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020			_a9789813364240
024	7		_a10.1007/978-981-33-6424-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aS494.5.D3 _b2021 EB
245	1	0	_aComputer Vision and Machine Learning in Agriculture _cedited by Mohammad Shorif Uddin, Jagdish Chand Bansal.
250			_aFirst edition 2021
264		1	_aSingapore _bSpringer International Pulishing _c2021
300			_a1 recurso en línea (XIV, 172 páginas) _b72 ilustraciones, 61 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aAlgorithms for Intelligent Systems _x2524-7573
490	0		_aIntelligent Technologies and Robotics (SpringerNature-42732)
490	0		_aIntelligent Technologies and Robotics (R0) (SpringerNature-43728)
505	0		_aChapter 1. Introduction to Computer Vision and Machine Learning Applications in Agriculture -- Chapter 2. Robots and Drones in Agriculture - A Survey -- Chapter 3. Detection of Rotten Fruits and Vegetables using Deep Learning -- Chapter 4. Deep Learning-Based Essential Paddy Pests Filtration Technique: A Better Economic Damage Management Process -- Chapter 5. Deep CNN-Based Mango Insect Classification -- Chapter 6. Implementation of a Deep Convolutional Neural Network for the Detection of Tomato Leaf Diseases -- Chapter 7. A Multi-Plant Disease Diagnosis Method using Convolutional Neural Network -- Chapter 8. A Deep Learning-Based Approach for Potato Diseases Classification -- Chapter 9. An In-Depth Analysis of Different Segmentation Techniques in Automated Local Fruit Disease Recognition -- Chapter 10. Machine Vision Based Fruit and Vegetable Disease Recognition: A Review -- Chapter 11. An Efficient Bag-of-Features for Diseased Plant Identification.
520	3		_aThis book discusses computer vision, a noncontact as well as a nondestructive technique involving the development of theoretical and algorithmic tools for automatic visual understanding and recognition which finds huge applications in agricultural productions. It also entails how rendering of machine learning techniques to computer vision algorithms is boosting this sector with better productivity by developing more precise systems. Computer vision and machine learning (CV-ML) helps in plant disease assessment along with crop condition monitoring to control the degradation of yield, quality, and severe financial loss for farmers. Significant scientific and technological advances have been made in defect assessment, quality grading, disease recognition, pests, insects, fruits, and vegetable types recognition and evaluation of a wide range of agricultural plants, crops, leaves, and fruits. The book discusses intelligent robots developed with the touch of CV-ML which can help farmers to perform various tasks like planting, weeding, harvesting, plant health monitoring, and so on. The topics covered in the book include plant, leaf, and fruit disease detection, crop health monitoring, applications of robots in agriculture, precision farming, assessment of product quality and defects, pest, insect, fruits, and vegetable types recognition.
988			_aSpringer_Robotics_2021
650		7	_2embne _9205076 _aAgricultura _xInnovaciones tecnológicas
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
776	0	8	_iPrinted edition: _z9789813364233
776	0	8	_iPrinted edition: _z9789813364257
776	0	8	_iPrinted edition: _z9789813364264
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-33-6424-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE