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Computer Vision and Machine Learning in Agriculture

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Computer Vision and Machine Learning in Agriculture/ edited by Mohammad Shorif Uddin, Jagdish Chand Bansal.
other author:	Uddin, Mohammad Shorif.
Description:	XIV, 172 p. 72 illus., 61 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computational intelligence. -
Online resource:	https://doi.org/10.1007/978-981-33-6424-0
ISBN:	9789813364240

Computer Vision and Machine Learning in Agriculture
Computer Vision and Machine Learning in Agriculture[electronic resource] /edited by Mohammad Shorif Uddin, Jagdish Chand Bansal. - 1st ed. 2021. - XIV, 172 p. 72 illus., 61 illus. in color.online resource. - Algorithms for Intelligent Systems,2524-7573. - Algorithms for Intelligent Systems,.

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.

ISBN: 9789813364240

Standard No.: 10.1007/978-981-33-6424-0doiSubjects--Topical Terms:

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Computer Vision and Machine Learning in Agriculture
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505 0 $a 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.
520 $a 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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