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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/10/4/239 |
| _version_ | 1797538532102766592 |
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| author | Amr Abd-Elrahman Feng Wu Shinsuke Agehara Katie Britt |
| author_facet | Amr Abd-Elrahman Feng Wu Shinsuke Agehara Katie Britt |
| author_sort | Amr Abd-Elrahman |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T12:32:46Z |
| format | Article |
| id | doaj.art-4f92ed3140e447e3843fc4a6171866c7 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T12:32:46Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-4f92ed3140e447e3843fc4a6171866c72023-11-21T14:31:06ZengMDPI AGISPRS International Journal of Geo-Information2220-99642021-04-0110423910.3390/ijgi10040239Improving Strawberry Yield Prediction by Integrating Ground-Based Canopy Images in Modeling ApproachesAmr Abd-Elrahman0Feng Wu1Shinsuke Agehara2Katie Britt3School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL 32611, USAGulf Coast Research and Education Center, University of Florida, Wimauma, FL 33598, USAGulf Coast Research and Education Center, University of Florida, Wimauma, FL 33598, USASchool of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL 32611, USAStrawberries (<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.https://www.mdpi.com/2220-9964/10/4/239canopy size metrics<i>Fragaria</i> × <i>ananassa</i>high-resolutionimage analysisregression model |
| spellingShingle | Amr Abd-Elrahman Feng Wu Shinsuke Agehara Katie Britt Improving Strawberry Yield Prediction by Integrating Ground-Based Canopy Images in Modeling Approaches ISPRS International Journal of Geo-Information canopy size metrics <i>Fragaria</i> × <i>ananassa</i> high-resolution image analysis regression model |
| title | Improving Strawberry Yield Prediction by Integrating Ground-Based Canopy Images in Modeling Approaches |
| title_full | Improving Strawberry Yield Prediction by Integrating Ground-Based Canopy Images in Modeling Approaches |
| title_fullStr | Improving Strawberry Yield Prediction by Integrating Ground-Based Canopy Images in Modeling Approaches |
| title_full_unstemmed | Improving Strawberry Yield Prediction by Integrating Ground-Based Canopy Images in Modeling Approaches |
| title_short | Improving Strawberry Yield Prediction by Integrating Ground-Based Canopy Images in Modeling Approaches |
| title_sort | improving strawberry yield prediction by integrating ground based canopy images in modeling approaches |
| topic | canopy size metrics <i>Fragaria</i> × <i>ananassa</i> high-resolution image analysis regression model |
| url | https://www.mdpi.com/2220-9964/10/4/239 |
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