Research on salinity stress has strongly increased over the last decade, as salinity stress is a main key factor limiting the global crop production in many regions of the world. In recent years, it is possible to obtain a large amount of genotypic data in a short time due to a reduction in genotyping costs. This wave of genomic information has effected the development of new strategies for the integration of molecular information in breeding programs. However, phenotyping is still a manual activity, and different from each species, environment, and trait. It often generates high labor costs, and can be sensitive to environmental changes, and sometimes includes the individual biased assessments from different people. There is a strong demand for phenotypic data of high quality. The current objective of phenomics is phenotyping a large number of individuals for many traits in a nondestructive manner and with good accuracy. Here we described the image-based technology as applied to alleviate the bottleneck for the development of high-throughput phenotyping platforms. Several trials to measure stress responses of rice plantlets based on image data under the salinity condition are underway to develop automation for the next-level of phenotyping.