Original Articles Open access

Research on an automated mapping method for rice aboveground biomass based on low-altitude remote sensing

Authors

Published
15 September 2026
Issue
Early Access
Section
Original Articles
DOI
10.4081/jae.2026.2061

Abstract

Accurate monitoring of rice aboveground biomass (AGB) is crucial for guiding agricultural production management. This study focuses on high-precision estimation and automated mapping of rice AGB. Field experiments were conducted in Nanxun District, Huzhou City, Zhejiang Province. We collected UAV RGB and multispectral images, rice AGB, and plant height data. By integrating vegetation indices, texture features, and plant height information, the AGB estimation model was established using algorithms such as Stacking. A framework combining "SAM + MobileNetV3-Small classification" was proposed to achieve automated paddy field extraction and phenology recognition. The results demonstrate that the rice AGB prediction model based on the Stacking ensemble algorithm performed excellently. The introduction of plant height significantly improved model accuracy. For example, during the heading stage, R2 increased from 0.421 to 0.739, and RPIQ rose from 1.995 to 3.015. The automated paddy field extraction and phenology recognition framework developed in this study achieved a segmentation accuracy of 0.968 and a classification accuracy of 0.993 on the dataset used in this study, without requiring manual annotation. This research provides a technical reference for automated and high-precision mapping of rice AGB.

Keywords

Supporting Agencies

National Key Research and Development Program of China (Grant No 2022YFD2001801)

Data Availability Statement

The data used to support the findings of this study are available from the corresponding author upon request.

