This systematic review aims to summarize the current knowledge on biological effects of micro- and nanoplastics (MNPs) on human health based on mammalian systems. An extensive search of the literature led to a total of 133 primary research articles on the health relevance of MNPs. Our findings revealed that although the study of MNP cytotoxicity and inflammatory response represents a major research theme, most studies (105 articles) focused on the effects of polystyrene MNPs due to their wide availability as a well characterised research material that can be manufactured with a large range of particle sizes, fluorescence labelling as well as various surface modifications. Among the 133 studies covered in this review, 117 articles reported adverse health effects after being exposed to MNPs. Mammalian in vitro studies identified multiple biological effects including cytotoxicity, oxidative stress, inflammatory response, genotoxicity, embryotoxicity, hepatotoxicity, neurotoxicity, renal toxicity and even carcinogenicity, while rodent in vivo models confirmed the bioaccumulation of MNPs in the liver, spleen, kidney, brain, lung and gut, presenting adverse effects at different levels including reproductive toxic effects and trans-generational toxicity. In contrast, the remaining 16 studies indicated an insignificant impact of MNPs on humans. A few studies attempted to investigate the mechanisms or factors driving the toxicity of MNPs and identified several determining factors including size, concentration, shape, surface charge, attached pollutants and weathering process, which, however, were not benchmarked or considered by most studies. This review demonstrates that there are still many inconsistencies in the evaluation of the potential health effects of MNPs due to the lack of comparability between studies. Current limitations hindering the attainment of reproducible conclusions as well as recommendations for future research directions are also presented.

Controlling crop diseases and pests is essential for intelligent agriculture (IA) due to the significant reduction in crop yield and quality caused by these problems. In recent years, the remote sensing (RS) areas has been prevailed over by unmanned aerial vehicle (UAV)-based applications. Herein, by using methods such as keyword co-contribution analysis and author co-occurrence analysis in bibliometrics, we found out the hot-spots of this field. UAV platforms equipped with various types of cameras and other advanced sensors, combined with artificial intelligence (AI) algorithms, especially for deep learning (DL) were reviewed. Acknowledging the critical role of comprehending crop diseases and pests, along with their defining traits, we provided a concise overview as indispensable foundational knowledge. Additionally, some widely used traditional machine learning (ML) algorithms were presented and the performance results were tabulated to form a comparison. Furthermore, we summarized crop diseases and pests monitoring techniques using DL and introduced the application for prediction and classification. Take it a step further, the newest and the most concerned applications of large language model (LLM) and large vision model (LVM) in agriculture were also mentioned herein. At the end of this review, we comprehensively discussed some deficiencies in the existing research and some challenges to be solved, as well as some practical solutions and suggestions in the near future.