The security of network information in the Internet of Things faces enormous challenges. The traditional security defense mechanism is passive and certain loopholes. Intrusion detection can carry out network security monitoring and take corresponding measures actively. The neural network-based intrusion detection technology has specific adaptive capabilities, which can adapt to complex network environments and provide high intrusion detection rate. For the sake of solving the problem that the farmland Internet of Things is very vulnerable to invasion, we use a neural network to construct the farmland Internet of Things intrusion detection system to detect anomalous intrusion. In this study, the temperature of the IoT acquisition system is taken as the research object. It has divided which into different time granularities for feature analysis. We provide the detection standard for the data training detection module by comparing the traditional ARIMA and neural network methods. Its results show that the information on the temperature series is abundant. In addition, the neural network can predict the temperature sequence of varying time granularities better and ensure a small prediction error. It provides the testing standard for the construction of an intrusion detection system of the Internet of Things.

Background: Pyrethroids have been widely used in residential and agricultural areas. However, little is known about the effects of prenatal exposure to deltamethrin on cognition in early development of offspring. In this study, the effects of prenatal exposure to deltamethrin on learning and memory abilities, N-methyl-D-aspartate receptor (NMDAR) subunits, brain derived neurotrophic factor (BDNF), Tyrosine kinase B (TrkB) receptor, and phosphorylated cAMP response element binding protein (pCREB) in the hippocampus of offspring rats were investigated. Experimental Approaches: Groups each of six female SD rats, as F0-generation, were administered with deltamethrin (0, 0.54, 1.35, and 2.7, 9 mg/kg), or memantine (10 mg/kg), or co-administered with deltamethrin (9 mg/kg) and memantine (10 mg/kg) daily by gavage during pregnancy. The learning and memory ability was evaluated using Morris water maze (MWM) task on postnatal day 21. The expression of NMDAR (GluN1, GluN2A, and GluN2B), BDNF, pTrkB/TrkB, and pCREB/CREB in hippocampus were assessed with western blotting. Results: Prenatal exposure to a relatively low dose of deltamethrin (2.7, 1.35, and 0.54 mg/kg) had no impact on learning and memory abilities or the expression of NMDAR, BDNF, pTrkB, and pCREB in the hippocampus of the exposed offspring. The group treated with 9 mg/kg deltamethrin showed impaired cognitive abilities and decreased expression levels of GluN1, GluN2A, GluN2B, BDNF, pCREB/CREB, and pTrkB/TrkB in the hippocampus. However, the declined cognitive ability were ameliorated by memantine treatment with increased GluN1, GluN2A, GluN2B, BDNF, pCREB/CREB, and pTrkB/TrkB expression in the hippocampus. Conclusion and Implications: Prenatal exposure to a relatively high does of deltamethrin (9 mg/kg) alters cognition in offsprings and that this cognitive dysfunction can be ameliorated by memantine treatment. Moreover, NMDAR/BDNF signaling may be associated with the effects of prenatal exposure to deltamethrin on cognitive ability in offspring.

In recent years, there have been several outbreaks of extensive flooding in northern provinces, and asparagus stem blight disease and brown spot diseases have become increasingly serious, significantly reducing the yield of asparagus grown in large fields, so asparagus production has developed toward facility agriculture. In order to solve the problems of high labor cost, labor shortage, and low production efficiency faced by facility agriculture, it is an inevitable trend to adopt mechanized harvesting for asparagus. The prerequisite for mechanized harvesting is target detection. Compared with algorithms such as Faster R-CNN, which need to use the features of candidate regions for classification and recognition and cannot meet the real-time requirements of mechanized harvesting, this paper proposes a YOLO-based asparagus recognition scheme that can quickly perform target detection with a detection accuracy of 85.45% and significantly enhanced interference resistance, which can greatly improve the production efficiency of facility agriculture and accelerate the mechanization process of facility agriculture.

Asparagus stem blight, also known as “asparagus cancer”, is a serious plant disease with a regional distribution. The widespread occurrence of the disease has had a negative impact on the yield and quality of asparagus and has become one of the main problems threatening asparagus production. To improve the ability to accurately identify and localize phenotypic lesions of stem blight in asparagus and to enhance the accuracy of the test, a YOLOv8-CBAM detection algorithm for asparagus stem blight based on YOLOv8 was proposed. The algorithm aims to achieve rapid detection of phenotypic images of asparagus stem blight and to provide effective assistance in the control of asparagus stem blight. To enhance the model’s capacity to capture subtle lesion features, the Convolutional Block Attention Module (CBAM) is added after C2f in the head. Simultaneously, the original CIoU loss function in YOLOv8 was replaced with the Focal-EIoU loss function, ensuring that the updated loss function emphasizes higher-quality bounding boxes. The YOLOv8-CBAM algorithm can effectively detect asparagus stem blight phenotypic images with a mean average precision (mAP) of 95.51%, which is 0.22%, 14.99%, 1.77%, and 5.71% higher than the YOLOv5, YOLOv7, YOLOv8, and Mask R-CNN models, respectively. This greatly enhances the efficiency of asparagus growers in identifying asparagus stem blight, aids in improving the prevention and control of asparagus stem blight, and is crucial for the application of computer vision in agriculture.