Estimation of Aboveground Rice Biomass by Unmanned Aerial Vehicle Imaging

SHU Shifu; LI Yanda; CAO Zhongsheng; SUN Binfeng; YE Chun; WU Luofa; ZHU Yan; DING Yanfeng; HE Yong

doi:10.19303/j.issn.1008-0384.2022.007.002

Volume 37 Issue 7

Jul. 2022

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Article Navigation > Fujian Journal of Agricultural Sciences > 2022 > 37(7): 824-832

SHU S F, LI Y D, CAO Z S, et al. Estimation of Aboveground Rice Biomass by Unmanned Aerial Vehicle Imaging [J]. Fujian Journal of Agricultural Sciences，2022，37（7）：824−832 doi: 10.19303/j.issn.1008-0384.2022.007.002

Citation:

SHU S F, LI Y D, CAO Z S, et al. Estimation of Aboveground Rice Biomass by Unmanned Aerial Vehicle Imaging [J]. Fujian Journal of Agricultural Sciences，2022，37（7）：824−832 doi: 10.19303/j.issn.1008-0384.2022.007.002

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Estimation of Aboveground Rice Biomass by Unmanned Aerial Vehicle Imaging

doi: 10.19303/j.issn.1008-0384.2022.007.002

1.
Institute of Agricultural Engineering, Jiangxi Academy of Agricultural Sciences/Jiangxi Province Engineering Research Center of Intelligent Agricultural Machinery Equipment/Jiangxi Province Engineering Research Center of Information Technology in Agriculture, Nanchang, Jiangxi　330200, China
2.
Nanjing Agricultural University, Nanjing, Jiangsu　210095, China
3.
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang　310029, China

Received Date: 2022-01-08
Rev Recd Date: 2022-06-28

Available Online: 2022-08-07

Publish Date: 2022-07-28

Abstract

Abstract

Objective Feasibility of using images generated by unmanned aerial vehicle (UAV) to estimate the aboveground biomass (AGB) on a rice field was evaluated for crop production prediction. Methods On fields of two different varieties of rice fertilized with 4 varied nitrogen applications, AGB of rice plants at tillering, booting, and full heading stages were recorded by using the UAV imaging technology. Data on color and texture measurements were extracted from the images to correlate with corresponding AGB. A mathematic model was constructed, tested, and validated for prediction accuracy. Result On color, the red and blue differentiation (r-b) of the images highly correlated with the AGB; on texture, it was the G-mean. A prediction model was thus obtained for the entire growth period as y=2 544.507+5 054.243x₁−145.543x₂−556.553x₁x₂+27 379.41x₁²+3.927x₂² , which had a correlation coefficient (R²) of 0.920 2 and a test determination coefficient of 0.911 2. Conclusion The prediction model based on r-b and G-mean derived from the UAV images performed satisfactorily in monitoring the AGB for the entire growth period of rice in the field. It was conceivably applicable for the farming operation and crop management.
- Rice,
- aboveground biomass,
- UAV image,
- estimation model

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References(24)

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