Impact of atmospheric particulate matter retention on physiological characters of five plant species under different pollution levels in Zhengzhou.

Atmospheric particulate matter (PM) pollution has become a major environmental risk, and green plants can mitigate air pollution by regulating their enzymatic activity, osmoregulatory substances, photosynthetic pigments, and other biochemical characteristics. The present investigation aims to evaluate the mitigation potential of five common evergreen tree species (, , , , ) against air pollution and to assess the effect of dust retention on plant physiological functions exposed to three different pollution levels (road, campus, and park). The results found that the amount of dust retained per unit leaf area of the plants was proportional to the mass concentration of atmospheric particulate matter in the environment, and that dust accumulation was higher on the road and campus than in the park.

Inter-row information recognition of maize in the middle and late stages LiDAR supplementary vision.

In the middle and late stages of maize, light is limited and non-maize obstacles exist. When a plant protection robot uses the traditional visual navigation method to obtain navigation information, some information will be missing. Therefore, this paper proposed a method using LiDAR (laser imaging, detection and ranging) point cloud data to supplement machine vision data for recognizing inter-row information in the middle and late stages of maize.

Development of SSR markers in Paeonia based on De Novo transcriptomic assemblies.

Peony is a famous ornamental and medicinal plant in China, and peony hybrid breeding is an important means of germplasm innovation. However, research on the genome of this species is limited, thereby hindering the genetic and breeding research on peony. In the present study, simple sequence repeat (SSR) locus analysis was performed on expressed sequence tags obtained by the transcriptome sequencing of Paeonia using Microsatellite software.