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Deep Learning for Agricultural Visual Perception

Crop Pest and Disease Detection

By: Rujing Wang,Lin Jiao,Kang Liu

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Deep Learning for Agricultural Visual Perception

Crop Pest and Disease Detection

By: Rujing Wang,Lin Jiao,Kang Liu

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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Page Count

140

Publisher

Springer Nature

Published Date

ISBN 10

9819949734

ISBN 13

9789819949731

Deep Learning for Agricultural Visual Perception

Crop Pest and Disease Detection

By: Rujing Wang,Lin Jiao,Kang Liu

Write a review Read reviews Take a Quiz Solve Book Puzzle

Deep Learning for Agricultural Visual Perception

Crop Pest and Disease Detection

By: Rujing Wang,Lin Jiao,Kang Liu

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.

Average ratings

N/A

5

0%

4

0%

3

0%

2

0%

1

0%

Solve jigsaw puzzle

Age

Page Count

140

Publisher

Springer Nature

Published Date

ISBN 10

9819949734

ISBN 13

9789819949731