VIP-STB farm: scale-up village to county/province level to support science and technology at backyard (STB) program

Yijun Yan; Sophia Zhao; Yuxi Fang; Yuren Liu; Zhongxin Chen; Jinchang Ren

doi:10.1007/978-3-030-39431-8_27

VIP-STB farm: scale-up village to county/province level to support science and technology at backyard (STB) program

Yijun Yan, Sophia Zhao, Yuxi Fang, Yuren Liu, Zhongxin Chen, Jinchang Ren

Electronic And Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

Abstract

In this paper, we introduce a new concept in VIP-STB, a funded project through Agri-Tech in China: Newton Network+ (ATCNN), in developing feasible solutions towards scaling-up STB from village level to upper level via some generic models and systems. There are three tasks in this project, i.e. normalized difference vegetation index (NDVI) estimation, wheat density estimation and household-based small farms (HBSF) engagement. In the first task, several machine learning models have been used to evaluate the performance of NDVI estimation. In the second task, integrated software via Python and Twilio is developed to improve communication services and engagement for HBSFs, and provides technical capabilities. In the third task, crop density/ population is predicted by conventional image processing techniques. The objectives and strategy for VIP-STB are described, experimental results on each task are presented, and more details on each model that has been implemented are also provided with future development guidance.

Original language	English
Title of host publication	Advances in Brain Inspired Cognitive Systems
Subtitle of host publication	10th International Conference, BICS 2019, Guangzhou, China, July 13–14, 2019, Proceedings
Editors	Jinchang Ren, Amir Hussain, Huimin Zhao, Kaizhu Huang, Jiangbin Zheng, Jun Cai, Rongjun Chen, Yinyin Xiao
Place of Publication	Cham, Switzerland
Publisher	Springer
Pages	283-292
Number of pages	10
ISBN (Electronic)	9783030394318
ISBN (Print)	9783030394301
DOIs	https://doi.org/10.1007/978-3-030-39431-8_27
Publication status	Published - 1 Feb 2020

Keywords

precision agriculture
machine learning
information fusion
regression

Access to Document

10.1007/978-3-030-39431-8_27

Cite this

Yan, Y., Zhao, S., Fang, Y., Liu, Y., Chen, Z., & Ren, J. (2020). VIP-STB farm: scale-up village to county/province level to support science and technology at backyard (STB) program. In J. Ren, A. Hussain, H. Zhao, K. Huang, J. Zheng, J. Cai, R. Chen, & Y. Xiao (Eds.), Advances in Brain Inspired Cognitive Systems: 10th International Conference, BICS 2019, Guangzhou, China, July 13–14, 2019, Proceedings (pp. 283-292). Springer. https://doi.org/10.1007/978-3-030-39431-8_27

Yan, Yijun ; Zhao, Sophia ; Fang, Yuxi et al. / VIP-STB farm : scale-up village to county/province level to support science and technology at backyard (STB) program. Advances in Brain Inspired Cognitive Systems: 10th International Conference, BICS 2019, Guangzhou, China, July 13–14, 2019, Proceedings. editor / Jinchang Ren ; Amir Hussain ; Huimin Zhao ; Kaizhu Huang ; Jiangbin Zheng ; Jun Cai ; Rongjun Chen ; Yinyin Xiao. Cham, Switzerland : Springer, 2020. pp. 283-292

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title = "VIP-STB farm: scale-up village to county/province level to support science and technology at backyard (STB) program",

abstract = "In this paper, we introduce a new concept in VIP-STB, a funded project through Agri-Tech in China: Newton Network+ (ATCNN), in developing feasible solutions towards scaling-up STB from village level to upper level via some generic models and systems. There are three tasks in this project, i.e. normalized difference vegetation index (NDVI) estimation, wheat density estimation and household-based small farms (HBSF) engagement. In the first task, several machine learning models have been used to evaluate the performance of NDVI estimation. In the second task, integrated software via Python and Twilio is developed to improve communication services and engagement for HBSFs, and provides technical capabilities. In the third task, crop density/ population is predicted by conventional image processing techniques. The objectives and strategy for VIP-STB are described, experimental results on each task are presented, and more details on each model that has been implemented are also provided with future development guidance.",

keywords = "precision agriculture, machine learning, information fusion, regression",

author = "Yijun Yan and Sophia Zhao and Yuxi Fang and Yuren Liu and Zhongxin Chen and Jinchang Ren",

year = "2020",

month = feb,

day = "1",

doi = "10.1007/978-3-030-39431-8_27",

language = "English",

isbn = "9783030394301",

pages = "283--292",

editor = "Jinchang Ren and Amir Hussain and Huimin Zhao and Kaizhu Huang and Jiangbin Zheng and Jun Cai and Rongjun Chen and Yinyin Xiao",

