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Computer Vision and Machine Learning in Agriculture / edited by Mohammad Shorif Uddin, Jagdish Chand Bansal.

Contributor(s): Uddin, Mohammad Shorif, editor literario | Bansal, Jagdish Chand, editor literario
Series: (Algorithms for Intelligent Systems, 2524-7573); (Intelligent Technologies and Robotics (SpringerNature-42732)); (Intelligent Technologies and Robotics (R0) (SpringerNature-43728)).Publisher: Singapore : Springer International Pulishing, 2021Edition: First edition 2021.Description: 1 recurso en línea (XIV, 172 páginas) : 72 ilustraciones, 61 ilustraciones a color.ISBN: 9789813364240.Subject: Agricultura -- Innovaciones tecnológicasOnline resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources
Contents:
Chapter 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.
Abstract: This 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.
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Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Madrid Digital Acceso Electrónico (UEM) Ciencias e Ingeniería S494.5.D3 2021 EB (Browse shelf(Opens below)) Acceso electrónico eBook.23122048
Total holds: 0

Chapter 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.

This 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.

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