Anthocyanin is known for positive health beneficial effects that including reduces age related oxidative stress and inflammatory responses. It was produced by vegetable crops and a lettuce is one of the crops. The general pathway of anthocyanin expression is well defined but it is not clear how environments effects on anthocyanin accumulation in a lettuce. Therefore we initiated to study interaction between anthocyanin expression and environment factors. Frist, we applied RGB leaf images in a lettuce to calculate anthocyanin areas in a leaflet with two different cultivars, different developmental stages, and different environments. Later, we attempted to capture RNA expression level with next generation sequence (NGS) RNA sequencing method called RNA-seq. As a result, combined two technologies showed that quantitate phenotypic data help to understand the gene expression of anthocyanin in lettuce cultivars.

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.

This study was conducted to provide basic data for high-throughput screening (HTS) system construction based on phenomics. Rice (Oryza sativa cv. Chucheongbyeo) seedlings in vegetative growth stage were grown in the glass house and treated with 0, 3.75, 7.5, 15, and 30% (w/v) of polyethylene glycol (PEG) to give osmotic stress. Three days after PEG treatment, hyper-spectral reflectance images were obtained and analyzed after removing background image in several steps. The reflectance of rice seedlings treated with 15 and 30% of PEG solutions were significantly different at 680 nm, where differences in the chlorophyll reflectance spectrum and visual symptoms were not observed. These results thus indicate that hyper-spectral reflectance observed at 680 nm can be used to screen drought tolerant rice lines. A HTS system equipped with this hyper-spectral reflectance system may play an important role of future rice breeding program.

Drought is one of the major abiotic stresses in agriculture affecting major crops worldwide. Any step taken towards improvement of either crops or its growing conditions which enables the crop to produce comparable yield with less water will help substantially to combat this problem. Water regulating gel-like growth substrate called PRS claims to improve the water use of the plants and helps them to grow better and bigger with less water. To test this claim, we used two crops Pepper and Ficus to grow in pot system with and without PRS. To monitor their growth variation with detailed and precision phenotyping we used the high-throughput, non-destructive digital phenotyping platform- PhenoFab®. The continuous growth information enabled capturing of minor growth variations seen in the plants treated with and without PRS. Results reveal that no significant growth differences were found between PRS treated and non-treated plants, however the PRS treated plants needed 35% less water compared to the non-treated plants. Hence, PRS allows use of less water to grow plants.