Microplastic (MP) pollution is increasingly viewed as a serious threat to waterways. However, little is known about the effects of land use and rainfall patterns on the occurrence and distribution of MPs in the river sediments. Herein, the MP polluti...
The 100 years of research, development, commercialization and use of entomopathogenic nematodes (EPN) since their discovery in the 1920s have seen impressive progress. Nowadays, many EPN products, based on a relatively limited number of EPN species, ...
Understanding how pedoclimatic drivers regulate soil organic carbon (SOC) stock is crucial for gaining insights into terrestrial carbon-climate feedback and thus adaptation to climate change. However, current data-driven SOC predictive models often n...
The surface soil organic carbon (SOC) dynamics typically follow a trend of initial loss followed by subsequent accumulation after cropland abandonment. However, the timing of SOC stock increase (referred to as the threshold in this study) remains ins...
Soil nitrous acid (HONO) emissions influence air quality by affecting atmospheric oxidizing capacity and secondary pollutant formation. However, estimating soil HONO emissions remains uncertain due to complex factors and limited data. Here, we presen...
In this study, machine learning was used to optimize the aerobic composting process of swine manure to enhance nitrogen retention and compost maturity in order to meet the demand for high-quality organic fertilizers in sustainable agriculture. In thi...
Crashes involving farm equipment vehicles are a significant safety concern on public roads, particularly in rural and agricultural regions. These vehicles display unique challenges due to their slow-moving operational speed and interactions with fast...
The widespread use of plastic agricultural films necessitates a thorough evaluation of environmental risks posed by soil microplastics (MPs). While the intestinal tract is a critical site for MP interactions in soil organisms, current research predom...
In this study, we introduce a groundbreaking deep learning (DL) model designed for the precise task of classifying common diseases in tea leaves, leveraging advanced image analysis techniques. Our model is distinguished by its complex multi-layer arc...
Agricultural diseases pose significant challenges to plant production. With the rapid advancement of deep learning, the accuracy and efficiency of plant disease identification have substantially improved. However, conventional convolutional neural ne...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.