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Link to original content: https://doi.org/10.1007/s11042-023-17593-y
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Analysis of visible–near infrared spectral characteristics for water layer management of rice based on the big data platform

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Abstract

The objective of this study was to evaluate the potential of big data technology for spectral feature extraction rice plants under different water management. In this study, we performed reflectance spectral analysis of rice grown under different water management schedules based on big data and machine learning approaches. We applied water management at the tillering, jointing, and heading stages of rice growth and collected visible to near-infrared reflectance spectra to analyze the spectral characteristics of rice grown under different water management schedules. Then, we constructed a data mining model based on the spectral analysis results using the Hadoop and Spark frameworks, and analyzed the characteristic bands of rice grown under each schedule using a parallel machine learning algorithm run in both local and cluster modes. The ChiSqSelector algorithm showed characteristic bands in the range of 400–1000 nm, with bands at approximately 474, 558, 632, 735, and 855 nm for water management in the tillering stage; 472, 551, 627, 721, and 843 nm in the jointing stage; and 471, 545, 642, 725, and 849 nm in the heading stage. By contrast, the UnivariateFeatureSelector algorithm showed characteristic bands at approximately 462, 665, 755, 833, and 937 nm in the tillering stage; 475, 671, 744, 848, and 932 nm in the jointing stage; and 470, 678, 757, 857, and 942 nm in the heading stage. These results demonstrate the feasibility and efficiency of applying big data and data mining technologies for spectral analysis of rice grown under different water management schedules.

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Acknowledgements

The authors gratefully acknowledge the financial support from the National Key R&D Program of China (No. 2016YFD0300604, 2017YFD0300409-4). In part by the Introduction of talent research fund (YK20-05-01, YK20-05-06). In part by the Key Research and Development Program of Jiangsu Province under Grant BE2022351 (Modern Agriculture). In part by the Jiangsu Agricultural Science and Technology Innovation Found (No. CX(21)2042). In part by the Science and Technology Development Center Project of the Ministry of Education of China (No. 2020HYB02005). In part by the Jiangsu Industrial Software Engineering Technology Research and Development Center Project(No. ZK20-04-12).

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Authors and Affiliations

  1. School of Computer and Software, Nanjing Vocational University of Industry Technology, Nanjing, 210023, Jiangsu, China

    Ji’An Xia, WenTao Mu, RongWang Xu & WangHao Yuan

  2. Institute of Agricultural Information, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, Jiangsu, China

    WeiXin Zhang, WenYu Zhang, DaoKuo Ge, Qian Zhang, SiJun Ge & HongXin Cao

  3. School of Agricultural Engineering, Jiangsu University, Zhenjiang, 212013, Jiangsu, China

    WeiXin Zhang & WenYu Zhang

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  1. Ji’An Xia
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  3. WenYu Zhang
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Contributions

Xia Ji’An: Writing—original draft. WenYu Zhang: Methodology. WeiXin Zhang: Formal analysis. Mu WenTao, Xu RongWang, Yuan WangHao: Software. Ge DaoKuo, Zhang Qian, Ge SiJun: Investigation. HongXin Cao: Corresponding author.

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Correspondence to HongXin Cao.

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No conflict of interest exits in the submission of this manuscript (“Analysis of visible–near infrared spectral characteristics for water layer management of rice based on the big data platform”), and manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere. All the authors listed have approved the manuscript that is enclosed.

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Xia, J., Zhang, W., Zhang, W. et al. Analysis of visible–near infrared spectral characteristics for water layer management of rice based on the big data platform. Multimed Tools Appl 83, 53279–53292 (2024). https://doi.org/10.1007/s11042-023-17593-y

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