Computer Vision and Machine Learning in Agriculture /

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 machin...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Uddin, Mohammad Shorif (Editor), Bansal, Jagdish Chand (Editor)
Format: eBook
Language:English
Published: Singapore : Springer Singapore : Imprint: Springer, 2021.
Edition:1st ed. 2021.
Series:Algorithms for Intelligent Systems,
Subjects:
Online Access:Connect to the full text of this electronic book
Description
Summary: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.
Physical Description:1 online resource (XIV, 172 pages 72 illustrations, 61 illustrations in color.)
ISBN:9789813364240
ISSN:2524-7573
DOI:10.1007/978-981-33-6424-0