Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-25T20:59:13.453Z Has data issue: false hasContentIssue false
  • English
  • Français

Design of Smart Agriculture Japan Model

Published online by Cambridge University Press:  01 June 2017

E. Morimoto  and
K. Hayashi
E. Morimoto*
Affiliation:
Faculty of Agriculture Tottori University, Koyama Minami 4-101, Tottori, Japan
K. Hayashi
Affiliation:
National Agriculture Research Organization, Kanondai 1-31-1 Tsukuba, Japan
*
† E-mail: [email protected]
Get access

Abstract

This paper presents schematic review for the smart agricultural model in Japan using data-on-demand information exchange based on smart agricultural machinery systems (SAMS). Four machines were developed in this study, namely Smart rice trans-planter with on-the-go soil sensor; Smart 2nd fertilizer applicator based on CropSpecTM; Yield monitor combine harvester with on-the-go lodging analysis system; and Farm Activity Record Management System (FARMS). The study obtained 450,000 datasets of topsoil accompanied by 65,000 datasets of crop status and 1 million images of lodging information from 50 ha of rice fields, taken in 2016. The results conclude that the field mapping using FARMS was available not only for manager’s decision on fertilizer application, but also for information sharing between employees. A two year feasibility study showed improvement of 20% fertilizer reduction and 30% harvest efficiency than conventional management. The study suggests that SAMS would play an important role for technology succession in the near future.

Keywords

Smart rice trans-planterSmart 2nd fertilizer application systemYield monitor combine harvesterFarm activity record management system
Type
PA in practice
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 713 - 717
Copyright
© The Animal Consortium 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Burrell, J, Brooke, T and Beckwith, R 2004. Vineyard computing: sensor networks in agricultural production. In: IEEE Pervasive Computing 3-1, 38–45.CrossRefGoogle Scholar
Fernandez-Cornejo, J, Beach, ED and Huang, W-Y 1994. The adoption of IPM techniques by vegetable growers in Florida, Michigan, and Texas. Journal of Agricultural and Applied Economics 1, 158–172.Google Scholar
Hayashi, K 2014. Monitoring of the Operating Status of Agricultural Machinery and Information Management Technology using FARMS. Journal of Japanese Society of Agriculture and Biological Engineering 6, 231–235.Google Scholar
Morimoto, E, Hirako, S, Yamasaki, H and Izumi, M 2013. Development of On-the-go Soil Sensor for Rice Transplanter. Engineering in Agriculture, Environment and Food 6 (3), 141–146.Google Scholar
Noguchi, N, Ishii, K and Terao, H 1997. Development of an Agricultural mobile robot using a geomagnetic direction sensor and image sensors. Journal of Agricultural Engineering ResearchVolume 67 (1), 1–15.Google Scholar
Shibusawa, S 2002. Precision farming approaches to small-farm agriculture. Agro-Chemicals Report 2 (4), 13.Google Scholar
Static Agriculture Census 2015. Ministry of Agriculture and Forestry and Fisheries. http://www.maff.go.jp/j/tokei/census/afc/2015/kekka_gaisuuti.html Google Scholar