Automated Canopy Delineation and Size Metrics Extraction for Strawberry Dry Weight Modeling Using Raster Analysis of High-Resolution Imagery
| Autor: | Katherine Britt, Zhen Guan, Cheryl Dalid, Amr Abd-Elrahman, Ali Gonzalez, Benjamin E. Wilkinson, Vance M. Whitaker |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Canopy
010504 meteorology & atmospheric sciences Mean squared error dry biomass modeling ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 0211 other engineering and technologies 02 engineering and technology 01 natural sciences Standard deviation Cross-validation high resolution images lcsh:Science 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing geoprocessing workflow geospatial modeling Geoprocessing Statistical model computer.file_format Metric (mathematics) General Earth and Planetary Sciences Environmental science lcsh:Q Raster graphics computer |
| Zdroj: | Remote Sensing, Vol 12, Iss 3632, p 3632 (2020) Remote Sensing Volume 12 Issue 21 |
| ISSN: | 2072-4292 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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