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Prediction of macronutrients in plant leaves using chemometric analysis and wavelength selection

  • Soils, Sec 2 • Global Change, Environ Risk Assess, Sustainable Land Use • Research Article
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Abstract

Purpose

Fast and real-time prediction of leaf nutrient concentrations can facilitate decision-making for fertilisation regimes on farms and address issues raised with over-fertilisation. Cacao (Theobroma cacao L.) is an important cash crop and requires nutrient supply to maintain yield. This project aimed to use chemometric analysis and wavelength selection to improve the accuracy of foliar nutrient prediction.

Materials and methods

We used a visible-near infrared (400–1000 nm) hyperspectral imaging (HSI) system to predict foliar calcium (Ca), potassium (K), phosphorus (P) and nitrogen (N) of cacao trees. Images were captured from 95 leaf samples. Partial least square regression (PLSR) models were developed to predict leaf nutrient concentrations and wavelength selection was undertaken.

Results and discussion

Using all wavelengths, Ca (R2CV = 0.76, RMSECV = 0.28), K (R2CV = 0.35, RMSECV = 0.46), P (R2CV = 0.75, RMSECV = 0.019) and N (R2CV = 0.73, RMSECV = 0.17) were predicted. Wavelength selection increased the prediction accuracy of Ca (R2CV = 0.79, RMSECV = 0.27) and N (R2CV = 0.74, RMSECV = 0.16), while did not affect prediction accuracy of foliar K (R2CV = 0.35, RMSECV = 0.46) and P (R2CV = 0.75, RMSECV = 0.019).

Conclusions

Visible-near infrared HSI has a good potential to predict Ca, P and N concentrations in cacao leaf samples, but K concentrations could not be predicted reliably. Wavelength selection increased the prediction accuracy of foliar Ca and N leading to a reduced number of wavelengths involved in developed models.

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Acknowledgements

The authors acknowledge Professor Helen Wallace for giving access to Hyperspectral Imaging cameras at the University of the Sunshine Coast and the National Agricultural Research Institute (NARI) in Keravat for assisting to collect leaf samples. MM thanks Environmental Future Research Institute of Griffith University for hosting him as a visiting scholar to undertake this study.

Funding

The study was financially supported by the Australian Centre for International Agricultural Research (project FST/2014/099).

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Authors and Affiliations

  1. Environmental Futures Research Institute, School of Science and Environment, Griffith University, Nathan, Brisbane, QLD, 4111, Australia

    Mohammad Malmir, Iman Tahmasbian & Zhihong Xu

  2. Department of Agronomy, Faculty of Agriculture, Tarbiat Modares University, Tehran, 14115-336, Iran

    Mohammad Malmir

  3. NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW, 2650, Australia

    Iman Tahmasbian

  4. Graham Centre for Agricultural Innovation, Charles Sturt University, Wagga Wagga, New South Wales, 2650, Australia

    Iman Tahmasbian

  5. Genecology, Faculty of Science, Health, Education and Engineering, University of the Sunshine Coast, QLD, Maroochydore DC, 4558, Australia

    Michael B. Farrar & Shahla Hosseini Bai

  6. School of Health, Medical and Applied Sciences, Central Queensland University, Bundaberg, Queensland, 4670, Australia

    Shahla Hosseini Bai

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  1. Mohammad Malmir
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Correspondence to Shahla Hosseini Bai.

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Malmir, M., Tahmasbian, I., Xu, Z. et al. Prediction of macronutrients in plant leaves using chemometric analysis and wavelength selection. J Soils Sediments 20, 249–259 (2020). https://doi.org/10.1007/s11368-019-02418-z

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