Version 1
: Received: 13 August 2024 / Approved: 14 August 2024 / Online: 14 August 2024 (12:28:09 CEST)
How to cite:
Conley, M. M.; Hejl, R. W.; Serba, D. D.; Williams, C. F. Visualizing Plant Responses: Novel Insights Possible through Affordable Imaging Techniques in the Greenhouse. Preprints2024, 2024080999. https://doi.org/10.20944/preprints202408.0999.v1
Conley, M. M.; Hejl, R. W.; Serba, D. D.; Williams, C. F. Visualizing Plant Responses: Novel Insights Possible through Affordable Imaging Techniques in the Greenhouse. Preprints 2024, 2024080999. https://doi.org/10.20944/preprints202408.0999.v1
Conley, M. M.; Hejl, R. W.; Serba, D. D.; Williams, C. F. Visualizing Plant Responses: Novel Insights Possible through Affordable Imaging Techniques in the Greenhouse. Preprints2024, 2024080999. https://doi.org/10.20944/preprints202408.0999.v1
APA Style
Conley, M. M., Hejl, R. W., Serba, D. D., & Williams, C. F. (2024). Visualizing Plant Responses: Novel Insights Possible through Affordable Imaging Techniques in the Greenhouse. Preprints. https://doi.org/10.20944/preprints202408.0999.v1
Chicago/Turabian Style
Conley, M. M., Desalegn D. Serba and Clinton F. Williams. 2024 "Visualizing Plant Responses: Novel Insights Possible through Affordable Imaging Techniques in the Greenhouse" Preprints. https://doi.org/10.20944/preprints202408.0999.v1
Abstract
Global climatic pressures and increased human demands create a modern necessity for efficient and affordable plant phenotyping unencumbered by arduous technical requirements. The analysis and archival of imagery have become easier as modern camera technology and computers are leveraged. This facilitates the detection of vegetation status and changes over time. Using a custom lightbox, an inexpensive camera, and common software, turfgrass pots were photographed in a greenhouse environment over an 8-week experiment period. Subsequent imagery was analyzed for area of cover, color metrics, and sensitivity to image corrections. Findings were compared to active spectral reflectance data and previously reported measurements of visual quality, productivity, and water use. Results indicate that Red Green Blue-based (RGB) imagery with simple controls is sufficient to measure the effects of plant treatments. Notable correlations were observed for corrected imagery, including between a percent yellow color area classification segment (%Y) with human visual quality ratings (VQ) (R = -0.89), the dark green color index (DGCI) with clipping productivity in mg d-1 (mg) (R = 0.61), and an index combination term (COMB2) with water use in mm d-1 (mm) (R = -0.60). The calculation of green cover area (%G) correlated with Normalized Difference Vegetation Index (NDVI) (R = 0.91) and its RED reflectance spectra (R = -0.87). A CIELAB b*/a* chromatic ratio (BA) correlated with Normalized Difference Red-Edge index (NDRE) (R = 0.90), and its Red-Edge (RE) (R = -0.74) reflectance spectra, while a new calculation termed HSVi correlated strongest to the Near-Infrared (NIR) (R = 0.90) reflectance spectra. Additionally, COMB2 significantly differentiated between the treatment effects of date, mowing height, deficit irrigation, and their interactions (p < 0.001). Sensitivity and statistical analysis of typical image file formats and corrections that included JPEG (JPG), TIFF (TIF), geometric lens correction (LC), and color correction (CC) were conducted. Results underscore the need for further research to support image corrections standardization and better connect image data to biological processes. This study demonstrates the potential of consumer-grade photography to capture plant phenotypic traits.
Biology and Life Sciences, Agricultural Science and Agronomy
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
