Coral Detection, Ranging, and Assessment (CDRA) algorithm-based automatic estimation of coral reef coverage.

Climate change poses significant threats to the health of coral reefs. The detection of coral coverage is a quantitative method for assessing the health of coral reefs. Valuable insights into the effects of climate change on coral reef ecosystems and the trends in coral reef development can be gained through the monitoring of coral coverage. However, traditional manual methods for estimating coral coverage are time-consuming and labor-intensive, limiting the scalability and efficiency of coral surveys. The field of automated processing for coral video imagery is currently limited, impeding the development of effective techniques for comprehensive coral monitoring and further hindering progress in coral ecosystem monitoring. To cope with these challenges, there is a crucial need for automated systems to efficiently handle large volumes of video data, thereby transforming imaging devices, such as underwater robots, into autonomous sensors capable of conducting reliable and comprehensive surveys. In response to this pressing issue, this study presents a novel video analysis approach, termed the Coral Detection, Ranging, and Assessment (CDRA) algorithm, which combines cutting-edge computer vision techniques including You Only Look Once version 5 (YOLOv5) object detection, binocular stereo vision, and tracking algorithms. The CDRA algorithm aims to analyze continuous video segments of coral reefs, enabling the identification, counting, and estimation of size and location of individual coral colonies. Experimental evaluations conducted in controlled pool environments demonstrated the high accuracy of the proposed method, with an average accuracy of 90.7% in estimating coral coverage at different locations. Moreover, field trials conducted in Xidao Island of China verified the effectiveness and robustness of the CDRA algorithm under varying water quality and lighting conditions. The findings of this study represent a significant step towards the development of reliable and automated techniques for coral reef detection, contributing to the advancement of coral reef conservation efforts.

Assessment of the influence of gluten quality on highland barley dough sheet quality by different instruments.

This study was to compare the results of texture analyzer with those of farinograph and extensograph and determine whether texture analyzer could be used to evaluate the processing quality of highland barley flour (HBF) dough sheet. The farinograph and extensograph tests were used to determine the reconstituted flour properties, a texture analyzer was applied to measure the tensile strength (TS) of HBF dough sheet, and the content of glutenin macropolymer (GMP), free sulfhydryl (-SH) and secondary structure of protein and microstructure in HBF dough sheet were investigated. Furthermore, correlations between these parameters were determined by regression analysis and Pearson correlation coefficient. It was suggested that the reconstituted flours with a higher gluten index showed a higher farinograph quality number (FQN) and greater maximum resistance to extension (Rm ). HBF dough sheets with higher gluten index possessed higher GMP and lower free -SH contents, a more ordered secondary structure of protein, resulting in a more compact gluten network and a stronger TS. The regression and correlation analysis showed that TS was positively correlated with FQN and Rm . In addition, it was significantly correlated with the content of GMP, -SH, secondary structure of protein and gluten network. It was concluded that texture analyzer could be an alternative approach to evaluate the processing quality of HBF dough sheet. Moreover, the gluten index of flours could be used to predict the processing quality of HBF dough sheet.