Improving Strawberry Yield Prediction by Integrating Ground-Based Canopy Images in Modeling Approaches

Strawberries (<i<Fragaria</i< × <i<ananassa</i< Duch.) are highly perishable fruit. Timely prediction of yield is crucial for labor management and marketing decision-making. This study demonstrates the use of high-resolution ground-based imagery, in addition to previous yield...
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Gespeichert in:

Autor*in:	Amr Abd-Elrahman [verfasserIn] Feng Wu [verfasserIn] Shinsuke Agehara [verfasserIn] Katie Britt [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	canopy size metrics high-resolution image analysis regression model

Übergeordnetes Werk:	In: ISPRS International Journal of Geo-Information - MDPI AG, 2012, 10(2021), 4, p 239
Übergeordnetes Werk:	volume:10 ; year:2021 ; number:4, p 239

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/ijgi10040239

Katalog-ID:	DOAJ05775294X

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callnumber-first	G - Geography, Anthropology, Recreation
author	Amr Abd-Elrahman
spellingShingle	Amr Abd-Elrahman misc G1-922 misc canopy size metrics misc <i<Fragaria</i< × <i<ananassa</i< misc high-resolution misc image analysis misc regression model misc Geography (General) Improving Strawberry Yield Prediction by Integrating Ground-Based Canopy Images in Modeling Approaches
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topic_title	G1-922 Improving Strawberry Yield Prediction by Integrating Ground-Based Canopy Images in Modeling Approaches canopy size metrics <i<Fragaria</i< × <i<ananassa</i< high-resolution image analysis regression model
topic	misc G1-922 misc canopy size metrics misc <i<Fragaria</i< × <i<ananassa</i< misc high-resolution misc image analysis misc regression model misc Geography (General)
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title	Improving Strawberry Yield Prediction by Integrating Ground-Based Canopy Images in Modeling Approaches
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title_full	Improving Strawberry Yield Prediction by Integrating Ground-Based Canopy Images in Modeling Approaches
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title_sort	improving strawberry yield prediction by integrating ground-based canopy images in modeling approaches
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title_auth	Improving Strawberry Yield Prediction by Integrating Ground-Based Canopy Images in Modeling Approaches
abstract	Strawberries (<i<Fragaria</i< × <i<ananassa</i< Duch.) are highly perishable fruit. Timely prediction of yield is crucial for labor management and marketing decision-making. This study demonstrates the use of high-resolution ground-based imagery, in addition to previous yield and weather information, for yield prediction throughout the season at different intervals (3–4 days, 1 week, and 3 weeks pre-harvest). Flower and fruit counts, yield, and high-resolution imagery data were collected 31 times for two cultivars (‘Florida Radiance’ and ‘Florida Beauty’) throughout the growing season. Orthorectified mosaics and digital surface models were created to extract canopy size variables (canopy area, average canopy height, canopy height standard deviation, and canopy volume) and visually count flower and fruit number. Data collected at the plot level (6 plots per cultivar, 24 plants per plot) were used to develop prediction models. Using image-based counts and canopy variables, flower and fruit counts were predicted with percentage prediction errors of 26.3% and 25.7%, respectively. Furthermore, by adding image-derived variables to the models, the accuracy of predicting out-of-sample yields at different time intervals was increased by 10–29% compared to those models without image-derived variables. These results suggest that close-range high-resolution images can contribute to yield prediction and could assist the industry with decision making by changing growers’ prediction practices.
abstractGer	Strawberries (<i<Fragaria</i< × <i<ananassa</i< Duch.) are highly perishable fruit. Timely prediction of yield is crucial for labor management and marketing decision-making. This study demonstrates the use of high-resolution ground-based imagery, in addition to previous yield and weather information, for yield prediction throughout the season at different intervals (3–4 days, 1 week, and 3 weeks pre-harvest). Flower and fruit counts, yield, and high-resolution imagery data were collected 31 times for two cultivars (‘Florida Radiance’ and ‘Florida Beauty’) throughout the growing season. Orthorectified mosaics and digital surface models were created to extract canopy size variables (canopy area, average canopy height, canopy height standard deviation, and canopy volume) and visually count flower and fruit number. Data collected at the plot level (6 plots per cultivar, 24 plants per plot) were used to develop prediction models. Using image-based counts and canopy variables, flower and fruit counts were predicted with percentage prediction errors of 26.3% and 25.7%, respectively. Furthermore, by adding image-derived variables to the models, the accuracy of predicting out-of-sample yields at different time intervals was increased by 10–29% compared to those models without image-derived variables. These results suggest that close-range high-resolution images can contribute to yield prediction and could assist the industry with decision making by changing growers’ prediction practices.
abstract_unstemmed	Strawberries (<i<Fragaria</i< × <i<ananassa</i< Duch.) are highly perishable fruit. Timely prediction of yield is crucial for labor management and marketing decision-making. This study demonstrates the use of high-resolution ground-based imagery, in addition to previous yield and weather information, for yield prediction throughout the season at different intervals (3–4 days, 1 week, and 3 weeks pre-harvest). Flower and fruit counts, yield, and high-resolution imagery data were collected 31 times for two cultivars (‘Florida Radiance’ and ‘Florida Beauty’) throughout the growing season. Orthorectified mosaics and digital surface models were created to extract canopy size variables (canopy area, average canopy height, canopy height standard deviation, and canopy volume) and visually count flower and fruit number. Data collected at the plot level (6 plots per cultivar, 24 plants per plot) were used to develop prediction models. Using image-based counts and canopy variables, flower and fruit counts were predicted with percentage prediction errors of 26.3% and 25.7%, respectively. Furthermore, by adding image-derived variables to the models, the accuracy of predicting out-of-sample yields at different time intervals was increased by 10–29% compared to those models without image-derived variables. These results suggest that close-range high-resolution images can contribute to yield prediction and could assist the industry with decision making by changing growers’ prediction practices.
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container_issue	4, p 239
title_short	Improving Strawberry Yield Prediction by Integrating Ground-Based Canopy Images in Modeling Approaches
url	https://doi.org/10.3390/ijgi10040239 https://doaj.org/article/4f92ed3140e447e3843fc4a6171866c7 https://www.mdpi.com/2220-9964/10/4/239 https://doaj.org/toc/2220-9964
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author2	Feng Wu Shinsuke Agehara Katie Britt
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up_date	2024-07-03T13:53:52.341Z
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Improving Strawberry Yield Prediction by Integrating Ground-Based Canopy Images in Modeling Approaches

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