{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T06:02:55Z","timestamp":1776492175112,"version":"3.51.2"},"reference-count":69,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T00:00:00Z","timestamp":1726099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hort Innovation"},{"name":"Griffith University"},{"name":"Plant and Food Research Limited"},{"name":"University of the Sunshine Coast"},{"name":"Australian Government"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Carbohydrate levels are important regulators of the growth and yield of tree crops. Current methods for measuring foliar carbohydrate concentrations are time consuming and laborious, but rapid imaging technologies have emerged with the potential to improve the effectiveness of tree nutrient management. Carbohydrate concentrations were predicted using hyperspectral imaging (400\u20131000 nm) of leaves of the evergreen tree crops, avocado, and macadamia. Models were developed using partial least squares regression (PLSR) and artificial neural network (ANN) algorithms to predict carbohydrate concentrations. PLSR models had R2 values of 0.51, 0.82, 0.86, and 0.85, and ANN models had R2 values of 0.83, 0.83, 0.78, and 0.86, in predicting starch, sucrose, glucose, and fructose concentrations, respectively, in avocado leaves. PLSR models had R2 values of 0.60, 0.64, 0.91, and 0.95, and ANN models had R2 values of 0.67, 0.82, 0.98, and 0.98, in predicting the same concentrations, respectively, in macadamia leaves. ANN only outperformed PLSR when predicting starch concentrations in avocado leaves and sucrose concentrations in macadamia leaves. Performance differences were possibly associated with nonlinear relationships between carbohydrate concentrations and reflectance values. This study demonstrates that PLSR and ANN models perform well in predicting carbohydrate concentrations in evergreen tree-crop leaves.<\/jats:p>","DOI":"10.3390\/rs16183389","type":"journal-article","created":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T06:31:40Z","timestamp":1726122700000},"page":"3389","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Predicting Carbohydrate Concentrations in Avocado and Macadamia Leaves Using Hyperspectral Imaging with Partial Least Squares Regressions and Artificial Neural Networks"],"prefix":"10.3390","volume":"16","author":[{"given":"Shahla Hosseini","family":"Bai","sequence":"first","affiliation":[{"name":"Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Brisbane, QLD 4111, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2774-1266","authenticated-orcid":false,"given":"Mahshid","family":"Tootoonchy","sequence":"additional","affiliation":[{"name":"John Grill Institute for Project Leadership, Faculty of Engineering, School of Project Management, The University of Sydney, K06A-21 Ross Street Building, Sydney, NSW 2037, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8646-4492","authenticated-orcid":false,"given":"Wiebke","family":"K\u00e4mper","sequence":"additional","affiliation":[{"name":"Functional Agrobiodiversity & Agroecology, Department of Crop Sciences, University of G\u00f6ttingen, 37077 G\u00f6ttingen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7636-0481","authenticated-orcid":false,"given":"Iman","family":"Tahmasbian","sequence":"additional","affiliation":[{"name":"Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Brisbane, QLD 4111, Australia"},{"name":"Department of Agriculture and Fisheries, Queensland Government, Toowoomba, QLD 4350, Australia"}]},{"given":"Michael B.","family":"Farrar","sequence":"additional","affiliation":[{"name":"Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Brisbane, QLD 4111, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5754-9254","authenticated-orcid":false,"given":"Helen","family":"Boldingh","sequence":"additional","affiliation":[{"name":"The New Zealand Institute for Plant & Food Research Limited, Private Bag 3230, Waikato Mail Centre, Hamilton 3240, New Zealand"}]},{"given":"Trisha","family":"Pereira","sequence":"additional","affiliation":[{"name":"The New Zealand Institute for Plant & Food Research Limited, Private Bag 3230, Waikato Mail Centre, Hamilton 3240, New Zealand"}]},{"given":"Hannah","family":"Jonson","sequence":"additional","affiliation":[{"name":"The New Zealand Institute for Plant & Food Research Limited, Private Bag 3230, Waikato Mail Centre, Hamilton 3240, New Zealand"}]},{"given":"Joel","family":"Nichols","sequence":"additional","affiliation":[{"name":"Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Brisbane, QLD 4111, Australia"}]},{"given":"Helen M.","family":"Wallace","sequence":"additional","affiliation":[{"name":"School of Biology and Environmental Science, Queensland University of Technology, GPO Box 2434, Brisbane, QLD 4001, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7105-7130","authenticated-orcid":false,"given":"Stephen J.","family":"Trueman","sequence":"additional","affiliation":[{"name":"Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Brisbane, QLD 4111, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,12]]},"reference":[{"key":"ref_1","unstructured":"FAO (2022). 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