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Crops have diverse structures and complex growth environments. RGB image data can reflect the texture and color features of plants accurately, while 3D data contains information about crop volume. The combination of RGB image and 3D point cloud data can achieve the extraction of two-dimensional and three-dimensional phenotypic parameters of crops, which is of great significance for the research of phenomics methods. In this study, potatoe plants were chosen as the research subject, and RGB cameras and laser scanners were used to collect 50 potato RGB images and 3D laser point cloud data. The segmentation accuracy of four deep learning semantic segmentation methods, OCRNet, UpNet, PaNet, and DeepLab v3+, were compared and analyzed for the RGB images. OCRNet, which demonstrated higher accuracy, was used to perform semantic segmentation on top-view RGB images of potatoes. Mean shift clustering algorithm was optimized for laser point cloud data processing, and single-plant segmentation of laser point cloud data was completed. Stem and leaf segmentation of single-plant potato point cloud data were accurately performed using Euclidean clustering and K-Means clustering algorithms. In addition, a strategy was proposed to establish a one-to-one correspondence between RGB images and point clouds of single-plant potatoes using pot numbering. 8 2D phenotypic parameters and 10 3D phenotypic parameters, including maximum width, perimeter, area, plant height, volume, leaf length, and leaf width, etc., were extracted from RGB images and laser point clouds, respectively. Finally, the accuracy of three representative and easily measurable phenotypic parameters, leaf number, plant height, and maximum width were evaluated. The mean absolute percentage errors (MAPE) were 8.6%, 8.3% and 6.0%, respectively, while the root mean square errors (RMSE) were 1.371 pieces, 3.2 cm and 1.86 cm, respectively, and the determination coefficients (R2) were 0.93, 0.95 and 0.91, respectively. The research results indicated that the extracted phenotype parameters can accurately and efficiently reflect the growth status of potatoes. Combining the RGB image data of potatoes with three-dimensional laser point cloud data can fully exploit the advantages of the rich texture and color characteristics of RGB images and the volumetric information provided by three-dimensional point clouds, achieving non-destructive, efficient, and high-precision extraction of two-dimensional and three-dimensional phenotype parameters of potato plants. The achievements of this study could not only provide important technical support for the cultivation and breeding of potatoes but also provide strong support for phenotype-based research.

To investigate the air quality status and identify factors influencing air pollution, data on CO2, formaldehyde, PM2.5, PM10, temperature, and humidity were measured in a high-speed train carriage of the Changsha-Zhuzhou intercity in southwest China. In the test section, CO2, formaldehyde, relative humidity, and temperature exceeded the standard levels, and their compliance rates were 84%, 96%, 84%, and 76%, respectively. Then the influencing factors of pollutants were analyzed from three aspects: driving environment, the number of passengers, and relative humidity. Results show that the percentage of PM2.5 to PM10 in tunnel environments was higher; the increase in the number of passengers led to the elevation of CO2 concentration in the carriage, while only in the ground environment, the number of passengers significantly correlated with particulate matter; the increase in relative humidity promotes the production and release of particulate matter and formaldehyde. Finally, fitting relationships between relative humidity and formaldehyde, as well as the number of passengers and CO2 were established to predict the air quality status. It indicated that when the relative humidity of the carriage is less than 73%, the formaldehyde solubility was easy to exceed standards, and when the number of passengers was greater than 70, the CO2 was easy to exceed standards.

To cope with the challenges posed by the rapid growth of world population and global environmental changes, scholars should employ genetic and phenotypic analyses to breed crop varieties with improved responses to limited resource environments and soil conditions to increase crop yield and quality. Therefore, the efficient, accurate, and non-destructive measurement of crop phenotypic traits, and the dynamic quantification of phenotypic traits are urgently needed for crop phenotypic research, and breeding as well as for modern agricultural development. In this study, cotton plants were taken as research objects, and the multi-temporal point cloud data of cotton plants were collected by using three-dimensional laser scanning technology. The multi-temporal point clouds of three cotton plants at four time points were collected. First, RANSAC algorithm was implemented for main stem extraction on the original point cloud data of cotton plants, then region growing based clustering was carried out for leaf segmentation. Plant height was estimated by calculating the end points of the segmented main stem. Leaf length and width measurements were conducted on the segmented leaf parts. In addition, the volume was also estimated through the convex hull of the original point cloud of plant cotton. Then, multi-temporal point clouds of plants were registered, and organ correspondence was constructed with the Hungarian method. Finally, dynamic quantification of phenotypic traits including plant volume, plant height, leaf length, leaf width, and leaf area were calculated and analyzed. The overall performance of the approaches achieved a matching rate through a series of experiments, and the traits extracted by using of point cloud showed high correlation with the manually measured ones. The relative error between plant height and manual measurement results did not exceed 1.0%. The estimated leaf length and width on point clouds were highly correlated with the manually measured ones, and the coefficient of determination was nearly 1.0. The proposed 3D phenotyping methodology can be introduced and used to other crops for phenotyping.

The human cognitive performance under hot and humid exposure is a critical issue for people working under hot and humid environments, for the considerations of both their health and safety. In order to find an indicator of the cognitive performance, the electrocardiogram (ECG) data were recorded continuously for 2.5 hours with portable Holter, when participants were exposed to 4 hot-humid environmental conditions and conducting different cognitive tests. The ECG data then were analysed to obtain the HRV indices that were used to establish a relationship with the cognitive test results. Temporal index pNN50 of HRV time domain indices was selected as a biomarker of the cognitive performance in this study. Results showed that the pNN50 responded significantly to the air temperature, while the influences of the cognitive tasks and the exposure time were found to be less significant. Meanwhile, the mean accuracy of the cognitive tasks was found to be positively related to the changes of mean pNN50, but a negatively related relationship between the mean reaction time and the mean pNN50 was observed. Furthermore, a positive relationship between the relative cognitive performance and the pNN50 was obtained, which meant that higher pNN50 responded to the higher cognitive performance. The potential reason could be ascribed to the changes in temperature-related allocation of the mental resources. And the low cognitive performance could be attributed to the rise of the mental fatigue due to the high sympathetic activities that corresponded to the low pNN50. The results revealed that the pNN50 could act as a biomarker of the cognitive performance under hot-humid exposure. The findings gave some implications to the real-time evaluation of the working performance and health of the people working under hot-humid environments.

In recent years, constant temperature and humidity air-conditioning systems have been widely used and promoted in residential buildings all over the world. At the same time, the influences of long-term stable indoor environment on people’s lives, especially on sleep quality and health, have attracted wide attention. Therefore, in this paper, a questionnaire investigation on people’s sleep and health in constant temperature and humidity environment was carried out in China. By comparing the questionnaire investigation results from the users of constant temperature and humidity air-conditionings with unused, it could be found that people living in constant temperature and humidity houses have better performance in sleep quality, but worse health condition. These results suggested that constant temperature and humidity air-conditioning had an effect on human bodies. The findings can also inform future study on the role of thermal environment on sleep and health.