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Deep learning for agricultural visua...

Wang, Rujing.

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Deep learning for agricultural visual perception = crop pest and disease detection /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning for agricultural visual perception/ by Rujing Wang, Lin Jiao, Kang Liu.
Reminder of title:	crop pest and disease detection /
Author:	Wang, Rujing.
other author:	Jiao, Lin.
Published:	Singapore :Springer Nature Singapore : : 2023.,
Description:	xii, 131 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1. Introduction -- Chapter 2. Deep Learning Technology -- Chapter 3. Large-Scale Agricultural Pest and Disease Datasets -- Chapter 4. Sampling-balanced Region Proposal Network for Pest Detection -- Chapter 5. Crop Pest Detection Methods in Field -- Chapter 6. A CNN-based Arbitrary-oriented Wheat Disease Detection Method.
Contained By:	Springer Nature eBook
Subject:	Deep learning (Machine learning) -
Online resource:	https://doi.org/10.1007/978-981-99-4973-1
ISBN:	9789819949731

Deep learning for agricultural visual perception = crop pest and disease detection /
Wang, Rujing.

Deep learning for agricultural visual perceptioncrop pest and disease detection /[electronic resource] :by Rujing Wang, Lin Jiao, Kang Liu. - Singapore :Springer Nature Singapore :2023. - xii, 131 p. :ill., digital ;24 cm.

Chapter 1. Introduction -- Chapter 2. Deep Learning Technology -- Chapter 3. Large-Scale Agricultural Pest and Disease Datasets -- Chapter 4. Sampling-balanced Region Proposal Network for Pest Detection -- Chapter 5. Crop Pest Detection Methods in Field -- Chapter 6. A CNN-based Arbitrary-oriented Wheat Disease Detection Method.

This monograph provides a detailed and systematic introduction to the application of deep learning technology in the intelligent monitoring of crop diseases and pests. Taking 24 types of crop pests, wheat aphids, and wheat diseases with complex backgrounds as examples, a large-scale crop pest and disease dataset was constructed to provide necessary data support for the deep learning module. Various schemes for identifying and detecting large-scale crop diseases and pests based on deep convolutional neural network technology have also been proposed. This book can be used as a reference for teachers and students majoring in agriculture, computer science, artificial intelligence, intelligent science and technology, and other related fields in higher education institutions. It can also be used as a reference book for researchers in fields such as image processing technology, intelligent manufacturing, and high-tech applications.

ISBN: 9789819949731

Standard No.: 10.1007/978-981-99-4973-1doiSubjects--Topical Terms:

3538509
Deep learning (Machine learning)

LC Class. No.: S494.5.D3

Dewey Class. No.: 632.0285631

Deep learning for agricultural visual perception = crop pest and disease detection /
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505 0 $a Chapter 1. Introduction -- Chapter 2. Deep Learning Technology -- Chapter 3. Large-Scale Agricultural Pest and Disease Datasets -- Chapter 4. Sampling-balanced Region Proposal Network for Pest Detection -- Chapter 5. Crop Pest Detection Methods in Field -- Chapter 6. A CNN-based Arbitrary-oriented Wheat Disease Detection Method.
520 $a This monograph provides a detailed and systematic introduction to the application of deep learning technology in the intelligent monitoring of crop diseases and pests. Taking 24 types of crop pests, wheat aphids, and wheat diseases with complex backgrounds as examples, a large-scale crop pest and disease dataset was constructed to provide necessary data support for the deep learning module. Various schemes for identifying and detecting large-scale crop diseases and pests based on deep convolutional neural network technology have also been proposed. This book can be used as a reference for teachers and students majoring in agriculture, computer science, artificial intelligence, intelligent science and technology, and other related fields in higher education institutions. It can also be used as a reference book for researchers in fields such as image processing technology, intelligent manufacturing, and high-tech applications.
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700 1 $a Liu, Kang. $3 3341689
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