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Article

Efficient Damage Assessment of Rice Bacterial Leaf Blight Disease in Agricultural Insurance Using UAV Data

1
Center for Environmental Remote Sensing, Chiba University, Chiba 277-2835, Japan
2
NHK (Japan Broadcasting Corporation), Osaka 540-8501, Japan
3
Provincial Office of Food Crops and Horticulture of West Java Province, Bandung 40133, Indonesia
*
Author to whom correspondence should be addressed.
Agronomy 2024, 14(6), 1328; https://doi.org/10.3390/agronomy14061328
Submission received: 31 March 2024 / Revised: 6 June 2024 / Accepted: 14 June 2024 / Published: 19 June 2024

Abstract

In Indonesia, where the agricultural insurance system has been in full operation since 2016, a new damage assessment estimation formula for rice diseases was created through integrating the current damage assessment method and unmanned aerial vehicle (UAV) multispectral remote sensing data to improve the efficiency and precision of damage assessment work performed for the payments of insurance claims. The new method can quickly and efficiently output objective assessment results. In this study, UAV images and bacterial leaf blight (BLB) rice damage assessment data were acquired during the rainy and dry seasons of 2021 and 2022 in West Java, Indonesia, where serious BLB damage occurs every year. The six-level BLB score (0, 1, 3, 5, 7, and 9) and damage intensity calculated from the score were used as the BLB damage assessment data. The relationship between normalized UAV data, normalized difference vegetation index (NDVI), and BLB score showed significant correlations at the 1% level. The analysis of damage intensities and UAV data for paddy plots in all cropping seasons showed high correlation coefficients with the normalized red band, normalized near-infrared band, and NDVI, similar to the results of the BLB score analysis. However, for paddy plots with damage intensities of 70% or higher, the biased numbering of the BLB score data may have affected the evaluation results. Therefore, we conducted an analysis using an average of 1090 survey points for each BLB score and confirmed a strong relationship, with correlation coefficients exceeding 0.9 for the normalized red band, normalized near-infrared band, and NDVI. Through comparing the time required by the current assessment method with that required by the assessment method integrating UAV data, it was demonstrated that the evaluation time was reduced by more than 60% on average. We are able to propose a new assessment method for the Indonesian government to achieve complete objective enumeration.
Keywords: food security; remote sensing; agricultural insurance; pest and diseases food security; remote sensing; agricultural insurance; pest and diseases

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MDPI and ACS Style

Hongo, C.; Isono, S.; Sigit, G.; Tamura, E. Efficient Damage Assessment of Rice Bacterial Leaf Blight Disease in Agricultural Insurance Using UAV Data. Agronomy 2024, 14, 1328. https://doi.org/10.3390/agronomy14061328

AMA Style

Hongo C, Isono S, Sigit G, Tamura E. Efficient Damage Assessment of Rice Bacterial Leaf Blight Disease in Agricultural Insurance Using UAV Data. Agronomy. 2024; 14(6):1328. https://doi.org/10.3390/agronomy14061328

Chicago/Turabian Style

Hongo, Chiharu, Shun Isono, Gunardi Sigit, and Eisaku Tamura. 2024. "Efficient Damage Assessment of Rice Bacterial Leaf Blight Disease in Agricultural Insurance Using UAV Data" Agronomy 14, no. 6: 1328. https://doi.org/10.3390/agronomy14061328

APA Style

Hongo, C., Isono, S., Sigit, G., & Tamura, E. (2024). Efficient Damage Assessment of Rice Bacterial Leaf Blight Disease in Agricultural Insurance Using UAV Data. Agronomy, 14(6), 1328. https://doi.org/10.3390/agronomy14061328

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