Mechanized harvesting has been progressively supplanting manual harvesting in the domain of shellfish harvesting. A crucial technical quandary within this area pertains to the attainment of a high harvesting rate concomitant with a low damage rate under conditions of diminished digging force and minimal soil disturbance. This study investigates the effects of two types of shellfish harvesting methods, i.e., roller-type and vibratingtype, on the performance of digging shovel, and systematically compares their characteristics of harvesting rate, forces exerted on the shellfish, digging force, kinetic energy, and soil disturbance during the harvesting process. Additionally, the study also investigates the effects of different forward speeds and soil types on the performance of roller toothed (RT) shovel, roller flat (RF) shovel, vibrating toothed (VT) shovel, and vibrating flat (VF) shovel. Test results indicate that the harvesting rate of RT shovel is lower than that of vibrating digging shovel at shallow depths, but it has a certain cost advantage due to its capability to achieve fewer small shellfish. The VT shovel exhibits a relatively high harvesting rate (78.33 %) and exerts lower forces on shellfish at deeper depths. The digging force encountered by roller-type shovels exceeds that of vibrating shovels. As the digging depth increases, the roller-type shovels have a significant influence on the soil disturbance rate, while the vibrating-type excavation shovel has a relatively large effect on the soil bulkiness. At high forward speed, the digging force and kinetic energy of RT shovel are 1.78 times and 4.85 times those of vibrating shovel respectively. The roller-type digging shovel, especially the RF shovel, is not suitable for shellfish harvesting in compacted soil, because it needs to overcome larger digging force (i.e., 103.12 N) and leads to higher soil disturbance rate (i.e., 0.74). The vibrating shovel can adapt to various loose soils other than just high-density soil, and it experiences relatively small digging force and soil disturbance rate. This study provides theoretical guidance for the optimized design and application of mechanized shellfish harvesting, and its findings can further assist engineers in developing more energy-efficient and environmentally friendly harvesting machines, thereby demonstrating the greater practical value of the research.

Accurate prediction of indoor temperature is a crucial component for achieving intelligent control of heating processes. However, due to the inherent thermal inertia and response delay of heating systems, variations in indoor temperature exhibit markedly lagged and highly nonlinear characteristics. To overcome this challenge, this paper presents a structurally optimized and innovated deep learning prediction model based on the Transformer architecture. The results demonstrate that compared to other mainstream deep learning prediction models, the improved model achieves reductions in RMSE, MAE, and MAPE by $16.2 {\%}, 11 {\%}$, and 22.3%, respectively. Furthermore, the model's parameter count is substantially compressed, while prediction time is reduced by over 40%, effectively realizing a lightweight yet highly efficient architecture.

Identifying critical dimensions in computer-aided design (CAD) models of large welded structures, such as biomass boilers, often relies heavily on manual expertise and lacks unified discriminative criteria. To address these issues, this study proposes a multi-objective optimization method based on an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) framework for critical dimension identification under unlabeled or sparsely labeled conditions, where pseudo-labels are used only for search guidance. A four-driver rule system—incorporating functionality, assembly relevance, geometric dominance, and tolerance sensitivity—was constructed to evaluate the dimensional importance in typical biomass boiler components, including pressure-bearing parts, flange interfaces, and welded joints. Knowledge-guided encoding, adaptive crossover, and feasible-solution repair mechanisms are further integrated to enhance convergence efficiency and solution feasibility. A bi-objective model was designed to simultaneously optimize recognition performance and computational cost. Experimental evaluations of representative parts, assemblies, and complex support structures demonstrate that the proposed method achieves high-quality recognition with pseudo-label guidance for search and manual annotations for final evaluation, improving precision by an average of 2.7% over the standard NSGA-II and significantly accelerating convergence. The results indicate consistent performance across boiler CAD models of increasing complexity and suggest potential transferability to similar welded assemblies, providing a practical approach for parameter identification and design optimization in engineering applications.

