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Hyperspectral imaging for estimating leaf, flower, and fruit macronutrient concentrations and predicting strawberry yields2.35 MB
Published VersionCC BY V4.0, Open Access
Journal article
Hyperspectral imaging for estimating leaf, flower, and fruit macronutrient concentrations and predicting strawberry yields
Environmental Science and Pollution Research, Vol.30, pp.114166-114182
2023
Abstract
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 EngineeringStephen Trueman (Author) - Griffith UniversityHelen Wallace (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringMichael Farrar (Author) - Griffith UniversityTsvakai Gama (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringIman Tahmasbian (Author) - Department of Primary IndustriesShahla 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; 0944-1344
- 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
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