Drought Damage Assessment for Crop Insurance Based on Vegetation Index by Unmanned Aerial Vehicle (UAV) Multispectral Images of Paddy Fields in Indonesia

Yu Iwahashi, Gunardi Sigit, Budi Utoyo, Iskandar Lubis, Ahmad Junaedi, Bambang Hendro Trisasongko, I Made Anom Sutrisna Wijaya, Masayasu Maki, Chiharu Hongo and Koki Homma ()
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Yu Iwahashi: Graduate School of Agriculture, Kyoto University, Kyoto 6068224, Japan
Gunardi Sigit: Regional Office of Food Crops Service West Java Province, Cianjur 43283, Indonesia
Budi Utoyo: Regional Office of Food Crops Service West Java Province, Cianjur 43283, Indonesia
Iskandar Lubis: Faculty of Agriculture, IPB University, Bogor 16680, Indonesia
Ahmad Junaedi: Faculty of Agriculture, IPB University, Bogor 16680, Indonesia
Bambang Hendro Trisasongko: Faculty of Agriculture, IPB University, Bogor 16680, Indonesia
I Made Anom Sutrisna Wijaya: Department of Agricultural and Bio-System Engineering, Udayana University, Badung 803611, Indonesia
Masayasu Maki: Faculty of Food and Agricultural Sciences, Fukushima University, Fukushima 9061296, Japan
Chiharu Hongo: Center for Environmental Remote Sensing, Chiba University, Chiba 2638522, Japan
Koki Homma: Graduate School of Agricultural Science, Tohoku University, Sendai 9808572, Japan

Agriculture, 2022, vol. 13, issue 1, 1-14

Abstract: Drought is increasingly threatening smallholder farmers in Southeast Asia. The crop insurance system is one of the promising countermeasures that was implemented in Indonesia in 2015. Because the damage assessment in the present system is conducted through direct investigations based on appearance, it is not objective and needs a long time to cover large areas. In this study, we investigated a rapid assessment method for paddy fields using a vegetation index (VI) taken by an unmanned aerial vehicle (UAV) with a multispectral camera in 2019 and 2021. Then, two ways of assessment for drought damage were tested: linear regression (LR) based on a visually assessed drought level (DL), and k-means clustering without an assessed DL. As a result, EVI2 could represent the damage level, showing the tendency of the decrease in the value along with the increasing DL. The estimated DL by both methods mostly coincided with the assessed DL, but the concordance rates varied depending on the locations and the number of assessed fields. Differences in the growth stage and rice cultivars also affected the results. This study revealed the feasibility of the UAV-based rapid and objective assessment method. Further data collection and analysis would be required for implementation in the future.

Keywords: crop insurance; drought; Indonesia; rice; unmanned aerial vehicle; vegetation index (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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