Article
Version 1
Preserved in Portico This version is not peer-reviewed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: Ruirui, Z.; Shuli, X.; Wenchao, F. A Study of Tangerine Pest Recognition Using Advanced Deep Learning Methods. Preprints 2018, 2018110161 Ruirui, Z.; Shuli, X.; Wenchao, F. A Study of Tangerine Pest Recognition Using Advanced Deep Learning Methods. Preprints 2018, 2018110161
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.
Keywords
pest recognition; Tangerine; advanced deep learning; minimum classification error; Inception Module; CNN
Subject
Engineering, Electrical and Electronic Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment
