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

Insect pests like Tribolium and Sitophilus 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. Therefore, there is a pressing need for an online automated detection system. In this work, we enriched the stored-grain pest sibling image dataset, which includes 25,032 annotated Tribolium samples of two species and five geographical strains from real warehouse and another 1774 from the lab. As previously demonstrated on the Sitophilus family, Convolutional Neural Networks demonstrate distinct advantages over other model architectures in detecting Tribolium. Our CNN model, MCSNet+, integrates Soft-NMS for better recall in dense object detection, a Position-Sensitive Prediction Model to handle translation issues, and anchor parameter fine-tuning for improved matching and speed. This approach significantly enhances mean Average Precision (mAP) for Sitophilus and Tribolium, reaching a minimum of 92.67 ± 1.74% and 94.27 ± 1.02%, respectively. Moreover, MCSNet+ exhibits significant improvements in prediction speed, advancing from 0.055 s/img to 0.133 s/img, and elevates the recognition rates of moving insect sibling species in real wheat storage and visible light, rising from 2.32% to 2.53%. The detection performance of the model on laboratory-captured images surpasses that of real storage facilities, with better results for Tribolium compared to Sitophilus. Although inter-strain variances are less pronounced, the model achieves acceptable detection results across different Tribolium geographical strains, with a minimum recognition rate of 82.64 ± 1.27%. In real-time monitoring videos of grain storage facilities with wheat backgrounds, the enhanced deep learning model based on Convolutional Neural Networks successfully detects and identifies closely related stored-grain pest images. This achievement provides a viable solution for establishing an online pest management system in real storage facilities.

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). The ANCA-based visualization method contains four calculation modules. First, discrete grain temperature data, obtained by sensors, are collected and interpolated using back propagation (BP) neural networks to model the temperature field. Then a new adaptive neighborhood clustering algorithm is applied to divide interpolation data into different categories by combining spatial characteristics and spatiotemporal information. Next, the Quickhull algorithm is used to compute the boundary points of each cluster. Finally, the polyhedrons determined by boundary points are rendered into different colors and are constructed in a 3D temperature model of the grain pile. RESULTS: The experimental results show that ANCA is much better than the DBSCAN and MeanShift algorithms on compactness (around 95.7% of tested cases) and separation (approximately 91.3% of tested cases). Moreover, the ANCA-based visualization method for grain pile temperatures has a shorter rendering time and better visual effects. CONCLUSION: This research provides an efficient 3D visualization method that allows managers of grain depots to obtain temperature field information for bulk grain visually in real time to help them protect grain quality during storage. © 2023 Society of Chemical Industry.

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. zeamais adults are darker and shinier than S. oryzae in visible light. Convolutional neural network (CNN) can be applied for the effective differentiation due to its high effectiveness in object recognition. RESULTS: We propose a multilayer convolutional structure (MCS) feature extractor to extract insect characteristics within each layer of the CNN architecture. A region proposal network is adopted to determine the location of a potential pest in the wheat background. The precision of classification and the robustness of bounding box regression are increased by including deeper layer variables into the classification and bounding box regression subnets, as well as combining loss functions softmax and smooth L1. The proposed multilayer convolutional structure network (MCSNet) achieves the mean average precision of 87.89 ± 2.36% from the laboratory test, with an average detection speed of 0.182 ± 0.005 s per test. The model was further assessed with the field trials, and the obtained accuracy was 90.35 ± 3.12%. For all test conditions, the average precision for S. oryzae was higher than that for S. zeamais. CONCLUSION: The proposed MCSNet model has demonstrated that it is a fast and accurate method for detecting sibling species from visible light images in both laboratory and field trials. This will ultimately be applied for pest management together with an upgraded industrial camera system, which has been installed in over 100 000 grain depots of China.

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. Changes in cell apoptosis and proliferation were assessed by flow cytometry and MTS and changes in expression of apoptosis and cell cycle-related proteins by western blot. Specific cytotoxic T lymphocytes (CTLs) were induced by HSP70 and its lethal effect on target MG63 tumor cells analyzed by MTS assay. RESULTS: The specific vector simultaneously downregulated ezrin and upregulated HSP70. Compared with ezrin knockdown alone, simultaneous HSP70 overexpression partially recovered the promoted cellular apoptosis and proliferation suppression by induced by ezrin knockdown; however, the apoptosis rate of MG63 cells was significantly greater than that of a negative control. In addition, ezrin-shRNA and ezrin-shRNA/HSP70 promoted expression of Bax. However, expression of these agents reduces Bcl-2 and Cyclin D1. The cytotoxic effects of CTLs on target MG63 tumor cells were significantly greater in the CTL + IL-2 + HSP70 group than the CTL + IL-2 group. CONCLUSIONS: Simultaneously knocking down ezrin and overexpressing HSP70 promotes apoptosis and inhibits proliferation of osteosarcoma cells and HSP70 induces CTL, enhancing the lethal effect on tumor cells.

[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. In ENVI, by utilizing the MNF rotation to implement the noise reduction and de-correlation of hyperspectral data and reduce the band dimensions from 256 to 11, and extracting the region of interest to get the spectral library as standard spectra, finally, using the SAM taxonomy to identify cabbages and weeds, the classification effect was good when the spectral angle threshold was set as 0. 1 radians. In HSI Analyzer, after selecting the training pixels to obtain the standard spectrum, the SAM taxonomy was used to distinguish weeds from cabbages. Furthermore, in order to measure the recognition accuracy of weeds quantificationally, the statistical data of the weeds and non-weeds were obtained by comparing the SAM classification image with the best classification effects to the manual classification image. The experimental results demonstrated that, when the parameters were set as 5-point smoothing, 0-order derivative and 7-degree spectral angle, the best classification result was acquired and the recognition rate of weeds, non-weeds and overall samples was 80%, 97.3% and 96.8% respectively. The method that combined the spectral imaging technology and the SAM taxonomy together took full advantage of fusion information of spectrum and image. By applying the spatial classification algorithms to establishing training sets for spectral identification, checking the similarity among spectral vectors in the pixel level, integrating the advantages of spectra and images meanwhile considering their accuracy and rapidity and improving weeds detection range in the full range that could detect weeds between and within crop rows, the above method contributes relevant analysis tools and means to the application field requiring the accurate information of plants in agricultural precision management