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Estimation of the Leaf Area Index of Winter Rapeseed Based on Hyperspectral and Machine Learning

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  • Wei Zhang

    (Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Yangling 712100, China
    Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Yangling 712100, China
    College of Water Resources and Architectural Engineering, Northwest A & F University, Yangling, 712100, China)

  • Zhijun Li

    (Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Yangling 712100, China
    Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Yangling 712100, China
    College of Water Resources and Architectural Engineering, Northwest A & F University, Yangling, 712100, China)

  • Yang Pu

    (Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Yangling 712100, China
    Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Yangling 712100, China
    College of Water Resources and Architectural Engineering, Northwest A & F University, Yangling, 712100, China)

  • Yunteng Zhang

    (Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Yangling 712100, China
    Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Yangling 712100, China
    College of Water Resources and Architectural Engineering, Northwest A & F University, Yangling, 712100, China)

  • Zijun Tang

    (Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Yangling 712100, China
    Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Yangling 712100, China
    College of Water Resources and Architectural Engineering, Northwest A & F University, Yangling, 712100, China)

  • Junyu Fu

    (Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Yangling 712100, China
    Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Yangling 712100, China
    College of Water Resources and Architectural Engineering, Northwest A & F University, Yangling, 712100, China)

  • Wenjie Xu

    (Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Yangling 712100, China
    Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Yangling 712100, China
    College of Water Resources and Architectural Engineering, Northwest A & F University, Yangling, 712100, China)

  • Youzhen Xiang

    (Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Yangling 712100, China
    Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Yangling 712100, China
    College of Water Resources and Architectural Engineering, Northwest A & F University, Yangling, 712100, China)

  • Fucang Zhang

    (Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Yangling 712100, China
    Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Yangling 712100, China
    College of Water Resources and Architectural Engineering, Northwest A & F University, Yangling, 712100, China)

Abstract

Leaf area index (LAI) is essential for evaluating crop growth and development. Destructive manual measurement methods mainly achieve traditional crop LAI acquisition. Due to the advantages of being fast and non-destructive, spectroscopy technology provides a feasible method for obtaining crop LAI. In order to achieve efficient acquisition of winter oilseed rape LAI, this study collected hyperspectral data and LAI data at the full-bloom stage of winter oilseed rape. It calculated the spectral indexes related to the LAI of the original spectrum and the first-order differential spectrum, respectively. The index with the highest correlation with the LAI of winter oilseed rape at the flowering stage was selected as the optimal spectral index for input. Subsequently, three machine learning methods, Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (RF), were used to construct the LAI model of winter oilseed rape, and the model was tested. The results show that the correlation coefficient between the spectral index calculated by the first-order differential processing of the original spectral data and the LAI of winter rapeseed is significantly improved compared with the original data. Among them, the spectral index NDVI with the best correlation coefficient with LAI can be obtained under the first-order differential: the correlation coefficient is 0.734, and the wavelength combination is 716 nm and 724 nm. At the same time, we found that when the input variables are the same, the RF model has higher estimation accuracy than the other models. The best estimation accuracy is obtained when the input variable is the first-order differential spectral index. The R 2 of the model validation set is 0.810, RMSE is 0.455 cm 2 /cm 2 , MRE is 10.465%, and the model accuracy is high. The results of this study can provide a theoretical basis for crop monitoring based on spectral technology and provide a theoretical basis for crop growth.

Suggested Citation

  • Wei Zhang & Zhijun Li & Yang Pu & Yunteng Zhang & Zijun Tang & Junyu Fu & Wenjie Xu & Youzhen Xiang & Fucang Zhang, 2023. "Estimation of the Leaf Area Index of Winter Rapeseed Based on Hyperspectral and Machine Learning," Sustainability, MDPI, vol. 15(17), pages 1-13, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:12930-:d:1226485
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    References listed on IDEAS

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    1. Tinghui Wu & Jian Yu & Jingxia Lu & Xiuguo Zou & Wentian Zhang, 2020. "Research on Inversion Model of Cultivated Soil Moisture Content Based on Hyperspectral Imaging Analysis," Agriculture, MDPI, vol. 10(7), pages 1-14, July.
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    Cited by:

    1. Mohamed Mouafik & Abdelghani Chakhchar & Mounir Fouad & Ahmed El Aboudi, 2024. "Remote Sensing Technologies for Monitoring Argane Forest Stands: A Comprehensive Review," Geographies, MDPI, vol. 4(3), pages 1-21, July.

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