000			03481nam a22006255i 4500
001			978-981-99-4973-1
003			DE-He213
005			20240423130105.0
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008			230920s2023 si | s |||| 0|eng d
020			_a9789819949731 _9978-981-99-4973-1
024	7		_a10.1007/978-981-99-4973-1 _2doi
050		4	_aQ334-342
050		4	_aTA347.A78
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072		7	_aCOM004000 _2bisacsh
072		7	_aUYQ _2thema
082	0	4	_a006.3 _223
100	1		_aWang, Rujing. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aDeep Learning for Agricultural Visual Perception _h[electronic resource] : _bCrop Pest and Disease Detection / _cby Rujing Wang, Lin Jiao, Kang Liu.
250			_a1st ed. 2023.
264		1	_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2023.
300			_aXII, 131 p. 1 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aChapter 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			_aThis 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	_aArtificial intelligence.
650		0	_aImage processing _xDigital techniques.
650		0	_aComputer vision.
650		0	_aMachine learning.
650		0	_aImage processing.
650		0	_aAgriculture.
650	1	4	_aArtificial Intelligence.
650	2	4	_aComputer Imaging, Vision, Pattern Recognition and Graphics.
650	2	4	_aComputer Vision.
650	2	4	_aMachine Learning.
650	2	4	_aImage Processing.
650	2	4	_aAgriculture.
700	1		_aJiao, Lin. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aLiu, Kang. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9789819949724
776	0	8	_iPrinted edition: _z9789819949748
776	0	8	_iPrinted edition: _z9789819949755
856	4	0	_uhttps://doi.org/10.1007/978-981-99-4973-1
912			_aZDB-2-SCS
912			_aZDB-2-SXCS
942			_cSPRINGER
999			_c184929 _d184929