The Kanzawa spider mite, Tetranychus kanzawai Kishida, is a major pest in tea fields [Camellia sinensis (L.) O. Kuntze] in Japan. However, recently, there have been some instances where acaricides are no longer applied as a result of the low occurrence of T. kanzawai in tea fields in Japan. In the period of 2015-2017, surveys of predatory mites in the study tea field detected Amblyseius eharai Amitai and Swirski, Phytoseiulus persimilis Athias-Henriot, Euseius sojaensis (Ehara), Amblyseius obtuserellus Wainstein and Begljarov, and Typhlodromus vulgaris Ehara in tea fields, but not Neoseiulus womersleyi (Schicha), indicating that a major change in the composition of the phytoseiid mite population had occurred. In laboratory studies, we confirmed the ability to avoid synthetic pyrethroid insecticides of the major beneficial mites in tea fields, A. eharai and P. persimilis, but not of E. sojaensis, a predatory mite whose population declined heavily after pesticide application. Attempts are made in this study to associate the decrease in T. kanzawai frequency in Japan with changes in pesticide used, method of spraying, and composition of the phytoseiid mite population. By continuing the method of pesticide spraying ('partial surface'), which leaves refugia in the leaf layer with sub-lethal dosages of pesticide, phytoseiid mites are aided to evade pesticides, resulting in maintenance of the composition of the phytoseiid mite populations in terms of diversity and abundance. Maintaining the diversity and abundance of Phytoseiidae may have contributed to the stabilization of the T. kanzawai population at low densities in Japanese tea fields.
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Department of Nutrition and Food Hygiene, School of Public Health, Anhui Medical University, Hefei, 230032, China.
Sci Rep
December 2024
College of Horticulture and Plant Protection, Henan University of Science and Technology, Luoyang, 47100, China.
Tea bud detection technology is of great significance in realizing automated and intelligent plucking of tea buds. This study proposes a lightweight tea bud identification model based on modified Yolov5 to increase the picking accuracy and labor efficiency of intelligent tea bud picking while lowering the deployment pressure of mobile terminals. The following methods are used to make improvements: the backbone network CSPDarknet-53 of YOLOv5 is replaced with the EfficientNetV2 feature extraction network to reduce the number of parameters and floating-point operations of the model; the neck network of YOLOv5, the Ghost module is introduced to construct the ghost convolution and C3ghost module to further reduce the number of parameters and floating-point operations of the model; replacing the upsampling module of the neck network with the CARAFE upsampling module can aggregate the contextual tea bud feature information within a larger sensory field and improve the mean average precision of the model in detecting tea buds.
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