References

  1. Adeluyi O, Harris A, Foster T, Clay GD, 2022. Exploiting centimetre resolution of drone-mounted sensors for estimating mid-late season above ground biomass in rice. Eur J Agron 132:126411. DOI: https://doi.org/10.1016/j.eja.2021.126411
  2. Agrawal J, Arafat MY, 2024. Transforming farming: a review of AI-powered UAV technologies in precision agriculture. Drones 8:664. DOI: https://doi.org/10.3390/drones8110664
  3. Cano PB, Carcedo AJP, Hernandez CM, Gomez FM, Gimenez VD, Kyveryga PM, et al., 2025. Trends in agricultural technology: a review of US patents. Precis Agric 26:59. DOI: https://doi.org/10.1007/s11119-025-10257-x
  4. Cheng Q, Xu H, Fei S, Li Z, Chen Z, 2022. Estimation of maize LAI using ensemble learning and UAV multispectral imagery under different water and fertilizer treatments. Agriculture 12:1267. DOI: https://doi.org/10.3390/agriculture12081267
  5. Dreier A, Lopez G, Bajracharya R, Kuhlmann H, Klingbeil L, 2025. Structural wheat trait estimation using UAV-based laser scanning data: analysis of critical aspects and recommendations based on a case study. Precis Agric 26:18. DOI: https://doi.org/10.1007/s11119-025-10230-8
  6. Garcia AD, Islam S, Prudente VHR, Sanches ID, Cheng I, 2025. Irrigated rice-field mapping in Brazil using phenological stage information and optical and microwave remote sensing. Appl Comput Geosci 25:100223. DOI: https://doi.org/10.1016/j.acags.2025.100223
  7. Hu PC, Chapman SC, Zheng BY, 2021. Coupling of machine learning methods to improve estimation of ground coverage from unmanned aerial vehicle (UAV) imagery for high-throughput phenotyping of crops. Funct Plant Biol 48:766-779. DOI: https://doi.org/10.1071/FP20309
  8. Huang Y, Thomson SJ, Hoffmann WC, Lan Y, Fritz BK, 2013. Development and prospect of unmanned aerial vehicle technologies for agricultural production management. Int J Agric Biol Eng 6:1-10.
  9. Kroupova ZZ, Aulova R, Rumankova L, Bajan B, Cechura L, Simek P, et al., 2025. Drivers and barriers to precision agriculture technology and digitalisation adoption: meta-analysis of decision choice models. Precis Agric 26:17. DOI: https://doi.org/10.1007/s11119-024-10213-1
  10. Li B, Xu X, Zhang L, Han J, Bian C, Li G, et al., 2020. Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging. ISPRS J Photogramm Remote Sens 162:161-172. DOI: https://doi.org/10.1016/j.isprsjprs.2020.02.013
  11. Li J, Li J, Zhao D, Cao Q, Yu F, Cao Y, et al., 2025a. High-throughput method for improving rice AGB estimation based on UAV multi-source remote sensing image feature fusion and ensemble learning. Front Plant Sci 16:1576212. DOI: https://doi.org/10.3389/fpls.2025.1576212
  12. Li J, Wei Y, Wei T, He W, 2025b. A comprehensive deep-learning framework for fine-grained farmland mapping from high-resolution images. IEEE Trans Geosci Remote Sens 63:5601215. DOI: https://doi.org/10.1109/TGRS.2024.3515157
  13. Li W, He J, Yu M, Su X, Wang X, Zheng H, et al., 2024. Multisource remote sensing data-driven estimation of rice grain starch accumulation: leveraging matter accumulation and translocation characteristics. IEEE Trans Geosci Remote Sens 62:1-18. DOI: https://doi.org/10.1109/TGRS.2024.3500000
  14. Liu Y, Feng H, Yue J, Jin X, Fan Y, Chen R, et al., 2023. Improved potato AGB estimates based on UAV RGB and hyperspectral images. Comput Electron Agric 214:108260. DOI: https://doi.org/10.1016/j.compag.2023.108260
  15. Liu Y, Pei J, Zou Y, Tan S, He Y, Zheng X, et al., 2025. A novel Hybrid-DCNN-based framework for enhanced rice aboveground biomass estimation under limited samples. IEEE Trans Geosci Remote Sens 63:1-16. DOI: https://doi.org/10.1109/TGRS.2025.3544343
  16. Liu Y, Zhang T, Huang Y, Shi F, 2024. An edge-aware multitask network based on CNN and Transformer backbone for farmland instance segmentation. IEEE J Sel Top Appl Earth Obs Remote Sens 17:13765-13779. DOI: https://doi.org/10.1109/JSTARS.2024.3435425
  17. Maimaitijiang M, Sagan V, Sidike P, Maimaitiyiming M, Hartling S, Peterson KT, et al., 2019. Vegetation index weighted canopy volume model (CVMVI) for soybean biomass estimation from unmanned aerial system-based RGB imagery. ISPRS J Photogramm Remote Sens 151:27-41. DOI: https://doi.org/10.1016/j.isprsjprs.2019.03.003
  18. Meng L, Ming B, Liu Y, Nie CW, Fang L, Zhou LL, et al., 2025. Maize biomass estimation by integrating spectral, structural, and textural features from unmanned aerial vehicle data. Eur J Agron 168:127647. DOI: https://doi.org/10.1016/j.eja.2025.127647
  19. Niu YX, Song XY, Zhang LY, Xu LZ, Wang AC, Zhu QZ, 2025. Enhancing model accuracy of UAV-based biomass estimation by evaluating effects of image resolution and texture feature extraction strategy. IEEE J Sel Top Appl Earth Obs Remote Sens 18:878-891. DOI: https://doi.org/10.1109/JSTARS.2024.3501673
  20. Raouhi EM, Lachgar M, Hrimech H, Kartit A, 2023. Unmanned aerial vehicle-based applications in smart farming: a systematic review. Int J Adv Comput Sci Appl 14:1150-1165. DOI: https://doi.org/10.14569/IJACSA.2023.01406123
  21. Shi J, Yang K, Yuan N, Li Y, Ma L, Liu Y, et al., 2025. UAV-based rice aboveground biomass estimation using a random forest model with multi-organ feature selection. Eur J Agron 164:127529. DOI: https://doi.org/10.1016/j.eja.2025.127529
  22. Wang D, Li R, Zhu B, Liu T, Sun C, Guo W, 2023. Estimation of wheat plant height and biomass by combining UAV imagery and elevation data. Agriculture 13:9. DOI: https://doi.org/10.3390/agriculture13010009
  23. Xu F, Yao X, Zhang K, Yang H, Feng Q, Li Y, et al., 2024. Deep learning in cropland field identification: a review. Comput Electron Agric 222:109042. DOI: https://doi.org/10.1016/j.compag.2024.109042
  24. Xu L, Zhou L, Meng R, Zhao F, Lv Z, Xu B, et al., 2022. An improved approach to estimate ratoon rice aboveground biomass by integrating UAV-based spectral, textural and structural features. Precis Agric 23:1276-1301. DOI: https://doi.org/10.1007/s11119-022-09884-5
  25. Xu TY, Wang FM, Shi Z, Xie LL, Yao XP, 2023. Dynamic estimation of rice aboveground biomass based on spectral and spatial information extracted from hyperspectral remote sensing images at different combinations of growth stages. ISPRS J Photogramm Remote Sens 202:169-183. DOI: https://doi.org/10.1016/j.isprsjprs.2023.05.021
  26. Yang K, Mo J, Luo S, Peng Y, Fang S, Wu X, et al., 2023. Estimation of rice aboveground biomass by UAV imagery with photosynthetic accumulation models. Plant Phenomics 5:0056. DOI: https://doi.org/10.34133/plantphenomics.0056
  27. Zhang J, Yuan N, Liu Y, Wu X, Fang S, Peng Y, et al., 2026. Multi-temporal UAV rice AGB estimation with dynamic biomass allocation and interpretable deep learning. Eur J Agron 177:128096. DOI: https://doi.org/10.1016/j.eja.2026.128096
  28. Zhang PP, Lu B, Ge JY, Shang JL, Tan CW, Wang XY, et al., 2025. A sustainable learning framework: UAV-based oat chlorophyll monitoring using radiative transfer models and deep learning techniques. IEEE Trans Geosci Remote Sens 63:4416613. DOI: https://doi.org/10.1109/TGRS.2025.3597921
  29. Zhang RN, Jin SC, Wang Y, Zang JR, Wang Y, Zhao RF, et al., 2025. PhenoSR: enhancing organ-level phenotyping with super-resolution RGB UAV imagery for large-scale field experiments. ISPRS J Photogramm Remote Sens 228:582-602. DOI: https://doi.org/10.1016/j.isprsjprs.2025.07.025
  30. Zhu S, Zhang W, Yang T, Wu F, Jiang Y, Yang G, et al., 2024. Combining 2D image and point cloud deep learning to predict wheat above ground biomass. Precis Agric 25:3139-3166. DOI: https://doi.org/10.1007/s11119-024-10186-1

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