide for Roof Top Pest Image Using Convolutional Neural Network Model

Recommendation of Pesticide for Roof Top Pest Image Using Convolutional Neural Network Model

Elangovan Ramanujam, S. Padmavathi, Nashwa Ahmad Kamal
Copyright: © 2021 |Pages: 14
DOI: 10.4018/IJSKD.2021010104
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Abstract

Rooftop farming in urban places is gaining more popularity which increases the cultivation of organic vegetables on the rooftop of houses and buildings with the minimal utilization of water. But rooftop farming is more vulnerable to pest infestation which reduces the quality of plants. Urban residents are novices in farming, and they are unaware of the pest attacks. Various researchers have proposed pest identification systems using image processing techniques and machine learning algorithms specific to particular disease which shows less accuracy on generaliztion and not user-friendly. To provide user-friendly pest identification system, this paper proposes a mobile based pest identification system using the concept of pre-trained convolutional neural network model – AlexNet. Experimental results have been analyzed with various rooftop pests using different kernel sizes and layers of convolutional neural network. In addition, the best evaluated pre-trained model has been converted to a mobile application using REST API for the recommendation of pesticide to the novice user.
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2. Literature Review

Plant disease due to attack of pest is the significant factor which reduces the yield and quality of the food products (Oerke, 2006). In this case, early detection and removal of pest is very important to improve the yield of the agricultural products. Traditionally, the pest and the plant disease are classified by an expert based on the features of pest. The accuracy of the proposed classification greatly depends on the experts’ knowledge and experience, which makes subjective and limited. On comparing the state-of-the-art techniques, Digital Image Processing (Barbedo, 2016) and pattern recognition technique (Kandalkar et al, 2013, el den Mohamed et al, 2020) performs better and provide accurate results.

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