Shiitake mushrooms are an important edible fungus, and their nutrient content is related to their quality. With the acceleration of urbanization, there has been a serious loss of population and shortage of labor in rural areas. The problem of harvesting agricultural products after maturity is becoming more and more prominent. In recent years, deep learning techniques have performed well in classification tasks using image data. These techniques can replace the manual labor needed to classify the quality of shiitake mushrooms quickly and accurately. Therefore, in this paper, a MobileNetV3_large deep convolutional network is improved, and a mushroom quality classification model using images with complex backgrounds is proposed. First, captured image data of shiitake mushrooms are divided into three categories based on the appearance characteristics related to shiitake quality. By constructing a hybrid data set, the model’s focus on shiitake mushrooms in images with complex backgrounds is improved. And the constructed data set is expanded using data enhancement methods to improve the generalization ability of the model. The total number of images after expansion is 10,991. Among them, the number of primary mushroom images is 3758, the number of secondary mushroom images is 3678, and the number of tertiary mushroom images is 3555. Subsequently, the SE module in MobileNetV3_large network is improved and processed to enhance the model recognition accuracy while reducing the network size. Finally, PolyFocalLoss and migration learning strategies are introduced to train the model and accelerate model convergence. In this paper, the recognition performance of the improved MobileNetV3_large model is evaluated by using the confusion matrix evaluation tool. It is also compared with other deep convolutional network models such as VGG16, GoogLeNet, ResNet50, MobileNet, ShuffleNet, and EfficientNet using the same experimental conditions. The results show that the improved MobileNetV3_large network has a recognition accuracy of 99.91%, a model size of 11.9 M, and a recognition error rate of 0.09% by the above methods. Compared to the original model, the recognition accuracy of the improved model is increased by 18.81% and the size is reduced by 26.54%. The improved MobileNetV3_large network model in this paper has better comprehensive performance, and it can provide a reference for the development of quality recognition and classification technologies for shiitake mushrooms cultivated in greenhouse environments.

High-precision mapping of soybeans, a globally significant oil and protein crop, has long been constrained by the limitations of traditional remote sensing methods, particularly regarding their inefficiency in fusing spatio-temporal information and suboptimal classification accuracy. Consequently, the rigorous demands of modern precision agriculture for crop monitoring have often not been adequately addressed. To address these challenges, a novel hybrid model was developed in this study for soybean identification using multi-temporal remote sensing imagery. This model is built upon an improved three-dimensional (3D) U-Net architecture, which synergistically integrates Bidirectional Long Short-Term Memory (Bi-LSTM) networks and Random Forest (RF) algorithms. This integration enables the effective extraction of multi-level features and subsequent integrated classification. A notable innovation of this research involves the incorporation of the Greenness-Water Content Composite Index (GWCCI) into the feature set. GWCCI quantifies canopy greenness and water status, improving discrimination of soybean phenology. Experimental validation was conducted within the primary soybean production region of Hailun City, Heilongjiang Province. The results demonstrated that the proposed model significantly outperformed 10 baseline methods (including standard 3D U-Net, CNN, and RF) across four key metrics: Overall Accuracy (OA), User Accuracy (UA), F1-score, and Kappa coefficient. Specifically, when benchmarked against a standard 3D U-Net, the developed model exhibited a 3.2% increase in OA, a 9.3% increase in UA, a 2.6% improvement in F1-score, and a 6.4% increase in the Kappa coefficient. These quantitative improvements highlight its enhanced capability in distinguishing soybeans from background crops. Ultimately, this research not only provides a novel methodological framework for precise crop classification but also contributes a viable multi-dimensional feature fusion strategy directly applicable to crop monitoring and intelligent agricultural management.

Navigation control system is an important navigation building of inland‐river in China. Because of its special semiclosed structure, when the efficiency of the mutation control system is low, the ship system cannot identify the ship, which may endanger the life safety of the crew and cause water traffic accidents. Based on catastrophe control and intelligent sensor, this paper studies the spatial distribution and fluctuation mechanism of risk in the ship traffic system, constructs a catastrophe control model, and combines with intelligent sensors to identify the spatial distribution and fluctuation of risk in ship traffic. In this paper, a typical navigation control system of inland‐river is selected as the research object, and the three‐dimensional calculation model of the ship traffic system is established. The numerical simulation of catastrophe control and intelligent sensor in ship traffic systems under different ventilation conditions and the navigation water level is carried out by using computational fluid dynamics method, and the spatial distribution and fluctuation mechanism of ship traffic systems are analyzed. The results show that the maximum variation of sensitivity decreases monotonously with the decrease of the plate angle. When the other design parameters of the sensor remain unchanged, the maximum variation of sensitivity reaches the minimum when the plate angle is 10° which indicates that a small plate angle is helpful to reduce the eccentricity error. When the pitch increases from 56.32 mm to 98 mm, the maximum change of sensitivity decreases fastest, and the decrease trend of the maximum change of sensitivity slows down. In the range of 380 mm pitch, the maximum change of sensitivity decreases monotonously with the increase of pitch, which indicates that increasing the properly pitch is helpful to reduce the eccentricity error.