Akademik Veri Yönetim Sistemi
Euphytica, cilt.219, sa.8, 2023 (SCI-Expanded)
This study evaluated UAV-imaging and ground-based platforms as nondestructive means of phenotyping drought resistant zoysiagrasses and assessed their potential for replacing the subjective visual rating system commonly used in turfgrass drought resistance evaluations. The study compared 243 zoysiagrass hybrids and two commercially available cultivars. The entries were established in a randomized complete block design with three replications. The established zoysiagrass entries were subjected to drought stress by ceasing irrigation for 21 days during summer drought conditions. The UAV flights were conducted over twelve days during the experiment. Near infrared, UAV-based NDVI, chlorophyll index-green, green leaf index, soil-adjusted vegetation index, optimized soil-adjusted vegetation index, and leaf area index were determined from the UAV images obtained on the 12 sampling dates. Visual turfgrass quality ratings, visual percent green cover ratings, ground-based NDVI (NDVI-G), and relative chlorophyll content were taken immediately following each UAV flight. High correlations (0.70–0.86) were determined between the UAV-based indexes, including NDVI, SAVI, and OSAVI, and the ground truth data on 11, 15, 18, and 21 days under drought stress. Results revealed that UAV-based NDVI, SAVI, and OSAVI can be used to predict TQ (R2 = 0.74) and PGC (R2 = 0.63) under drought stress conditions. Out of the top performing 10% of breeding lines identified using ground measurement (TQ, NDVI-G, and PGC), 97% coincided with those identified using UAV-based NDVI, SAVI, and OSAVI indexes during drought stress. UAVs offer a more rapid and non-subjective alternative to both visual scoring and ground-based sensors in large-scale plant breeding applications.