MCSNet+: Enhanced Convolutional Neural Network for Detection and Classification of and Sibling Species in Actual Wheat Storage Environments.

Insect pests like and siblings are major threats to grain storage and processing, causing quality and quantity losses that endanger food security. These closely related species, having very similar morphological and biological characteristics, often exhibit variations in biology and pesticide resistance, complicating control efforts. Accurate pest species identification is essential for effective control, but workplace safety in the grain bin associated with grain deterioration, clumping, fumigator hazards, and air quality create challenges.

Online temperature-monitoring technology for grain storage: a three-dimensional visualization method based on an adaptive neighborhood clustering algorithm.

Background: Post-harvest quality assurance is a crucial link between grain production and end users. It is essential to ensure that grain does not deteriorate due to heating during storage. To visualize the temperature distribution of a grain pile, the present study proposed a three-dimensional (3D) temperature field visualization method based on an adaptive neighborhood clustering algorithm (ANCA).

Auto-identification of two Sitophilus sibling species on stored wheat using deep convolutional neural network.

Background: Sitophilus oryzae and Sitophilus zeamais are the two main insect pests that infest stored grain worldwide. Accurate and rapid identification of the two pests is challenging because of their similar appearances. The S.

Effects of Ezrin and Heat Shock Protein 70 on Apoptosis and Proliferation of Human Osteosarcoma Cells.

Objective: To investigate the influence of knocking down ezrin expression in combination with heat shock protein (HSP)-induced immune killing on the apoptosis and proliferation of mouse osteosarcoma cells.

Methods: The HSP70 and ezrin-shRNA DNA fragments cloned into the expression vector pGFP-V-RS and the expression vectors pGFP-V-RS-shRNA and pGFP-V-RS-shRNA-HSP70 constructed and transfected into MG63 cell line, where their status was observed by fluorescent microscopy. Expression of ezrin and HSP70 was determined by RT-PCR and western blot.

[Research on identification of cabbages and weeds combining spectral imaging technology and SAM taxonomy].

Weeds automatic identification is the key technique and also the bottleneck for implementation of variable spraying and precision pesticide. Therefore, accurate, rapid and non-destructive automatic identification of weeds has become a very important research direction for precision agriculture. Hyperspectral imaging system was used to capture the hyperspectral images of cabbage seedlings and five kinds of weeds such as pigweed, barnyard grass, goosegrass, crabgrass and setaria with the wavelength ranging from 1000 to 2500 nm.