The development of rice varieties that are tolerant of drought stress needs to be detected with Image-based phenotyping. This Image-based phenotyping method in combination with selection index and multivariate analysis can characterize the morphological response easily within a short time, which makes it is suitable for rice screening under drought stress. Therefore, this study aims to determine the selection index based on multivariate analysis and assess the effectiveness of using image-based phenotyping in drought rice screening. This study was conducted in two stages, the first was in static hydroponic and the second was in dynamic hydroponic. In static hydroponic, a split-plot design was used, where the levels of drought were the main plots and varieties were the subplots. However, in dynamic hydroponic, a nested design was used, where the replicates were nested in the drought level treatments. Also, The drought level factors used were PEG 0%, PEG 10%, and PEG 20%, and the variety factor consisted of 5 varieties which were repeated three times. The results showed that the selection index for static hydroponic consisted of shoot area (0.421), green shoot area (0.4177), and the area growth rate (0.4192). Meanwhile, the selection index in dynamic hydroponics consisted of object extent Y from the side (0.4516) and convex hull from the side (0.4177). The regression of the two-selection index has a good determination of 0.84. Hence, these results showed that rice screening based on image-based phenotyping can be recommended for rapid screening under drought stress.
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The world population is projected to reach to 9.7 billion people by 2050. With increasing population and improving living standards, the demand for food is accelerating. In order to meet increasing demand for food while arable land and other resources are decreasing, agriculture needs all the tools available to sustainably increase crop yields. Development of effective genetically modified (GM) traits to protect crops from abiotic and biotic stressors is a critical aspect of sustainable yield improvement. Efficient identification of traits and rapid integration of the traits into commercial elite germplasm requires robust and rapid trait testing. Monsanto has developed numerous high-throughput phenotyping platforms to support rapid trait identification and integration. Selected phenotyping platforms will be reviewed to gain understanding of how they are utilized for trait phenotyping.
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