TY - GEN
T1 - An application of multispectral semantic-image space for global farming analysis and crop condition comparisons
AU - Wijitdechakul, Jinmika
AU - Kiyoki, Yasushi
AU - Sasaki, Shiori
N1 - Funding Information:
This research is supported in part by MEXT Grant-in-Aid for the Program for Leading Graduate School Program (GESL) and Multimedia Database Laboratory (MDBL), Graduate School of Media and Governance, Keio University. We thank the anonymous reviewers for their valuable and suggestions.
Publisher Copyright:
© 2018 The authors and IOS Press. All rights reserved.
PY - 2018
Y1 - 2018
N2 - The global environmental analysis system is a new platform to analyze environmental multimedia data that acquired from nature resources. This system aims to realize and interpret environmental phenomena and changes occurring that happening in world wide scope. Semantic computing is important and promising approach to multispectral semantic-image analysis for various environmental aspects and contexts in physical world. In the previous study, we proposed a new system of agricultural monitoring and analysis based on semantic computing concept that it realizes the interpretation of agricultural health condition as human-level interpretation. In this paper, we propose a new analytical method for agriculture global comparisons to realize and recognize crop condition with several places in global scale. Multispectral semantic-image space for agricultural analysis can be utilized for global crop health monitoring by comparing crop conditions among different places. Our method applies semantic distance calculation to measure similarity among multispectral image data to realize the crop health condition as a ranking. According to our new proposed analytical method, we demonstrate a prototype implementation in the case of rye farm in Latvia and Finland. This prototype implementation shows an analysis in the case that image data have same crop type and conditions.
AB - The global environmental analysis system is a new platform to analyze environmental multimedia data that acquired from nature resources. This system aims to realize and interpret environmental phenomena and changes occurring that happening in world wide scope. Semantic computing is important and promising approach to multispectral semantic-image analysis for various environmental aspects and contexts in physical world. In the previous study, we proposed a new system of agricultural monitoring and analysis based on semantic computing concept that it realizes the interpretation of agricultural health condition as human-level interpretation. In this paper, we propose a new analytical method for agriculture global comparisons to realize and recognize crop condition with several places in global scale. Multispectral semantic-image space for agricultural analysis can be utilized for global crop health monitoring by comparing crop conditions among different places. Our method applies semantic distance calculation to measure similarity among multispectral image data to realize the crop health condition as a ranking. According to our new proposed analytical method, we demonstrate a prototype implementation in the case of rye farm in Latvia and Finland. This prototype implementation shows an analysis in the case that image data have same crop type and conditions.
KW - Agricultural monitoring
KW - Global farming analysis
KW - Semantic computing
KW - UAV-multispectral sensor
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U2 - 10.3233/978-1-61499-834-1-176
DO - 10.3233/978-1-61499-834-1-176
M3 - Conference contribution
AN - SCOPUS:85063365128
T3 - Frontiers in Artificial Intelligence and Applications
SP - 176
EP - 187
BT - Information Modelling and Knowledge Bases XXIX
A2 - Sornlertlamvanich, Virach
A2 - Chawakitchareon, Petchporn
A2 - Kiyoki, Yasushi
A2 - Thalheim, Bernhard
A2 - Hansuebsai, Aran
A2 - Yoshida, Naofumi
A2 - Jaakkola, Hannu
A2 - Koopipat, Chawan
PB - IOS Press
T2 - 27th International Conference on Information Modelling and Knowledge Bases, EJC 2017
Y2 - 5 June 2017 through 9 June 2017
ER -
