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Journal of Integrative Agriculture
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Investigating the impact of hyperspectral reconstruction techniques on the quantitative inversion of rice physiological parameters: A case study using the MST++ model
Weiguang Yang1, 4, 5*, Bin Zhang3*, Weicheng Xu3*, Shiyuan Liu2, 4, 5, Yubin Lan1, 4, 5#, Lei Zhang2, 4, 5#

1 College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China

2 College of Agriculture, South China Agricultural University, Guangzhou 510642, China 

3 Guangdong Key Laboratory of New Technology for Rice Breeding, Rice Research Institute of Guangdong Academy of Agricultural Science, Guangzhou 510640, China

4 Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China 

5 National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangdong, China 

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摘要  定量反演是遥感科学中的一个重要研究内容。基于可见光的高光谱重建技术的发展,为农业中的低成本、高精度遥感反演打开了新的前景。本研究旨在评估高光谱重建技术在农业遥感应用中的有效性。使用MST++高光谱重建模型重建了高光谱图像,并就它们与生理参数的相关性、单一特征建模的准确性以及组合特征建模的准确性,与原始可见光图像进行了比较。结果显示,与可见光图像相比,重建数据与生理参数的相关性更强,无论是单一特征还是组合特征反演模式,准确性都有所提高。然而,与多光谱传感器相比,高光谱重建在反演模型准确性上提供的改进有限。结果表明,对于不易直接观察的生理参数,通过高光谱重建技术对可见光数据进行深入特征挖掘,可以提高反演模型的准确性。适当的特征选择和简单模型更适合传统农艺小区实验的遥感反演任务。为了加强高光谱重建技术在农业遥感中的应用,需要进一步发展包括更广泛的波长范围和更多样的农业场景。

Abstract  Quantitative inversion is a significant topic in remote sensing science.  The development of visible light-based hyperspectral reconstruction techniques has opened up novel prospects for low-cost, high-precision remote sensing inversion in agriculture.  The aim of this study was to assesses the effectiveness of hyperspectral reconstruction technology in agricultural remote sensing applications.  Hyperspectral images were reconstructed using the MST++ hyperspectral reconstruction model and compared with the original visible light images in terms of their correlation with physiological parameters, the accuracy of single-feature modeling, and the accuracy of combined feature modeling.  The results showed that compared to the visible light image, the reconstructed data exhibited a stronger correlation with physiological parameters, and the accuracy was improved in both the single-feature and the combined feature inversion mode.  However, compared to multispectral sensors, hyperspectral reconstruction provided limited improvement on the inversion model accuracy.  It was concluded that for physiological parameters that are not easy to be directly observed, deep mining of features in visible light.   data through hyperspectral reconstruction technology can improve the accuracy of the inversion model.  Appropriate feature selection and simple models are more suitable for the remote sensing inversion task of traditional agronomic plot experiments. To strengthen the application of hyperspectral reconstruction technology in agricultural remote sensing, further development is necessary with broader wavelength ranges and more diverse agricultural scenes.
Keywords:  multistage spectral-wise transformer       hyperspectral reconstruction              rice              dry matter content              height  
Online: 26 April 2024  
Fund: This work was supported by the Laboratory of Lingnan Modern Agriculture Project, China (NT2021009), the China Agriculture Research System (CARS-15-22), the Key-Area Research and Development Program of Guangdong Province, China (2019B020214003), the Guangdong Technical System of Peanut and Soybean Industry, China (2019KJ136-05), and the 111 Project (D18019).
About author:  Weiguang Yang, E-mail: yangweiguang@stu.scau.edu.cn; Bin Zhang, E-mail:zhangbin@gdaas.cn; Weicheng Xu, E-mail: xuweicheng@gdaas.cn; #Correspondence Lei Zhang, E-mail: zhanglei@scau.edu.cn; Yubin Lan, E-mail: ylan@scau.edu.cn * These authors contribute equally to these studies.

Cite this article: 

Weiguang Yang, Bin Zhang, Weicheng Xu, Shiyuan Liu, Yubin Lan, Lei Zhang. 2024. Investigating the impact of hyperspectral reconstruction techniques on the quantitative inversion of rice physiological parameters: A case study using the MST++ model. Journal of Integrative Agriculture, Doi:10.1016/j.jia.2024.03.082


Albergel C, Dorigo W, Balsamo G, Muñoz-Sabater J, de Rosnay P, Isaksen L, Brocca L, de Jeu R, Wagner W. 2013. Monitoring multi-decadal satellite earth observation of soil moisture products through land surface reanalyses. Remote Sensing of Environment, 138, 77-89.

Al-Saddik H, Laybros A, Billiot B, Cointault F. 2018. Using image texture and spectral reflectance analysis to detect yellowness and esca in grapevines at leaf-level. Remote Sensing, 10, 618.

Berger K, Atzberger C, Danner M, D Urso G, Mauser W, Vuolo F, Hank T. 2018. Evaluation of the prosail model capabilities for future hyperspectral model environments: a review study. Remote Sensing, 10, 85.

