Wheat Biomass Estimation in Different Growth Stages Based on Color and Texture Features of UAV Images

doi:10.12133/j.smartag.SA202202004

Abstract

Abstract:

In order to realize the rapid and non-destructive monitoring of wheat biomass, field wheat trials were conducted based on different densities, nitrogen fertilizers and varieties, and unmanned aerial vehicle (UAV) was used to obtain RGB images in the pre-wintering stage, jointing stage, booting stage and flowering stage of wheat. The color and texture feature indices of wheat were obtained using image processing, and wheat biomass was obtained by manual field sampling in the same period. Then the relationship between different color and texture feature indices and wheat biomass was analyzed to select the suitable feature index for wheat biomass estimation. The results showed that there was a high correlation between image color index and wheat biomass in different stages, the values of r were between 0.463 and 0.911 (P<0.05). However, the correlation between image texture feature index and wheat biomass was poor, only 5 index values reached significant or extremely significant correlation level. Based on the above results, the color indices with the highest correlation to wheat biomass or the combining indices of color and texture features in different growth stages were used to construct estimation model of wheat biomass. The models were validated using independently measured biomass data, and the correlation between simulated and measured values reached the extremely significant level (P<0.01), and root mean square error (RMSE) was smaller. The R² of color index model in the four stages were 0.538, 0.631, 0.708 and 0.464, and RMSE were 27.88, 516.99, 868.26 and 1539.81 kg/ha, respectively. The R² of the model combined with color and texture index were 0.571, 0.658, 0.753 and 0.515, and RMSE were 25.49, 443.20, 816.25 and 1396.97 kg/ha, respectively. This indicated that the estimated results using the models were reliable and accurate. It also showed that the estimation models of wheat biomass combined with color and texture feature indices of UAV images were better than the single color index models.

Key words: wheat, UAV?image, color?index, texture?feature?index, biomass, texture index

CLC Number:

S512

DAI Mian, YANG Tianle, YAO Zhaosheng, LIU Tao, SUN Chengming. Wheat Biomass Estimation in Different Growth Stages Based on Color and Texture Features of UAV Images[J]. Smart Agriculture, 2022, 4(1): 71-83.

