國立高雄大學圖資館 |

Language: English

Back

Sensing technologies for field and in-house crop productiontechnology review and case studies /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Sensing technologies for field and in-house crop productionedited 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.
Contained By:	Springer Nature eBook
Subject:	Agricultural innovations.
Online resource:	https://doi.org/10.1007/978-981-99-7927-1
ISBN:	9789819979271$q(electronic bk.)

Sensing technologies for field and in-house crop productiontechnology 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. 1..

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$q(electronic bk.)

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

575627
Agricultural innovations.

LC Class. No.: S494.5.I5

Dewey Class. No.: 631.3

Sensing technologies for field and in-house crop productiontechnology review and case studies /
LDR:02416nmm a2200337 a 4500 001 654002
003 DE-He213
005 20231127131655.0
006 m d
007 cr nn 008maaau
008 240426s2023 si s 0 eng d
020 $a 9789819979271$q(electronic bk.)
020 $a 9789819979264$q(paper)
024 7 $a 10.1007/978-981-99-7927-1 $2 doi
035 $a 978-981-99-7927-1
040 $a GP $c GP
041 0 $a eng
050 4 $a S494.5.I5
072 7 $a TJFM1 $2 bicssc
072 7 $a TEC037000 $2 bisacsh
072 7 $a TJFM1 $2 thema
082 0 4 $a 631.3 $2 23
090 $a S494.5.I5 $b S478 2023
245 0 0 $a Sensing technologies for field and in-house crop production $h [electronic resource] : $b technology review and case studies / $c edited by Man Zhang ... [et al.].
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2023.
300 $a vii, 136 p. : $b illustrations (chiefly color), digital ; $c 24 cm.
490 1 $a Smart agriculture, $x 2731-3484 ; $v v. 7
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.
650 0 $a Agricultural innovations. $3 575627
650 0 $a Artificial intelligence $x Agricultural applications. $3 803531
650 0 $a Detectors. $3 197808
650 0 $a Image processing. $3 182627
650 1 4 $a Robotic Engineering. $3 919661
650 2 4 $a Mechatronics. $3 189824
650 2 4 $a Robotics. $3 181952
650 2 4 $a Agriculture. $3 274257
650 2 4 $a Automation. $3 190451
650 2 4 $a Image Processing. $3 273705
700 1 $a Zhang, Man. $3 964751
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Smart agriculture ; $v v. 1. $3 917804
856 4 0 $u https://doi.org/10.1007/978-981-99-7927-1
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)