Mepanipyrim induces visual developmental toxicity and vision-guided behavioral alteration in zebrafish larvae

Mepanipyrim,an anilinopyrimidine fungicide,has been extensively used to prevent fungal diseases in fruit culture.Currently,research on mepanipyrim-induced toxicity in organisms is still very scarce,especially visual developmental toxicity.Here,zebrafish larvae were employed to investigate mepanipyrim-induced visual developmental toxicity.Intense light andmonochromatic light stimuli-evoked escape experiments were used to investigate vision-guided behaviors.Meanwhile,transcriptomic sequencing and real-time quantitative PCR assays were applied to assess the potential mechanisms of mepanipyrim-induced visual developmental toxicity and vision-guided behavioral alteration.Our results showed that mepanipyrim exposure could induce retinal impairment and vision-guided behavioral alteration in larval zebrafish.In addition,the grk1b gene of the phototransduction signaling pathway was found to be a potential aryl hydrocarbon receptor(AhR)-regulated gene.Mepanipyrim-induced visual developmental toxicity was potentially related to the AhR signaling pathway.Furthermore,mepanipyrim-induced behavioral alteration was guided by the visual function,and the effects of mepanipyrim on long and middle wavelength light-sensitive opsins may be the main cause of vision-guided behavioral alteration.Our results provide insights into understanding the relationship between visual development and vision-guided behaviors induced by mepanipyrim exposure.

Computer vision in smart agriculture and precision farming:Techniques and applications

The transformation of age-old farming practices through the integration of digitization and automation has sparked a revolution in agriculture that is driven by cutting-edge computer vision and artificial intelligence(AI)technologies.This transformation not only promises increased productivity and economic growth,but also has the potential to address important global issues such as food security and sustainability.This survey paper aims to provide a holistic understanding of the integration of vision-based intelligent systems in various aspects of precision agriculture.By providing a detailed discussion on key areas of digital life cycle of crops,this survey contributes to a deeper understanding of the complexities associated with the implementation of vision-guided intelligent systems in challenging agricultural environments.The focus of this survey is to explore widely used imaging and image analysis techniques being utilized for precision farming tasks.This paper first discusses various salient crop metrics used in digital agriculture.Then this paper illustrates the usage of imaging and computer vision techniques in various phases of digital life cycle of crops in precision agriculture,such as image acquisition,image stitching and photogrammetry,image analysis,decision making,treatment,and planning.After establishing a thorough understanding of related terms and techniques involved in the implementation of vision-based intelligent systems for precision agriculture,the survey concludes by outlining the challenges associated with implementing generalized computer vision models for real-time deployment of fully autonomous farms.