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Article

Automated Canopy Delineation and Size Metrics Extraction for Strawberry Dry Weight Modeling Using Raster Analysis of High-Resolution Imagery

1
Gulf Coast Research and Education Center, University of Florida, Wimauma, FL 33598, USA
2
School of Forest Resources and Conservation, University of Florida, Gainesville, FL 32603, USA
3
Department of Horticultural Sciences, University of Florida, Gainesville, FL 32611, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(21), 3632; https://doi.org/10.3390/rs12213632
Received: 19 September 2020 / Revised: 16 October 2020 / Accepted: 3 November 2020 / Published: 5 November 2020
(This article belongs to the Special Issue Digital Agriculture with Remote Sensing)
Capturing high spatial resolution imagery is becoming a standard operation in many agricultural applications. The increased capacity for image capture necessitates corresponding advances in analysis algorithms. This study introduces automated raster geoprocessing methods to automatically extract strawberry (Fragaria × ananassa) canopy size metrics using raster image analysis and utilize the extracted metrics in statistical modeling of strawberry dry weight. Automated canopy delineation and canopy size metrics extraction models were developed and implemented using ArcMap software v 10.7 and made available by the authors. The workflows were demonstrated using high spatial resolution (1 mm resolution) orthoimages and digital surface models (2 mm) of 34 strawberry plots (each containing 17 different plant genotypes) planted on raised beds. The images were captured on a weekly basis throughout the strawberry growing season (16 weeks) between early November and late February. The results of extracting four canopy size metrics (area, volume, average height, and height standard deviation) using automatically delineated and visually interpreted canopies were compared. The trends observed in the differences between canopy metrics extracted using the automatically delineated and visually interpreted canopies showed no significant differences. The R2 values of the models were 0.77 and 0.76 for the two datasets and the leave-one-out (LOO) cross validation root mean square error (RMSE) of the two models were 9.2 g and 9.4 g, respectively. The results show the feasibility of using automated methods for canopy delineation and canopy metric extraction to support plant phenotyping applications. View Full-Text
Keywords: geoprocessing workflow; dry biomass modeling; high resolution images; geospatial modeling geoprocessing workflow; dry biomass modeling; high resolution images; geospatial modeling
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MDPI and ACS Style

Abd-Elrahman, A.; Guan, Z.; Dalid, C.; Whitaker, V.; Britt, K.; Wilkinson, B.; Gonzalez, A. Automated Canopy Delineation and Size Metrics Extraction for Strawberry Dry Weight Modeling Using Raster Analysis of High-Resolution Imagery. Remote Sens. 2020, 12, 3632. https://doi.org/10.3390/rs12213632

AMA Style

Abd-Elrahman A, Guan Z, Dalid C, Whitaker V, Britt K, Wilkinson B, Gonzalez A. Automated Canopy Delineation and Size Metrics Extraction for Strawberry Dry Weight Modeling Using Raster Analysis of High-Resolution Imagery. Remote Sensing. 2020; 12(21):3632. https://doi.org/10.3390/rs12213632

Chicago/Turabian Style

Abd-Elrahman, Amr, Zhen Guan, Cheryl Dalid, Vance Whitaker, Katherine Britt, Benjamin Wilkinson, and Ali Gonzalez. 2020. "Automated Canopy Delineation and Size Metrics Extraction for Strawberry Dry Weight Modeling Using Raster Analysis of High-Resolution Imagery" Remote Sensing 12, no. 21: 3632. https://doi.org/10.3390/rs12213632

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