Smartagb: Aboveground Biomass Estimation of Sorghum Based on Spatial Resolution, Machine Learning and Vegetation Index




Sorghum, Aboveground Biomass, UAV, Multispectral Image, Spatial Resolution


This work aims to explore the feasibility of predicting and estimating the aboveground biomass (AGB) of sorghum using multispectral images captured by UAVs, and clarify the quantitative relationship between vegetation index and sorghum AGB based on different spatial resolutions, and build an AGB estimation model based on UAV multispectral images and vegetation index under different spatial resolutions. Combining spatial resolution, vegetation index, and machine learning, a training set is used to train the model, and a verification set is used to verify the model to select the best prediction model corresponding to different spatial resolutions. The three best prediction models under three spatial resolutions are classic machine learning models. 1) when the spatial resolution is 0.017m, the model precision obtained from the random forest is R2=0.8961, MAE=26.4340, and RMSE=32.2459. 2) when the spatial resolution is 0.024m, the model accuracy obtained by the Lasso algorithm is R2=0.8826, MAE=31.106, and RMSE=40.2937; 3) when the spatial resolution is 0.030m, the model accuracy obtained by the decision tree algorithm is R2=0.8568, MAE=30.3373, and RMSE=40.8082; and 4) the model's accuracy decreases with the decrease of spatial resolution. The results show that the combination of spatial resolution, vegetation index, and machine learning algorithm is an effective, fast, and accurate prediction method.


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Wang Y , Feng D , Li S , et al. Review of Estimating Crop Biomass based on Remote Sensing Information[J]. Remote Sensing Technology & Application, 2016, 31(3).

Colomina I , Molina P . Unmanned aerial systems for photogrammetry and remote sensing: A review - ScienceDirect[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 92(2): 79-97. DOI:

Yang G J, Li C C, Wang Y J, Yuan H H, Feng H K, Xu B and Yang X D. The DOM generation and precise radiometric calibration of a UAV-mounted miniature snapshot hyperspectral imagery[J]. Remote Sensing, 2017, 9(7): 642. DOI:

Xu Y B. Envirotyping and its applications in crop science[J]. Scientia Agricultura Sinica, 2015, 48(17): 3354-3371.

Watanabe K, Guo W, Arai K, Takanashi H, Kajiya-Kanegae H, Kobayashi M, Yano K, Tokunaga T, Fujiwara T, Tsutsumi N and Iwata H. Highthroughput phenotyping of sorghum plant height using an unmanned aerial vehicle and its application to genomic prediction modeling[J]. Frontiers in Plant Science, 2017, 8: 421. DOI:

Singh S K, Houx III J H, Maw M J W and Fritschi F B. Assessment of growth, leaf N concentration and chlorophyll content of sweet sorghum using canopy reflectance[J]. Field Crops Research, 2017, 209: 47-57. DOI:

Yan G J, Hu R H, Luo J H, Mu X H, Xie D H and Zhang W M. Review of indirect methods for leaf area index measurement[J]. Journal of Remote Sensing, 2016, 20(5): 958-978.

Chen Z X, Ren J Q, Tang H J, Shi Y, Leng P, Liu J, Wang L M, Wu W B and Yao Y M. Progress and perspectives on agricultural remote sensing research and applications in China[J]. Journal of Remote Sensing, 2016, 20(5): 748-767.

Yao K, Guo X D, Nan Y, Li K, Jiang S F and Sun T T. Research progress of hyperspectral remote sensing monitoring of vegetation biomass assessment[J]. Science of Surveying and Mapping, 2016, 41(8): 48-53.

Liu Y, Huang J, Sun Q, Feng H K, Yang G J and Yang F Q. Estimation of plant height and aboveground biomass of potato based on UAV digital image[J]. National Remote Sensing Bulletin, 2021, 25(9): 2004-2014.

Zhang C B , Li W G, Zhang H, Li W, Ma T H, Zhang Z Z, Chen H, et al. Estimation of aboveground biomass of winter wheat based on remote sensing spectral index and neural network[J]. Journal of Triticeae Crops. 2022, 42(05): 631-639.

Candiago S, Remondino F, De Giglio M, Dubbini M and Gattelli M. Evaluating multispectral images and vegetation indices for precision farming applications from UAV images[J]. Remote Sensing, 2015, 7(4): 4026-4047. DOI:

ang G J, Liu J G, Zhao C J, Li Z H, Huang Y B, Yu H Y, Xu B, Yang X D, Zhu D M, Zhang X Y, Zhang R Y, Feng H K, Zhao X Q, Li Z H, Li H L and Yang H. Unmanned aerial vehicle remote sensing for field-based crop phenotyping: current status and perspectives[J]. Frontiers in Plant Science, 2017, 8: 1111. DOI:

Guo Q H, Su Y J, Hu T Y, Zhao X Q, Wu F F, Li Y M, Liu J, Chen L H, Xu G C, Lin G H, Zheng Y, Lin Y Q, Mi X C, Fei L and Wang X G. An integrated UAV-borne lidar system for 3D habitat mapping in three forest ecosystems across China[J]. International Journal of Remote Sensing, 2017, 38(8/10): 2954-2972. DOI:

Nie S, Wang C, Dong P L, Xi X H, Luo S Z and Zhou H Y. Estimating leaf area index of maize using airborne discrete-return LiDAR data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(7): 3259-3266. DOI:

Potgieter A B, George-Jaeggli B, Chapman S C, Laws K, Suárez Cadavid L A, Wixted J, Watson J, Eldridge M, Jordan D R and Hammer G L. Multi-spectral imaging from an unmanned aerial vehicle enables the assessment of seasonal leaf area dynamics of sorghum breeding lines[J]. Frontiers in Plant Science, 2017, 8: 1532. DOI:

Yuan H H, Yang G J, Li C C, Wang Y J, Liu J G, Yu H Y, Feng H K, Xu B, Zhao X Q and Yang X D. Retrieving soybean leaf area index from unmanned aerial vehicle hyperspectral remote sensing: analysis of RF, ANN, and SVM regression models[J]. Remote Sensing, 2017, 9(4): 309. DOI:

Zhang L X, Chen Y Q, Li Y X, Ma J C, Du K M, Zheng F X and Sun Z F. Estimating above ground biomass of winter wheat at early growth stages based on visual spectral[J]. Spectroscopy and Spectral Analysis, 2019, 39(8): 2501-2506.

