Preprint Article Version 1 This version is not peer-reviewed

A Study of Tangerine Pest Recognition Using Advanced Deep Learning Methods

Version 1 : Received: 5 November 2018 / Approved: 7 November 2018 / Online: 7 November 2018 (13:09:30 CET)

How to cite: Lee, M.; Xing, S. A Study of Tangerine Pest Recognition Using Advanced Deep Learning Methods. Preprints 2018, 2018110161 (doi: 10.20944/preprints201811.0161.v1). Lee, M.; Xing, S. A Study of Tangerine Pest Recognition Using Advanced Deep Learning Methods. Preprints 2018, 2018110161 (doi: 10.20944/preprints201811.0161.v1).

Abstract

To improve the tangerine crop yield, the work of recognizing and then disposing of specific pests is becoming increasingly important. The task of recognition is based on the features extracted from the images that have been collected from websites and outdoors. Traditional recognition and deep learning methods, such as KNN (k-nearest neighbors) and AlexNet, are not preferred by knowledgeable researchers, who have proven them inaccurate. In this paper, we exploit four kinds of structures of advanced deep learning to classify 10 citrus pests. The experimental results show that Inception-ResNet-V3 obtains the minimum classification error.

Subject Areas

pest recognition; Tangerine; advanced deep learning; minimum classification error; Inception Module; CNN

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