Berger K, Verrelst J, Féret J, Wang Z, Wocher M, Strathmann M, Danner M, Mauser W, Hank T. 2020. Crop nitrogen monitoring: recent progress and principal developments in the context of imaging spectroscopy missions. Remote Sensing of Environment, 242, 111758.

Cai Y, Lin J, Hu X, Wang H, Yuan X, Zhang Y, Timofte R, Van Gool L. 2022. Mask-guided spectral-wise transformer for efficient hyperspectral image reconstructionProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Ithaca, pp. 17481-17490.

Cai Y, Lin J, Lin Z, Wang H, Zhang Y, Pfister H, Timofte R, Van Gool L. 2022. Mst++: multi-stage spectral-wise transformer for efficient spectral reconstructionProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, IEEE, Ithaca, pp. 745-755.

Chen T, Yang W, Zhang H, Zhu B, Zeng R, Wang X, Wang S, Wang L, Qi H, Lan Y, Zhang L. 2020. Early detection of bacterial wilt in peanut plants through leaf-level hyperspectral and unmanned aerial vehicle data. Computers and Electronics in Agriculture, 177, 105708.

Chen W, Lin Y, Ng F, Liu C, Lin Y. 2020. Ricetalk: rice blast detection using internet of things and artificial intelligence technologies. Ieee Internet of Things Journal, 7, 1001-1010.

Danner M, Berger K, Wocher M, Mauser W, Hank T. 2021. Efficient rtm-based training of machine learning regression algorithms to quantify biophysical & biochemical traits of agricultural crops. Isprs Journal of Photogrammetry and Remote Sensing, 173, 278-296.

Deng L, Sun J, Chen Y, Lu H, Duan F, Zhu L, Fan T. 2021. M2h-net: a reconstruction method for hyperspectral remotely sensed imagery. Isprs Journal of Photogrammetry and Remote Sensing, 173, 323-348.

Fitzgerald G, Rodriguez D, O Leary G. 2010. Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index—the canopy chlorophyll content index (ccci). Field Crops Research, 116, 318-324.

Fu J, Liu J, Zhao R, Chen Z, Qiao Y, Li D. 2022. Maize disease detection based on spectral recovery from rgb images. Frontiers in Plant Science, 13, 1056842.

Gong L, Zhu C, Luo Y, Fu X. 2023. Spectral reflectance reconstruction from red-green-blue (rgb) images for chlorophyll content detection. Applied Spectroscopy, 77, 200-209.

Han S, Zhao Y, Cheng J, Zhao F, Yang H, Feng H, Li Z, Ma X, Zhao C, Yang G. 2022. Monitoring key wheat growth variables by integrating phenology and uav multispectral imagery data into random forest model. Remote Sensing, 14, 3723.

Haralick R M, Shanmugam K, Dinstein I. 1973. Textural features for image classification. Ieee Transactions On Systems, Man, and Cybernetics, SMC-3, 610-621.

Islam Elmanawy A, Sun D, Abdalla A, Zhu Y, Cen H. 2022. Hsi-pp: a flexible open-source software for hyperspectral imaging-based plant phenotyping. Computers and Electronics in Agriculture, 200, 107248.

Liu D, Yang F, Liu S. 2021. Estimating wheat fractional vegetation cover using a density peak k-means algorithm based on hyperspectral image data. Journal of Integrative Agriculture, 20, 2880-2891.

Liu L, Li W, Shi Z, Zou Z. 2022. Physics-informed hyperspectral remote sensing image synthesis with deep conditional generative adversarial networks. Ieee Transactions On Geoscience and Remote Sensing, 60, 1-15.

Liu S, Zhang B, Yang W, Chen T, Zhang H, Lin Y, Tan J, Li X, Gao Y, Yao S, Lan Y, Zhang L. 2023. Quantification of physiological parameters of rice varieties based on multi-spectral remote sensing and machine learning models. Remote Sensing, 15, 453.

Liu W, Deng Y, Hussain S, Zou J, Yuan J, Luo L, Yang C, Yuan X, Yang W. 2016. Relationship between cellulose accumulation and lodging resistance in the stem of relay intercropped soybean [ glycine max (l.) Merr.]. Field Crops Research, 196, 261-267.

Nagasubramanian K, Jones S, Sarkar S, Singh A K, Singh A, Ganapathysubramanian B. 2018. Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems. Plant Methods, 14, 86.

Ojala T, Pietikainen M, Harwood D. 1994. Performance evaluation of texture measures with classification based on kullback discrimination of distributionsProceedings of 12th International Conference on Pattern Recognition. 1, IEEE, Israel. pp. 582-585.

Ojala T, Pietikäinen M, Harwood D. 1996. A comparative study of texture measures with classification based on featured distributions. Pattern Recognition, 29, 51-59.

Pei S, Zeng H, Dai Y L, Bai W, Fan J. 2023. Nitrogen nutrition diagnosis for cotton under mulched drip irrigation using unmanned aerial vehicle multispectral images. Journal of Integrative Agriculture, 22, 2536-2552.

