Hyperspectral imaging for estimating leaf, flower, and fruit macronutrient concentrations and predicting strawberry yields

Dinh Dung Cao; Stephen Trueman; Helen Wallace; Michael Farrar; Tsvakai Gama; Iman Tahmasbian; Shahla Hosseini Bai

doi:10.1007/s11356-023-30344-8

Back

Hyperspectral imaging for estimating leaf, flower, and fruit macronutrient concentrations and predicting strawberry yields

Journal article

Open access

Peer reviewed

Hyperspectral imaging for estimating leaf, flower, and fruit macronutrient concentrations and predicting strawberry yields

Dinh Dung Cao, Stephen Trueman, Helen Wallace, Michael Farrar, Tsvakai Gama, Iman Tahmasbian and Shahla Hosseini Bai

Environmental Science and Pollution Research, Vol.30, pp.114166-114182

2023

DOI: https://doi.org/10.1007/s11356-023-30344-8

PMCID: PMC10663281

PMID: 37858016

Files and links (2)

pdf

Hyperspectral imaging for estimating leaf, flower, and fruit macronutrient concentrations and predicting strawberry yields2.35 MBDownload View

Published Version Open Access CC BY V4.0

url

https://doi.org/10.1007/s11356-023-30344-8View

Published Version

Abstract

Calcium

Hyperspectral imaging

Nitrogen

Potassium

Phosphorus

Managing the nutritional status of strawberry plants is critical for optimizing yield. This study evaluated the potential of hyperspectral imaging (400–1,000 nm) to estimate nitrogen (N), phosphorus (P), potassium (K), and calcium (Ca) concentrations in strawberry leaves, flowers, unripe fruit, and ripe fruit and to predict plant yield. Partial least squares regression (PLSR) models were developed to estimate nutrient concentrations. The determination coefficient of prediction (R2P) and ratio of performance to deviation (RPD) were used to evaluate prediction accuracy, which often proved to be greater for leaves, flowers, and unripe fruit than for ripe fruit. The prediction accuracies for N concentration were R2P = 0.64, 0.60, 0.81, and 0.30, and RPD = 1.64, 1.59, 2.64, and 1.31, for leaves, flowers, unripe fruit, and ripe fruit, respectively. Prediction accuracies for Ca concentrations were R2P = 0.70, 0.62, 0.61, and 0.03, and RPD = 1.77, 1.63, 1.60, and 1.15, for the same respective plant parts. Yield and fruit mass only had significant linear relationships with the Difference Vegetation Index (R2 = 0.256 and 0.266, respectively) among the eleven vegetation indices tested. Hyperspectral imaging showed potential for estimating nutrient status in strawberry crops. This technology will assist growers to make rapid nutrient-management decisions, allowing for optimal yield and quality.

Details

Title: Hyperspectral imaging for estimating leaf, flower, and fruit macronutrient concentrations and predicting strawberry yields
Authors: Dinh Dung Cao (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Stephen Trueman (Author) - Griffith University
Helen Wallace (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Michael Farrar (Author) - Griffith University
Tsvakai Gama (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Iman Tahmasbian (Author) - Department of Primary Industries
Shahla Hosseini Bai (Author) - Griffith University
Publication details: Environmental Science and Pollution Research, Vol.30, pp.114166-114182
Publisher: Springer
Date published: 2023
DOI: 10.1007/s11356-023-30344-8
ISSN: 1614-7499
PMID: 37858016; PMC10663281
Copyright note: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Data Availability: Data will be made available upon the request.
Grant note: PH16001 / Hort Innovation Vietnam Ministry of Education and Training (MOET) University of the Sunshine Coast (USC) Australian Government
Organisation Unit: School of Science and Engineering - Legacy; GeneCology Research Centre - Legacy; School of Science, Technology and Engineering
Language: English
Record Identifier: 99971189102621
Output Type: Journal article

Metrics

3 File views/ downloads

49 Record Views

14 Times Cited - Web of Science

InCites Highlights

These are selected metrics from InCites Benchmarking & Analytics tool, related to this output

Collaboration types: Domestic collaboration; International collaboration
Web Of Science research areas: Environmental Sciences

UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

Source: SDGs from InCites