Dinotefuran is a widely used neonicotinoid pesticides in agriculture and it has certain ecological toxicity to aquatic organisms. Studies on the potential toxicological effects of dinotefuran on fish are limited. In the present study, 96 h acute toxicity test indicated that enantiomers of R-(-)-dinotefuran had a greater toxic effect than Rac-dinotefuran on zebrafish, and S-(+)-dinotefuran was the least. In chronic assay, R-(-)-dinotefuran exerted more effects on the development of zebrafish than S-(+)-dinotefuran, and dinotefuran also had enantioselective effect on oxidative stress. Significant changes were observed in the superoxide dismutase (SOD), glutathione S-transferase (GST) and acetylcholinesterase (AChE) activities and malondialdehyde (MDA) contents, which demonstrated dinotefuran induced oxidative stress in zebrafish. Besides, through an ultra-performance liquid chromatography quadrupole-TOF mass spectrometry (UPLC-Q-TOF-MS)-based metabolomics method was used to evaluate the enantioselectivity of dinotefuran enantiomers in zebrafish. The results indicated that R-(-)-dinotefuran caused greater disturbances of endogenous metabolites. Phenylalanine metabolic pathways, glycine, serine and threonine metabolic pathways are only involved in zebrafish exposed to R-(-)-dinotefuran; whereas phenylalanine, tyrosine and tryptophan biosynthesis was only involved in zebrafish exposed to S-(+)-dinotefuran. This study provides a certain reference value for assessing the environmental risks of dinotefuran enantiomers to aquatic organisms, and has practical significance for guiding the ecologically and environmentally safety use of dinotefuran.

Tea sprout segmentation and picking point localization via machine vision are the core technologies of automatic tea picking. This study proposes a method of tea segmentation and picking point location based on a lightweight convolutional neural network named MC-DM (Multi-Class DeepLabV3+ MobileNetV2 (Mobile Networks Vision 2)) to solve the problem of tea shoot picking point in a natural environment. In the MC-DM architecture, an optimized MobileNetV2 is used to reduce the number of parameters and calculations. Then, the densely connected atrous spatial pyramid pooling module is introduced into the MC-DM to obtain denser pixel sampling and a larger receptive field. Finally, an image dataset of high-quality tea sprout picking points is established to train and test the MC-DM network. Experimental results show that the MIoU of MC-DM reached 91.85%, which is improved by 8.35% compared with those of several state-of-the-art methods. The optimal improvements of model parameters and detection speed were 89.19% and 16.05 f/s, respectively. After the segmentation results of the MC-DM were applied to the picking point identification, the accuracy of picking point identification reached 82.52%, 90.07%, and 84.78% for single bud, one bud with one leaf, and one bud with two leaves, respectively. This research provides a theoretical reference for fast segmentation and visual localization of automatically picked tea sprouts.