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Sensing technologies for field and i...

Zhang, Man.

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Sensing technologies for field and in-house crop production = technology review and case studies /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Sensing technologies for field and in-house crop production/ edited by Man Zhang ... [et al.].
Reminder of title:	technology review and case studies /
other author:	Zhang, Man.
Published:	Singapore :Springer Nature Singapore : : 2023.,
Description:	vii, 136 p. :illustrations (chiefly color), digital ;24 cm.
[NT 15003449]:	A Review of Three-Dimensional Multispectral Imaging in Plant Phenotyping -- Recent Advances in Soil Nutrient Monitoring: A Review -- Plant phenotyping robot platform -- Autonomous crop image acquisition system based on ROS system -- SeedingsNet: Field wheat seedling density detection based on deep learning -- Wheat lodging detection using smart vision-based method -- Design, construction, and experiment-based key parameter de-2 termination of auto maize seed placement system -- Development and test of an auto seedling detection System.
Contained By:	Springer Nature eBook
Subject:	Agricultural innovations. -
Online resource:	https://doi.org/10.1007/978-981-99-7927-1
ISBN:	9789819979271

Sensing technologies for field and in-house crop production = technology review and case studies /
Sensing technologies for field and in-house crop productiontechnology review and case studies /[electronic resource] :edited by Man Zhang ... [et al.]. - Singapore :Springer Nature Singapore :2023. - vii, 136 p. :illustrations (chiefly color), digital ;24 cm. - Smart agriculture,v. 72731-3484 ;. - Smart agriculture ;v. 7..

A Review of Three-Dimensional Multispectral Imaging in Plant Phenotyping -- Recent Advances in Soil Nutrient Monitoring: A Review -- Plant phenotyping robot platform -- Autonomous crop image acquisition system based on ROS system -- SeedingsNet: Field wheat seedling density detection based on deep learning -- Wheat lodging detection using smart vision-based method -- Design, construction, and experiment-based key parameter de-2 termination of auto maize seed placement system -- Development and test of an auto seedling detection System.

This book focuses on state-of-the-art sensing and automation technologies for field crops and in-house product production and provides a lot of innovative knowledge on image processing, AI algorithms and applications in agriculture, and robotics. This book provides undergraduate or graduate students with take-away knowledge for unmanned agricultural production, including but not limited to corn disease detection, wheat head detection and counting, and soil nutrient condition monitoring. The first three chapters focus on reviewing plant phenotyping sensing technology and robotics and soil nutrient monitoring, followed by in-house crop sensing robotics. Then two case studies on corn and the other two case studies on wheat are presented.

ISBN: 9789819979271

Standard No.: 10.1007/978-981-99-7927-1doiSubjects--Topical Terms:

570532
Agricultural innovations.

LC Class. No.: S494.5.I5

Dewey Class. No.: 631.3

Sensing technologies for field and in-house crop production = technology review and case studies /
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505 0 $a A Review of Three-Dimensional Multispectral Imaging in Plant Phenotyping -- Recent Advances in Soil Nutrient Monitoring: A Review -- Plant phenotyping robot platform -- Autonomous crop image acquisition system based on ROS system -- SeedingsNet: Field wheat seedling density detection based on deep learning -- Wheat lodging detection using smart vision-based method -- Design, construction, and experiment-based key parameter de-2 termination of auto maize seed placement system -- Development and test of an auto seedling detection System.
520 $a This book focuses on state-of-the-art sensing and automation technologies for field crops and in-house product production and provides a lot of innovative knowledge on image processing, AI algorithms and applications in agriculture, and robotics. This book provides undergraduate or graduate students with take-away knowledge for unmanned agricultural production, including but not limited to corn disease detection, wheat head detection and counting, and soil nutrient condition monitoring. The first three chapters focus on reviewing plant phenotyping sensing technology and robotics and soil nutrient monitoring, followed by in-house crop sensing robotics. Then two case studies on corn and the other two case studies on wheat are presented.
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