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An Adaptive Position Synchronization Controller Using Orthogonal Neural Network for 3-DOF Planar Parallel Manipulators Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Agricultural Pests Tracking and Identification in Video Surveillance Based on Deep Learning

Authors : Xi Cheng, You-Hua Zhang, Yun-Zhi Wu, Yi Yue

Published in: Intelligent Computing Methodologies

Publisher: Springer International Publishing

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Abstract

Agricultural pests can cause serious damage to crops and need to be identified during the agricultural pest prevention and control process. In comparison with the low-speed and inefficient artificial identification method, it is important to develop a fast and reliable method for identifying agricultural pests based on computer vision. Aiming at the problem of agricultural pest identification in complex farmland environment, a recognition method through deep learning is proposed. The method could recognize and track the agricultural pests in surveillance videos of farmlands by using deep convolutional neural network and Faster R-CNN models. Compared with the traditional machine learning methods, this method has higher recognition accuracy in high background noise, and it can still effectively recognize agricultural pests with protective colorations. Therefore, compared with the current agricultural pest static-image recognition method, this method has a higher practical value and can be put into the actual agricultural production environment with the agricultural networking technology.

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Metadata
Title
Agricultural Pests Tracking and Identification in Video Surveillance Based on Deep Learning
Authors
Xi Cheng
You-Hua Zhang
Yun-Zhi Wu
Yi Yue
Copyright Year
2017
Book
Intelligent Computing Methodologies

Print ISBN: 978-3-319-63314-5

Electronic ISBN: 978-3-319-63315-2

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-63315-2_6

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