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Accurate estimation of sorghum crop water content under different water stress levels using machine learning and hyperspectral data

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Abstract

This study investigates the effects of different water stress levels on spectral information, leaf area index (LAI), and the performance of three machine learning (ML) algorithms in estimating crop water content (CWC) of sorghum. The results show that the spectral reflectance of sorghum varies with growth stage and irrigation treatment, but consistent patterns are observed for each treatment. The LAI of sorghum gradually increased throughout the growth stages, with the most significant variation observed during the flowering stage. In this study, three machine learning-based regression models, namely, extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM), were utilized to estimate sorghum CWC using hyperspectral measurements. Recursive feature elimination (RFE) method was used to select the optimal spectral reflectance wavelengths for the ML models, and principal component analysis (PCA) was used to reduce the dimensionality of the hyperspectral data. The results indicated that the RF model achieved the highest R2 (0.90) and lowest of RMSE (56.05) value using selected wavelengths, while the XGBoost model demonstrated superior accuracy and reliability in estimating CWC using dimensionality-reduced hyperspectral data (r = 0.96, RMSE = 45.77). Also, the study highlights the importance of vegetation index (VI) in CWC estimate. Some VIs, such as NDVI and MSAVI, performed poorly, while others, such as CL_Rededge and EVI, performed better. The study provides valuable insights into the effects of water stress levels on spectral information, LAI, and the performance of ML algorithms in estimating the CWC of sorghum. The findings have significant implications for precision agriculture, as accurate and reliable estimates of CWC can help farmers optimize irrigation and fertilizer applications, leading to improved crop yields and resource efficiency.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Funding

This study was supported by The Scientific and Technological Research Council of Turkey (118O831).

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Authors and Affiliations

  1. Department of Biosystem Engineering, Faculty of Agriculture, Düzce University, Düzce, Turkey

    Emre Tunca

  2. Department of Agricultural Structures and Irrigation, Faculty of Agriculture, Ondokuz Mayıs University, Samsun, Turkey

    Eyüp Selim Köksal & Sakine Çetin Taner

  3. Department of Field Crops, Faculty of Agriculture, Ondokuz Mayıs University, Samsun, Turkey

    Elif Öztürk & Hasan Akay

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  1. Emre Tunca
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  2. Eyüp Selim Köksal
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  3. Elif Öztürk
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Contributions

Emre Tunca, designed the study, data collection, analyzed the data, and wrote the manuscript. Eyüp Selim Köksal, conceived the research project, participated in the data collection and analysis, and provided critical feedback on the manuscript. Elif Öztürk, contributed to the data collection. Hasan Akay and Sakine Çetin Taner assisted in the interpretation of the results. All authors reviewed and approved the final version of the manuscript.

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Correspondence to Emre Tunca.

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Tunca, E., Köksal, E.S., Öztürk, E. et al. Accurate estimation of sorghum crop water content under different water stress levels using machine learning and hyperspectral data. Environ Monit Assess 195, 877 (2023). https://doi.org/10.1007/s10661-023-11536-8

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