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publisher = "Springer",

}

Yan, Y, Zhao, S, Fang, Y, Liu, Y, Chen, Z & Ren, J 2020, VIP-STB farm: scale-up village to county/province level to support science and technology at backyard (STB) program. in J Ren, A Hussain, H Zhao, K Huang, J Zheng, J Cai, R Chen & Y Xiao (eds), Advances in Brain Inspired Cognitive Systems: 10th International Conference, BICS 2019, Guangzhou, China, July 13–14, 2019, Proceedings. Springer, Cham, Switzerland, pp. 283-292. https://doi.org/10.1007/978-3-030-39431-8_27

VIP-STB farm: scale-up village to county/province level to support science and technology at backyard (STB) program. / Yan, Yijun; Zhao, Sophia; Fang, Yuxi et al.
Advances in Brain Inspired Cognitive Systems: 10th International Conference, BICS 2019, Guangzhou, China, July 13–14, 2019, Proceedings. ed. / Jinchang Ren; Amir Hussain; Huimin Zhao; Kaizhu Huang; Jiangbin Zheng; Jun Cai; Rongjun Chen; Yinyin Xiao. Cham, Switzerland: Springer, 2020. p. 283-292.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

TY - GEN

T1 - VIP-STB farm

T2 - scale-up village to county/province level to support science and technology at backyard (STB) program

AU - Yan, Yijun

AU - Zhao, Sophia

AU - Fang, Yuxi

AU - Liu, Yuren

AU - Chen, Zhongxin

AU - Ren, Jinchang

PY - 2020/2/1

Y1 - 2020/2/1

N2 - In this paper, we introduce a new concept in VIP-STB, a funded project through Agri-Tech in China: Newton Network+ (ATCNN), in developing feasible solutions towards scaling-up STB from village level to upper level via some generic models and systems. There are three tasks in this project, i.e. normalized difference vegetation index (NDVI) estimation, wheat density estimation and household-based small farms (HBSF) engagement. In the first task, several machine learning models have been used to evaluate the performance of NDVI estimation. In the second task, integrated software via Python and Twilio is developed to improve communication services and engagement for HBSFs, and provides technical capabilities. In the third task, crop density/ population is predicted by conventional image processing techniques. The objectives and strategy for VIP-STB are described, experimental results on each task are presented, and more details on each model that has been implemented are also provided with future development guidance.

AB - In this paper, we introduce a new concept in VIP-STB, a funded project through Agri-Tech in China: Newton Network+ (ATCNN), in developing feasible solutions towards scaling-up STB from village level to upper level via some generic models and systems. There are three tasks in this project, i.e. normalized difference vegetation index (NDVI) estimation, wheat density estimation and household-based small farms (HBSF) engagement. In the first task, several machine learning models have been used to evaluate the performance of NDVI estimation. In the second task, integrated software via Python and Twilio is developed to improve communication services and engagement for HBSFs, and provides technical capabilities. In the third task, crop density/ population is predicted by conventional image processing techniques. The objectives and strategy for VIP-STB are described, experimental results on each task are presented, and more details on each model that has been implemented are also provided with future development guidance.

KW - precision agriculture

KW - machine learning

KW - information fusion

KW - regression

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DO - 10.1007/978-3-030-39431-8_27

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BT - Advances in Brain Inspired Cognitive Systems

A2 - Ren, Jinchang

A2 - Hussain, Amir

A2 - Zhao, Huimin

A2 - Huang, Kaizhu

A2 - Zheng, Jiangbin

A2 - Cai, Jun

A2 - Chen, Rongjun

A2 - Xiao, Yinyin

PB - Springer

CY - Cham, Switzerland

ER -

Yan Y, Zhao S, Fang Y, Liu Y, Chen Z, Ren J. VIP-STB farm: scale-up village to county/province level to support science and technology at backyard (STB) program. In Ren J, Hussain A, Zhao H, Huang K, Zheng J, Cai J, Chen R, Xiao Y, editors, Advances in Brain Inspired Cognitive Systems: 10th International Conference, BICS 2019, Guangzhou, China, July 13–14, 2019, Proceedings. Cham, Switzerland: Springer. 2020. p. 283-292 doi: 10.1007/978-3-030-39431-8_27

VIP-STB farm: scale-up village to county/province level to support science and technology at backyard (STB) program

Abstract

Keywords

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