Cheng Yifeng, Gong Lu, Zhang Xueni, et al. Study of the remote sensing cotton yield estimation model in Northern Xinjiang[J/OL]. Xinjiang Agricultural Science, 2012, 49(8): 1497-1502[2021-01-30].

Cui R X, Liu Y D and Fu J D. Estimation of winter wheat biomass using visible spectral and BP based artificial neural networks[J]. Spectroscopy and Spectral Analysis, 2015, 35(9): 2596-2601.

Tao H L, Xu L J, Feng H K, Yang G J, Yang X D, Miao M K and Dai Y. Estimation of plant height and biomass of winter wheat based on UAV digital image[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(19): 107-116.

Guo C F, Chen W J, Niu M Y, Zhang Z G, et al. Collaborative estimation of aboveground biomass in grassland based on multiple vegetation index models[J]. Agricultural Research in the Arid Areas, 2022, 40(04): 206-213.

LI Y D, YE C, CAO Z S, et al. Monitoring leaf nitrogen concentration and nitrogen accumulation of double cropping rice based on crop growth monitoring and diagnosis apparatus [J]. Chinese Journal of Applied Ecology, 2020, 31(9): 3040−3050. (in Chinese)

Tong X, Yang Z L, Zhang Y R, Wu Y C, Duan L M, et al. Estimation of Pasture Aboveground Biomass using Different Orders of Differential Hyperspectral Vegetation Indices[J]. Acta Prataculturae Sinica, 2022, 30(09): 2438-2448.

YE C, LIU Y, LI Y D, et al. Monitoring the nitrogen nutrition of early rice based on RGB color space[J]. Journal of China Agricultural University, 2020, 25(8): 25−34.

GUO H L, LI X, FU Y, et al. High-resolution leaf area index inversion based on the Kernel Ridge Regression algorithm and PROSAIL model. Acta Prataculturae Sinica, 2022, 31(12): 41−51.

WANG Xu, DENG Yushuai, LIAN Xuemeng, et al. Inversion of chlorophyll content in sugar beet canopy based on UAV multispectral technique[J]. Sugar Crops of China, 2022, 44(4): 36-42.

GOSWAMI S, GAMON J, VARGAS S, et al. Relationships of NDVI, Biomass, and Leaf Area Index (LAI) for six key plant species in Barrow, Alaska [J]. Peer J Preprints, 2015, 3: e913v1. DOI:

Gitelson A, Merzlyak M N. Quantitative estimation of chlorophyll-a using reflectance spectra: experiments with autumn chestnut and maple leaves[J]. J Photochem Photobiol B Biol, 1994, 22(3): 247-252. DOI:

Gitelson A A, Kaufman Y J, Stark R, et al. Novel algorithms for remote estimation of vegetation fraction[J/OL]. Remote Sensing of Environment, 2002, 80 (1): 76-87 [2021-01-30]. DOI:

BROGE N H , MORTENSEN J VBROGE N H. Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data[J]. Remote Sensing of Environment, 2002, 81(8): 45-47. DOI:

GITELSON A A, MERZLYAK M N. Remote estimation of chlorophyll content in higher plant leaves[J]. International Journal of Remote Sensing, 1997, 18( 12): 2691-2697. DOI:

Vincini M, Frazzi E, Alessio D. P.. A broad-band leaf chlorophyll vegetation index at the canopy scale[J]. Precision Agriculture, 2008, 9(5): 303-319. DOI:

Haboudane D, Miller J R, Elizabeth P, et al. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture[J]. Remote Sensing of Environment, 2003, 90(3): 337-352. DOI:

Bendig J, Yu K, Aasen H, Bolten A, Bennertz S, Broscheit J, Gnyp M L and Bareth G. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley[J]. International Journal of Applied Earth Observation and Geoinformation, 2015, 39: 79-87. DOI:

Kataoka T , Kaneko T , Okamoto H , et al. Crop growth estimation system using machine vision[C]// Advanced Intelligent Mechatronics, 2003. AIM 2003. Proceedings. 2003 IEEE/ASME International Conference on. IEEE, 2003: 1079-1083.

Wang Xiaoqin, Wang Miaomiao, Wang Shaoqiang, et al. Extraction of vegetation information from visible unmanned aerial vehicle images[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2015, 31(5): 152-159. (in Chinese with English abstract)

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.

Liu Y, Zhang H, Feng H K, Sun Q, Huang Y, Wang J J, Yang G J, et al. Estimation of potato aboveground biomass by UAV hyperspectral imaging[J]. Spectroscopy and Spectral Analysis, 2021, 41(09): 2657-2664.




How to Cite

Q. Liu, “Smartagb: Aboveground Biomass Estimation of Sorghum Based on Spatial Resolution, Machine Learning and Vegetation Index”, EAI Endorsed Trans IoT, vol. 9, no. 1, p. e1, Mar. 2023.

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