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Hyperspectral imaging predicts yield and nitrogen content in grass–legume polycultures

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Abstract

Successful use of hyperspectral imaging technology to progress precision agriculture is highly dependent on calibration on species of interest. To date, high-throughput hyperspectral imaging to predict plant growth and nutrient content has largely been limited to single-species cultivations. Therefore, this study aimed to calibrate a range of agronomic traits in mixed cultivations to hyperspectral image data. It successfully demonstrated that hyperspectral imaging can predict the plant traits biomass (g), foliar nitrogen (N) concentration (mg g−1) and N yield (mg), in grass and legume monocultures and polycultures in response to differential N and phosphorus (P) fertilization in a controlled greenhouse experiment. Visible light and near infrared (VNIR) and short wavelength infrared (SWIR) input resulted in only minor image misclassification (0.02%) for the green plants from the background regardless of species. The trained partial least square regression (PLSR) models VNIR-HH (hyper-hue) and SWIR had the lowest misclassification errors of 3.16% and 9.56% and were used for the grass–legume classification. For grass, there was good agreement between the mixed-effect models derived from the laboratory, and the PLSR models from hyperspectral measurements, except for the effect of N × P on N yield. Legume model agreement was not as precise as grass, likely because fertilizer-driven treatment effects on the measured traits were not as clear. Key wavelengths contributing to the strength of the PLSR models for predicting N content and biomass were identified from this study. Effective calibration of growth and nutrient uptake traits against hyperspectral data in mixed cultivations under controlled conditions is an important contribution towards improving remote sensing technologies for broader application in polyculture field cropping.

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Funding

Funding for this project was provided by an Australian Plant Phenomics Facility postgraduate award to Kirsten Ball. The Australian Plant Phenomics Facility is funded by the Australian Government under the National Collaborative Research Infrastructure Strategy (NCRIS). Additional funds were provided by an Australian Postgraduate Award scholarship from the University of Western Sydney.

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Authors and Affiliations

  1. Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia

    K. R. Ball, S. A. Power & E. Pendall

  2. Australian Plant Phenomics Facility, The Plant Accelerator, School of Agriculture, Food and Wine, University of Adelaide, Waite Campus, Urrbrae, SA, 5064, Australia

    H. Liu, C. Brien & B. Berger

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  1. K. R. Ball
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  2. H. Liu
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  3. C. Brien
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  4. B. Berger
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Contributions

KB designed and managed the experiment, assisted with statistical analyses and wrote the manuscript. JL processed and analysed the imaging data and wrote the manuscript. CB consulted on and produced the experimental design, performed statistical analyses, and consulted on the manuscript. BB consulted on experimental design, managed the experiment and directed the development of the manuscript. SP and EP assisted with the manuscript. The authors wish to recognize that this study was conducted at the University of Adelaide on the traditional lands of the Kaurna people.

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Correspondence to K. R. Ball.

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Ball, K.R., Liu, H., Brien, C. et al. Hyperspectral imaging predicts yield and nitrogen content in grass–legume polycultures. Precision Agric 23, 2270–2288 (2022). https://doi.org/10.1007/s11119-022-09920-4

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