Development of image-based phenotyping for selection characters of rice adaptability on the seedling salinity screening

Abstract

Development of adaptability rice under salinity stress needs effective and selective methods in the screening process. The seedling screening method is a general method used in salinity screening. However, this screening method often uses conventional observation in its screening process. This observation is rated that has a high error level. Therefore, the development of a digital approach through image-based phenotyping expected could minimize the error in the adaptability screening. This study was designed with a nested randomized complete group design, where replications were nested in a stressful environment. The environment in this study was normal (0 mM NaCl) and salinity stress (120 mM NaCl). The genotype used consisted of 8 genotypes which were repeated three times. The number of characters observed was nine image-based phenotyping. The results of this study showed that green percentage, the 3rd leaf length, and total area were the selection characters of image-based phenotyping under seedling salinity screening. Besides that, the used adaptability index in salinity screening became a good approach in considered and distinguished tolerance responses among varieties, especially to Pokkali (tolerant control variety) and IR 29 (sensitive control variety). Based on this study, the application of image-based phenotyping recommended in the screening process of line adaptability under salinity stress.

Combining Image-Based Phenotyping and Multivariate Analysis to Estimate Fruit Fresh Weight in Segregation Lines of Lowland Tomatoes

“…This result was consistently observed across various direct and indirect selection methods, highlighting the robust effectiveness of WFA in predicting the FFW within the proposed IBP framework. Such evidence aligns with the findings previously reported by various researchers on several plant species [24,27,37,92,93], including tomato [22,[38][39][40].…”

“…IBP is a digitalization approach in the 4.0 era, encompassing the accurate and nondestructive screening of imaged objects [24][25][26][27][28][29]. The precision of the analysis is dependent on the type of imaging sensors employed, including Red, Green and Blue (RGB) cameras, multispectral cameras, hyperspectral cameras, Light Detection and Ranging (LiDAR) cameras, and Magnetic Resonance Imaging (MRI) [28,29].…”

“…The higher spatial resolution of the camera sensor, the more comprehensive the data obtained, especially when integrated with automation and big data concepts, such as high-throughput phenotyping [28,[30][31][32][33][34]. In this context, RGB cameras represent an affordable tool which can be used to indirectly obtain reliable information on the agronomic potential of plants and/or crops [27,29,[35][36][37][38]. Therefore, phenotypic traits derived from RGB images can serve as inputs for models aimed at predicting tomato fruit weight.…”

Normalized Difference Vegetation Index Analysis to Evaluate Corn Cultivation Technology Based on Farmer Participation

“…This finding corresponds to the previous regression analysis where the three characters showed the same direction of selection. 3D plot analysis is widely used to simplify the interaction between multiple aspects, thereby allowing for more effective assessment (Paulus 2019;Farid et al 2020Farid et al , 2021Anshori et al 2021b). However, the 3D concept is based on degrees of equality among the aspects observed; hence, no aspect is dominant (Paulus 2019).…”