The CONSTANS-like 4 transcription factor,AtCOL_4,positively regulates abiotic stress tolerance through an abscisic acid-dependent manner in Arabidopsis

The precise roles of the B-box zinc finger family of transcription factors in plant stress are poorly understood.Functional analysis was performed on AtCOL4,an Arabidopsis thaliana L.CONSTANS-like 4 protein that is a putative novel transcription factor,and which contains a predicted transcriptional activation domain.Analyses of an AtCOL4 promoter-b-glucuronidase（GUS） construct revealed substantial GUS activity in whole seedlings.The expression of AtCOL4 was strongly induced by abscisic acid（ABA）,salt,and osmotic stress.Mutation in atcol4 resulted in increased sensitivity to ABA and salt stress during seed germination and the cotyledon greening process.In contrast,AtCOL4-overexpressing plants were less sensitive to ABA and salt stress compared to the wild type.Interestingly,in the presence of ABA or salt stress,the transcript levels of other ABA biosynthesis and stress-related genes were enhanced induction in AtCOL4-overexpressing and WT plants,rather than in the atcol4 mutant.Thus,AtCOL4 is involved in ABA and salt stress response through the ABA-dependent signaling pathway.Taken together,these findings provide compelling evidence that AtCOL4 is an important regulator for plant tolerance to abiotic stress.e

Development of real-time onion disease monitoring system using image acquisition

In this study, real-time disease monitoring was conducted on onion which is the most representative crop in Republic of Korea, using an image acquisition system newly developed for the mobile measurement of phenotype. The purpose of this study was to improve the accuracy of prediction of disease and state variables by processing images acquired from monitoring. The image acquisition system was consisted of two parts, a motorized driving system and a PTZ(pan, tilt and zoom) camera to take images of the plants. The acquired images were processed as follows. Noise was removed through an image filter and RGB(red, green and blue) colors were converted to HSV(hue, saturation and value), which enabled thresholding of areas with different colors and properties for image binarization by comparing the color of onion leaf with ambient areas. Four objects with the most significant browning in the onion leaf to the naked eye were selected as the samples for data acquired. The thresholding method with image processing was found to be superior to the naked eye in identifying accurate disease areas. In addition, it was found that the incidence of disease was different in each disease area ratio. As a result, the use of image acquisition system in image processing analysis will enable more prompt detection of any changes in the onion and monitoring of disease outbreaks during the crop lifecycle.