Potato LAI estimation by fusing UAV multi-spectral and texture features

LI Jian; JIANG Hong; LUO Wenbin; MA Xia; ZHANG Yong

doi:10.7671/j.issn.1001-411X.202201002

Journal of South China Agricultural University > 2023 > 44(1): 93-101. > DOI: 10.7671/j.issn.1001-411X.202201002

LI Jian, JIANG Hong, LUO Wenbin, et al. Potato LAI estimation by fusing UAV multi-spectral and texture features[J]. Journal of South China Agricultural University, 2023, 44(1): 93-101. DOI: 10.7671/j.issn.1001-411X.202201002

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Potato LAI estimation by fusing UAV multi-spectral and texture features

1.
Academy of Digital China (Fujian), Fuzhou University/Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education/Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou 350108, China
2.
Crop Research Institue, Fujian Academy of Agricultural Sciences, Fuzhou 350013, China

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Received Date: January 03, 2022
Available Online: May 17, 2023

Graphical Abstract

Abstract

Abstract

Objective
Develop a method to improve the potato (Solanum tuberosum) leaf area index (LAI) estimation accuracy using the UAV multiple spectral wavebands and texture information.

Method
The DJI P4M drone was used to collect multispectral images of the southern winter potato at seedling period, budding period and tuber swelling period from February to April 2021. LAI data were measured by LAI-2000 canopy analyzer. The spectral and texture characteristics of images were extracted. The correlations between vegetation index, texture characteristics and LAI were analyzed. The selected characteristic variables were analyzed based on subset of adjusted R²_adj. The principal component analysis was used to fuse spectrum and texture features, and the principal component analysis-multiple linear regression (PCA-MLR) model was used to estimate potato LAI.

Result
From the seedling period to the tuber swelling period, the PCA-MLR estimation model was better than texture multiple linear regression (T-MLR) and vegetation index multiple linear regression (VI-MLR) model, with R² of 0.73, 0.59 and 0.66 respectively.

Conclusion
This study proposed a method of PCA-MLR to estimate the potato LAI and improve the levels of the potato growth monitoring and field management.
- UAV,
- Remote sensing,
- LAI,
- Multi-spectral,
- Texture feature,
- Potato

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References (23)

References

[1]	卢肖平. 马铃薯主粮化战略的意义、瓶颈与政策建议[J]. 华中农业大学学报(社会科学版), 2015(3): 1-7. doi: 10.13300/j.cnki.hnwkxb.2015.03.001
[2]	LUO S, HE Y, LI Q, et al. Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage[J]. Plant Methods, 2020, 16(1): 150. doi: 10.1186/s13007-020-00693-3.
[3]	常好雪, 蔡晓斌, 陈晓玲, 等. 基于实测光谱的植被指数对水稻叶面积指数的响应特征分析[J]. 光谱学与光谱分析, 2018, 38(1): 205-211.
[4]	孙越, 顾祝军, 李栋梁. 无人机与卫星影像的叶面积指数遥感反演研究[J]. 测绘科学, 2021, 46(2): 106-112. doi: 10.16251/j.cnki.1009-2307.2021.02.016
[5]	李月, 何宏昌, 王晓飞, 等. 农作物冠层光谱分析及反演技术综述[J]. 测绘通报, 2019(9): 13-17. doi: 10.13474/j.cnki.11-2246.2019.0277
[6]	HUNT E R, HIVELY W D, FUJIKAWA S J, et al. Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring[J]. Remote Sensing, 2010, 2(1): 290-305. doi: 10.3390/rs2010290
[7]	KAMAL M, SIDIK F, PRANANDA A R A, et al. Mapping leaf area index of restored mangroves using WorldView-2 imagery in Perancak Estuary, Bali, Indonesia[J]. Remote Sensing Applications: Society and Environment, 2021, 23: 100567. doi: 10.1016/j.rsase.2021.100567.
[8]	宋开山, 张柏, 王宗明, 等. 基于人工神经网络的大豆叶面积高光谱反演研究[J]. 中国农业科学, 2006, 39(6): 1138-1145. doi: 10.3321/j.issn:0578-1752.2006.06.007
[9]	刘畅, 杨贵军, 李振海, 等. 融合无人机光谱信息与纹理信息的冬小麦生物量估测[J]. 中国农业科学, 2018, 51(16): 3060-3073. doi: 10.3864/j.issn.0578-1752.2018.16.003
[10]	陈鹏, 冯海宽, 李长春, 等. 无人机影像光谱和纹理融合信息估算马铃薯叶片叶绿素含量[J]. 农业工程学报, 2019, 35(11): 63-74. doi: 10.11975/j.issn.1002-6819.2019.11.008
[11]	杨福芹, 冯海宽, 肖天豪, 等. 融合无人机影像光谱与纹理特征的冬小麦氮营养指数估算[J]. 农业现代化研究, 2020, 41(4): 718-726. doi: 10.13872/j.1000-0275.2020.0061
[12]	RYU C, SUGURI M, UMEDA M. Multivariate analysis of nitrogen content for rice at the heading stage using reflectance of airborne hyperspectral remote sensing[J]. Field Crops Research, 2011, 122(3): 214-224. doi: 10.1016/j.fcr.2011.03.013
[13]	刘昌华, 王哲, 陈志超等. 基于无人机遥感影像的冬小麦氮素监测[J]. 农业机械学报, 2018, 49(6): 207-214. doi: 10.6041/j.issn.1000-1298.2018.06.024
[14]	YUE J, YANG G, TIAN Q, et al. Estimate of winter-wheat above-ground biomass based on UAV ultrahigh-ground-resolution image textures and vegetation indices[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 150: 226-244. doi: 10.1016/j.isprsjprs.2019.02.022
[15]	TANAKA S, KAWAMURA K, MAKI M, et al. Spectral index for quantifying leaf area index of winter wheat by field hyperspectral measurements: A case study in Gifu prefecture, central Japan[J]. Remote Sensing, 2015, 7(5): 5329-5346. doi: 10.3390/rs70505329
[16]	王来刚, 徐建华, 贺佳, 等. 基于无人机遥感的玉米叶面积指数与产量估算[J]. 玉米科学, 2020, 28(6): 88-93. doi: 10.13597/j.cnki.maize.science.20200613
[17]	ZHANG J, WANG C, YANG C, et al. Assessing the effect of real spatial resolution of in situ UAV multispectral images on seedling rapeseed growth monitoring[J]. Remote Sensing, 2020, 12(7): 1207. doi: 10.3390/rs12071207.
[18]	李宗南. 冬小麦长势遥感监测指标研究[D]. 北京: 中国农业科学院, 2010.
[19]	LIANG L, DI L, ZHANG L, et al. Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method[J]. Remote Sensing of Environment, 2015, 165: 123-134. doi: 10.1016/j.rse.2015.04.032
[20]	夏天, 吴文斌, 周清波, 等. 冬小麦叶面积指数高光谱遥感反演方法对比[J]. 农业工程学报, 2013, 29(3): 139-147.
[21]	姚雄, 余坤勇, 刘健. 基于无人机多光谱遥感的马尾松林叶面积指数估测[J]. 农业机械学报, 2021, 52(7): 213-221. doi: 10.6041/j.issn.1000-1298.2021.07.022
[22]	ROUJEAN J L, BREON F M. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements[J]. Remote Sensing of Environment, 1995, 51(3): 375-384. doi: 10.1016/0034-4257(94)00114-3
[23]	周岑岑. 马铃薯生育期及形态建成的模拟研究[D]. 武汉: 华中农业大学, 2015.

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