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Transforming technologies in smart agriculturenew applications during the past decade /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Transforming technologies in smart agricultureedited by Zhao Zhang ... [et al.].
Reminder of title:	new applications during the past decade /
other author:	Zhang, Zhao.
Published:	Singapore :Springer Nature Singapore :2024.
Description:	ix, 193 p. :ill. (chiefly col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Agricultural innovations.
Online resource:	https://doi.org/10.1007/978-981-97-4141-0
ISBN:	9789819741410$q(electronic bk.)

Transforming technologies in smart agriculturenew applications during the past decade /
Transforming technologies in smart agriculturenew applications during the past decade /[electronic resource] :edited by Zhao Zhang ... [et al.]. - Singapore :Springer Nature Singapore :2024. - ix, 193 p. :ill. (chiefly col.), digital ;24 cm. - Smart agriculture,v. 82731-3484 ;. - Smart agriculture ;v. 1..

Chapter 1 A Review of Field Plant Phenotyping Platform, Sensing and Navigation Technology -- Chapter 2 Precision Handling of Green Apples in Orchard Environments Using Computer Vision -- Chapter 3 Corn Diseased Leaf Segmentation under Different Illumination Conditions -- Chapter 4 Research on Field Cotton Yield Prediction System Based on Improved YOLOv5 Cotton Detection Model and Android Development -- Chapter 5 Automatic segmentation algorithm for wheat field images based on UAV -- Chapter 6 Aphid Detection Techniques: A State-of-Art Review -- Chapter 7 A Comprehensive Analysis of Different Object Detection Frameworks and Path Optimization Algorithms for an RGB-D Camera-based Rock Detection System.

This book collects transforming technologies in smart agriculture and their applications during the past decade. It focuses on the latest sensing and automation technologies for field and specialty crop production, and provides a lot of innovative knowledge on imaging processing, AI algorithms, robotics, and their applications in agriculture. It provides undergraduate or graduate students take-away knowledge for unmanned agriculture production, including but not limited to, corn/wheat disease detection, cotton yield prediction, and apple detection. Furthermore, this book includes review reports on plant phenotyping sensing and automation technologies and techniques for aphid detection.

ISBN: 9789819741410$q(electronic bk.)

Standard No.: 10.1007/978-981-97-4141-0doiSubjects--Topical Terms:

575627
Agricultural innovations.

LC Class. No.: S494.5.I5

Dewey Class. No.: 338.16

Transforming technologies in smart agriculturenew applications during the past decade /
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505 0 $a Chapter 1 A Review of Field Plant Phenotyping Platform, Sensing and Navigation Technology -- Chapter 2 Precision Handling of Green Apples in Orchard Environments Using Computer Vision -- Chapter 3 Corn Diseased Leaf Segmentation under Different Illumination Conditions -- Chapter 4 Research on Field Cotton Yield Prediction System Based on Improved YOLOv5 Cotton Detection Model and Android Development -- Chapter 5 Automatic segmentation algorithm for wheat field images based on UAV -- Chapter 6 Aphid Detection Techniques: A State-of-Art Review -- Chapter 7 A Comprehensive Analysis of Different Object Detection Frameworks and Path Optimization Algorithms for an RGB-D Camera-based Rock Detection System.
520 $a This book collects transforming technologies in smart agriculture and their applications during the past decade. It focuses on the latest sensing and automation technologies for field and specialty crop production, and provides a lot of innovative knowledge on imaging processing, AI algorithms, robotics, and their applications in agriculture. It provides undergraduate or graduate students take-away knowledge for unmanned agriculture production, including but not limited to, corn/wheat disease detection, cotton yield prediction, and apple detection. Furthermore, this book includes review reports on plant phenotyping sensing and automation technologies and techniques for aphid detection.
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