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Published VersionCC BY V4.0, Open Access
Journal article
Hyperspectral Imaging of Adaxial and Abaxial Leaf Surfaces as a Predictor of Macadamia Crop Nutrition
Plants, Vol.12(3), pp.1-15
2023
PMID: 36771641
Abstract
Tree crop yield is highly dependent on fertiliser inputs, which are often guided by the assessment of foliar nutrient levels. Traditional methods for nutrient analysis are time-consuming but hyperspectral imaging has potential for rapid nutrient assessment. Hyperspectral imaging has generally been performed using the adaxial surface of leaves although the predictive performance of spectral data has rarely been compared between adaxial and abaxial surfaces of tree leaves. We aimed to evaluate the capacity of laboratory-based hyperspectral imaging (400-1000 nm wavelengths) to predict the nutrient concentrations in macadamia leaves. We also aimed to compare the prediction accuracy from adaxial and abaxial leaf surfaces. We sampled leaves from 30 macadamia trees at 0, 6, 10 and 26 weeks after flowering and captured hyperspectral images of their adaxial and abaxial surfaces. Partial least squares regression (PLSR) models were developed to predict foliar nutrient concentrations. Coefficients of determination (R-P(2)) and ratios of prediction to deviation (RPDs) were used to evaluate prediction accuracy. The models reliably predicted foliar nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), copper (Cu), manganese (Mn), sulphur (S) and zinc (Zn) concentrations. The best-fit models generally predicted nutrient concentrations from spectral data of the adaxial surface (e.g., N: R-P(2) = 0.55, RPD = 1.52; P: R-P(2) = 0.77, RPD = 2.11; K: R-P(2) = 0.77, RPD = 2.12; Ca: R-P(2) = 0.75, RPD = 2.04). Hyperspectral imaging showed great potential for predicting nutrient status. Rapid nutrient assessment through hyperspectral imaging could aid growers to increase orchard productivity by managing fertiliser inputs in a more-timely fashion.
Details
- Title
- Hyperspectral Imaging of Adaxial and Abaxial Leaf Surfaces as a Predictor of Macadamia Crop Nutrition
- Authors
- Anushika L. De Silva (Author) - Griffith UniversityStephen J. Trueman (Author) - Griffith UniversityWiebke Kamper (Author) - Griffith UniversityHelen M. Wallace (Author) - Griffith UniversityJoel Nichols (Author) - Griffith UniversityShahla Hosseini Bai (Author) - Griffith University
- Publication details
- Plants, Vol.12(3), pp.1-15
- Publisher
- MDPI AG
- Date published
- 2023
- DOI
- 10.3390/plants12030558
- ISSN
- 2223-7747
- PMID
- 36771641
- Copyright note
- © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
- Grant note
- The study was funded by project PH16001 of the Hort Frontiers Pollination Fund, part of the Hort Frontiers strategic partnership initiative developed by Hort Innovation, with co-investment from Griffith University, University of the Sunshine Coast, Plant & Food Research Ltd. and contributions from the Australian Government. Anushika De Silva was supported by Griffith University International Postgraduate Research Scholarship and Griffith University Postgraduate Research Scholarship.
- Organisation Unit
- School of Science and Engineering - Legacy; GeneCology Research Centre - Legacy; School of Science, Technology and Engineering; Centre for Bioinnovation
- Language
- English
- Record Identifier
- 99716026702621
- Output Type
- Journal article
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