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008			220625s2022 si | s |||| 0|eng d
020			_a9789811699917
024	7		_a10.1007/978-981-16-9991-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aS494.5.D3 _b2022 EB
245	0	0	_aComputer Vision and Machine Learning in Agriculture _cedited by Mohammad Shorif Uddin, Jagdish Chand Bansal _nVolume 2
250			_aFirst edition 2022
264		1	_aSingapore _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XIII, 260 páginas) _b142 ilustraciones, 125 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aAlgorithms for Intelligent Systems _x2524-7573
505	0		_aHarvesting robots for smart agriculture -- Drone-based weed detection architectures using deep learning algorithms and real-time analytics -- A deep learning-based detection system of multi-class crops and orchards using a UAV -- Real-life agricultural data retrieval for large scale annotation flow optimization -- Design and analysis of IoT-based modern agriculture monitoring system for real time data collection -- Estimation of wheat yield based on precipitation and evapotranspiration using soft computing methods -- Coconut maturity recognition using convolutional neural network -- Agri food products quality assessment methods -- Medicinal plant recognition from leaf images using deep learning -- ESMO based plant leaf disease identification: A machine learning approach -- Deep learning-based cuali flower disease classification -- An Intelligent System for Crop Disease Identification and Dispersion Forecasting in SriLanka -- Apple leaves diseases detection using deep convolutional neural networks and transfer learning -- A deep learning paradigm for detection and segmentation of plant leaves diseases -- Early-stage prediction of plant leaf diseases using deep learning models.
520			_aThis book is as an extension of previous book "Computer Vision and Machine Learning in Agriculture" for academicians, researchers, and professionals interested in solving the problems of agricultural plants and products for boosting production by rendering the advanced machine learning including deep learning tools and techniques to computer vision algorithms. The book contains 15 chapters. The first three chapters are devoted to crops harvesting, weed, and multi-class crops detection with the help of robots and UAVs through machine learning and deep learning algorithms for smart agriculture. Next, two chapters describe agricultural data retrievals and data collections. Chapters 6, 7, 8 and 9 focuses on yield estimation, crop maturity detection, agri-food product quality assessment, and medicinal plant recognition, respectively. The remaining six chapters concentrates on optimized disease recognition through computer vision-based machine and deep learning strategies.
988			_aSpringer_Robotics_2022
650		7	_9665994 _aAgricultura _xProceso de datos
650		7	_2embne _9159793 _aVisión por ordenador
650		7	_2embne _9166090 _aAprendizaje automático
700			_aUddin, Mohammad Shorif _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _9681111
700	1		_aBansal, Jagdish Chand _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _9100803
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9789811699900
776	0	8	_iPrinted edition: _z9789811699924
776	0	8	_iPrinted edition: _z9789811699931
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-16-9991-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b06/2022 _dz _eIG _zSI