References

1	WANG E. Study on remote sensing monitoring of maize growth and biomass[D]. Hefei: Anhui Agricultural University, 2018.
2	FAN Y, GONG Z, ZHAO W, et al. Study on vegetation biomass inversion method based on hyperspectral remote sensing[J]. Journal of Hebei Normal University(Natural Science Edition), 2016, 3: 267-271.
3	JIMENEZ-SIERRA D, CORREA E, BENíTEZ-RESTREPO H, et al. Novel feature-extraction methods for the estimation of above-ground biomass in rice crops[J]. Sensors, 2021, 21(13): 1-14.
4	HOU X, NIU Z, HUANG N, et al. The hyperspectral remote sensing estimation models of total biomass and true LAI of wheat[J]. Remote Sensing for Land & Resources, 2012, 24(4): 30-35.
5	LIU Q, PENG D, TU Y, et al. Estimating forest biomass by partial least squares regression[J]. Journal of Northeast Forestry University, 2014, 7: 44-47.
6	YUE J, ZHOU C, GUO W, et al. Estimation of winter-wheat above-ground biomass using the wavelet analysis of unmanned aerial vehicle-based digital images and hyperspectral crop canopy images[J]. International Journal of Remote Sensing, 2021, 42(5): 1602-1622.
7	LI J, SHI Y, VEERANAMPALAYAM-SIVAKUMAR A N, et al. Elucidating sorghum biomass, nitrogen and chlorophyll contents with spectral and morphological traits derived from unmanned aircraft system[J]. Frontiers in Plant Science, 2018, 9: 1-12.
8	GU Z, JU W, LI L, et al. Using vegetation indices and texture measures to estimate vegetation fractional coverage (VFC) of planted and natural forests in Nanjing city, China[J]. Advances in Space Research, 2013, 51(7): 1186-1194.
9	SARKER L R, NICHOL J E. Improved forest biomass estimates using ALOS AVNIR-2 texture indices[J]. Remote Sensing of Environment, 2011, 115(4): 968-977.
10	CAO Q, XU D, JU H. The biomass estimation of mangrove community based on the textural features and spectral information of TM images[J]. Forest Resources Management, 2010, 6: 102-108.
11	MU Q, GAO Z, BAO Y, et al. Estimation of sparse vegetation biomass based on grey-level co-occurrence matrix of vegetation indices[J]. Remote Sensing Information, 2016, 1: 58-63.
12	LIU C, YANG G, LI Z, et al. Biomass estimation in winter wheat by UAV spectral information and texture information fusion[J]. Scientia Agricultura Sinica, 2018, 51(16): 3060-3073.
13	LEE K J, LEE B W. Estimation of rice growth and nitrogen nutrition status using color digital camera image analysis[J]. European Journal of Agronomy, 2013, 48(3), 57-65.
14	WANG Y, WANG D, ZHANG G, et al. Estimating nitrogen status of rice using the image segmentation of G-R thresholding method[J]. Field Crops Research, 2013, 149(149): 33-39.
15	WANG J, ZHOU Q, SHANG J, et al. UAV- and machine learning-based retrieval of wheat SPAD values at the overwintering stage for variety screening[J]. Remote sensing, 2021, 13: 1-19.
16	YUE J, FENG H, LI Z, et al. Mapping winter-wheat biomass and grain yield based on a crop model and UAV remote sensing[J]. International Journal of Remote Sensing, 2021, 42(5): 1577-1601.
17	SHAN C, LIAO S, GONG Y, et al. Application of digital image processing for determination of vertical distribution of biomass in the canopy of winter wheat[J]. Acta Agronomica Sinica, 2007, 33(3): 419-424.
18	CHEN W. Evsluation of seed emergence unifermity of wheat based on UAV image[D]. Yangzhou: Yangzhou Univefrsity, 2018.
19	GITELSON A A, STARK R, GRITS U, et al. Vegetation and soil lines in visible spectral space: A concept and technique for remote estimation of vegetation fraction[J]. International Journal of Remote Sensing, 2002, 23(13): 2537-2562.
20	MAO D, WU X, DEPPONG C, et al. Negligible role of antibodies and C5 in pregnancy loss associated exclusively with C3-dependent mechanisms through complement alternative pathway[J]. Immunity, 2003, 19(6): 813-822.
21	WOEBBECKE D M, MEYER G E, BARGEN K VON, et al. Plant species identification, size, and enumeration using machine vision techniques on near-binary images[J]. Proceedings of SPIE-The International Society for Optical Engineering, 1993, 1836: 1-12.
22	HUNT JR E R, DAUGHTRY C S T, EITEL J U H, et al. Remote sensing leaf chlorophyll content using a visible band index[J]. Agronomy Journal, 2011, 103(4): 1090-1099.
23	TUCKER P W, HAZEN E E, COTTON F A. Staphylococcal nuclease reviewed: A prototypic study in contemporary enzymology[J]. Molecular & Cellular Biochemistry, 1979, 23(2): 67-86.
24	BENDIG J, YU K, AASEN H, et al. 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 & Geoinformation, 2015, 39: 79-87.
25	HARALICK R M, SHANMUGAM K, DINSTEIN I. Textural features for image classification[J]. IEEE Transactions on Systems Man & Cybernetics, 1973, 3(6): 610-621.
26	HUNT JR E R, HIVELY W D, FUJIKAWA S, et al. Acquisition of NIR-Green-Blue digital photographs from unmanned aircraft for crop monitoring[J]. Remote Sensing, 2010, 2(1): 290-305.
27	LU N, ZHOU J, HAN Z, et al. Improved estimation ofaboveground biomass in wheat from RGB imagery and point cloud data acquired withalow?cost unmanned aerial vehicle system[J]. Plant Methods, 2019, 15: 1-16.
28	DUAN B, FANG S, ZHU R, et al. Remote estimation of rice yield with unmanned aerial vehicle (UAV) data and spectral mixture analysis[J]. Frontiers in Plant Science, 2019,10: 1-14.
29	ZHOU Y, WANG D, CHEN C, et al. Prediction of wheat yield based on color index and texture feature index of UAV RGB image[J]. Journal of Yangzhou University (Agricultural and Life Science Edition), 2021, 42(3): 110-116.

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