Qi H, Zhu B, Kong L, Yang W, Zou J, Lan Y, Zhang L. 2020. Hyperspectral inversion model of chlorophyll content in peanut leaves. Applied Sciences, 10, 2259.

Sousa J J, Toscano P, Matese A, Di Gennaro S F, Berton A, Gatti M, Poni S, Pádua L, Hruška J, Morais R, Peres E. 2022. Uav-based hyperspectral monitoring using push-broom and snapshot sensors: a multisite assessment for precision viticulture applications. Sensors, 22, 6574.

Sun D, Xu Y, Cen H. 2022. Optical sensors: deciphering plant phenomics in breeding factories. Trends in Plant Science, 27, 209-210.

Tahir M N, Lan Y, Zhang Y, Wang Y, Nawaz F, Shah M A A, Gulzar A, Qureshi W S, Naqvi S M, Naqvi S Z A. 2018. Real time estimation of leaf area index and groundnut yield using multispectral uav. International Journal of Precision Agricultural Aviation, 3, 1-6.

Tian L, Xue B, Wang Z, Li D, Yao X, Cao Q, Zhu Y, Cao W, Cheng T. 2021. Spectroscopic detection of rice leaf blast infection from asymptomatic to mild stages with integrated machine learning and feature selection. Remote Sensing of Environment, 257, 112350.

Wang W, Zheng H, Wu Y, Yao X, Zhu Y, Cao W, Cheng T. 2022. An assessment of background removal approaches for improved estimation of rice leaf nitrogen concentration with unmanned aerial vehicle multispectral imagery at various observation times. Field Crops Research, 283, 108543.

Weiss M, Jacob F, Duveiller G. 2020. Remote sensing for agricultural applications: a meta-review. Remote Sensing of Environment, 236, 111402.

Xie Q, Dash J, Huete A, Jiang A, Yin G, Ding Y, Peng D, Hall C C, Brown L, Shi Y, Ye H, Dong Y, Huang W. 2019. Retrieval of crop biophysical parameters from sentinel-2 remote sensing imagery. International Journal of Applied Earth Observation and Geoinformation, 80, 187-195.

Xu W, Yang W, Chen S, Wu C, Chen P, Lan Y. 2020. Establishing a model to predict the single boll weight of cotton in northern xinjiang by using high resolution uav remote sensing data. Computers and Electronics in Agriculture, 179, 105762.

Yang W, Xu W, Wu C, Zhu B, Chen P, Zhang L, Lan Y. 2021. Cotton hail disaster classification based on drone multispectral images at the flowering and boll stage. Computers and Electronics in Agriculture, 180, 105866.

Yao X, Ren H, Cao Z, Tian Y, Cao W, Zhu Y, Cheng T. 2014. Detecting leaf nitrogen content in wheat with canopy hyperspectrum under different soil backgrounds. International Journal of Applied Earth Observation and Geoinformation, 32, 114-124.

Yi L, Lan Y, Kong H, Kong F, Han X. 2019. Exploring the potential of uav imagery for variable rate spraying in cotton defoliation application. International Journal of Precision Agricultural Aviation, 2, 42-45.

Yu F, Bai J, Jin Z, Guo Z, Yang J, Chen C. 2023. Combining the critical nitrogen concentration and machine learning algorithms to estimate nitrogen deficiency in rice from uav hyperspectral data. Journal of Integrative Agriculture, 22, 1216-1229.

Yu F, Du W, Guo Z, Zhou C, Wang D, Xu T. 2018. Uav hyperspectral inversion modeling of rice nitrogen content based on woa-elm. International Journal of Precision Agricultural Aviation, 2, 43-48.

Yuan H, Yang G, Li C, Wang Y, Liu J, Yu H, Feng H, Xu B, Zhao X, Yang X. 2017. Retrieving soybean leaf area index from unmanned aerial vehicle hyperspectral remote sensing: analysis of rf, ann, and svm regression models. Remote Sensing, 9, 309.

Yulong G, Changchun H, Yunmei L, Chenggong D, Lingfei S, Yuan L, Weiqiang C, Hejie W, Enxiang C, Guangxing J. 2022. Hyperspectral reconstruction method for optically complex inland waters based on bio-optical model and sparse representing. Remote Sensing of Environment, 276, 113045.

Zhao J, Kumar A, Banoth B N, Marathi B, Rajalakshmi P, Rewald B, Ninomiya S, Guo W. 2022. Deep-learning-based multispectral image reconstruction from single natural color rgb image—enhancing uav-based phenotyping. Remote Sensing, 14, 1272.

Zhen-Qi L, Yu-Long D, Han W, Ketterings Q M, Jun-Sheng L, Fu-Cang Z, Zhi-Jun L, Jun-Liang F. 2023. A double-layer model for improving the estimation of wheat canopy nitrogen content from unmanned aerial vehicle multispectral imagery. Journal of Integrative Agriculture, 22, 2248-2270.

Zhu Y, Abdalla A, Tang Z, Cen H. 2022. Improving rice nitrogen stress diagnosis by denoising strips in hyperspectral images via deep learning. Biosystems Engineering, 219, 165-176.

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