國立虎尾科技大學 |

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

Record Type:	Language materials, printed : 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.;
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:

1381171
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 /
LDR:02289nam a2200325 a 4500 001 1116696
003 DE-He213
005 20230920063145.0
006 m d
007 cr nn 008maaau
008 240124s2023 si s 0 eng d
020 $a 9789819949731 $q (electronic bk.)
020 $a 9789819949724 $q (paper)
024 7 $a 10.1007/978-981-99-4973-1 $2 doi
035 $a 978-981-99-4973-1
040 $a GP $c GP
041 0 $a eng
050 4 $a S494.5.D3
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 632.0285631 $2 23
090 $a S494.5.D3 $b W246 2023
100 1 $a Wang, Rujing. $3 1430161
245 1 0 $a Deep learning for agricultural visual perception $h [electronic resource] : $b crop pest and disease detection / $c by Rujing Wang, Lin Jiao, Kang Liu.
260 $a Singapore : $c 2023. $b Springer Nature Singapore : $b Imprint: Springer,
300 $a xii, 131 p. : $b ill., digital ; $c 24 cm.
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.
650 0 $a Deep learning (Machine learning) $3 1381171
650 0 $a Artificial intelligence $x Agricultural applications. $3 1236795
650 1 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Computer Imaging, Vision, Pattern Recognition and Graphics. $3 671334
650 2 4 $a Computer Vision. $3 1127422
650 2 4 $a Machine Learning. $3 1137723
650 2 4 $a Image Processing. $3 669795
650 2 4 $a Agriculture. $3 660421
700 1 $a Jiao, Lin. $3 1430162
700 1 $a Liu, Kang. $e editor. $3 1358717
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-99-4973-1
950 $a Computer Science (SpringerNature-11645)