The rapid identification of licorice seed varieties has great significance for guiding large-scale cultivation practices and ensuring quality control in the licorice industry. This study aims to develop a rapid, efficient, nondestructive classification of licorice seed varieties based on a portable near infrared (NIR) spectrometer combined with machine learning (ML) algorithms. Three varieties of medicinal licorice seeds -Glycyrrhiza uralensis Fisch., Glycyrrhiza inflata Bat., and Glycyrrhiza glabra L., -were prepared, and their NIR spectra were collected using a portable NIR spectrometer (900-1700 nm). The raw spectra were then preprocessed by combining SavitzkyGolay (SG) smoothing with multiplicative scattering correction (MSC), and characteristic variables were extracted via Variable Combination Population Analysis (VCPA), Bootstrapping Soft Shrinkage (BOSS), and Variable Iterative Space Shrinkage Approach (VISSA), respectively. Furthermore, four ML methods, including partial least squares discriminant analysis (PLSDA), support vector machine (SVM), random forest (RF), and back propagation neural network (BPNN), were employed to establish classification models for the three varieties of licorice seeds. Among all the constructed models, our findings show that the VISSA-BPNN model, based on 55 characteristic variables selected from the 198 original wavelength variables, achieved the highest accuracy of 92.75% in identifying licorice seed varieties. The results highlight the effectiveness of the portable NIR spectrometer particularly when paired with VISSA-BPNN in enabling reliable and rapid identification of licorice seed varieties.

As an important characteristic crop in the Qinghai-Tibet Plateau, highland barley variety detection technology plays a key role in breeding improvement, food processing, and germplasm resource management. In recent years, deep learning-based machine vision methods have provided new ideas for seed detection. However, their application to highland barley, a special crop, still faces challenges. Most existing models are designed for plain crops and struggle to adapt to the phenotypic characteristics of highland barley seeds in high-altitude environments. Additionally, there is a lack of systematic phenotypic datasets to support model training. In view of this, this paper proposes a variety detection model for highland barley grains. By introducing an attention mechanism module and conducting multi-scale feature optimization on the head network of YOLOv8, the small target detection performance is enhanced. Furthermore, a loss calculation method more suitable for the characteristics of small target detection is adopted to further improve the overall detection performance of the model. Experimental results demonstrate that the effectiveness of the improved algorithm in this paper is significant. Compared with the original YOLOv8 model, the mean average precision (mAP) is increased by 6.1 percentage points, reaching 95.1%.

As an important oil crop in China, rapeseed (Brassica campestris) relies heavily on effective pest control to ensure both yield and quality. However, traditional detection methods suffer from high labor dependence, low efficiency, and poor real-time performance. To address these issues, this paper proposes an improved pest detection method based on YOLOv10s, named IAM-YOLO. Specifically, we propose a multi-scale fusion attention module (IAM), and further integrate existing modules, including the Convolution and Attention Fusion Module (CAFM) and the Efficient Channel Attention (ECA) module, to enhance the model’s feature extraction capability and detection accuracy. To validate the effectiveness of the proposed improvements, we constructed an image dataset containing multiple categories of rapeseed pests and designed a series of experiments. Experimental results demonstrate that our improved model outperforms the original YOLOv10s in terms of precision, recall, and mAP. In particular, the model incorporating IAM, CAFM, and ECA modules achieves a precision of 89.7%, representing a 3.9 percentage point improvement over the baseline model, showcasing excellent detection performance. These findings indicate that the proposed method holds significant potential for rapeseed pest detection and provides strong technical support for intelligent pest and disease monitoring in agriculture.

The detection of tea leaf blight (TLB) in UAV remote sensing images under intense lighting conditions is a challenging task. A large number of light spots are produced on the leaf surface by intense light, which results in information loss from the overexposed area; in addition, TLB disease spots are small and easy to overlook against a complex background. These factors lead to low accuracy of TLB detection by current methods. In this study, an efficient TLB detection method based on MLDNet is proposed for UAV remote sensing images under intense lighting conditions. Simulated infrared (SIR) images are obtained by reweighting and summing the RGB images channels after separation, and these generated SIR images can effectively supplement the information lost from the overexposed area. A multimodal fusion (MF) module is designed to fuse the RGB and SIR images. The design of the MF module is based on an asymmetric feature extraction strategy to ensure that the features of different modality images are effectively used. To solve the problem of small target detection, a super-resolution (SR) branch is designed, which uses the low- and high-level features extracted by the Backbone to reconstruct a highresolution (HR) feature map to guide detector learning and achieve accurate detection of small TLB disease spots. Furthermore, a lightweight Backbone is designed to significantly reduce the computational cost without affecting the detection accuracy. Experimental results show that the proposed method achieves good performance. Its precision, recall, and mAP@0.5 are 78.4 %, 67.4 %, 73.4 % respectively, values that are 9.1 %, 2.7 % and 5.2 % higher than for the baseline network YOLOv8s. The parameters of the MLDNet model require only 2.6 MB, less than a third of the memory consumption of YOLOv8s.

An accurate indoor-temperature prediction model during winter seasons is critical for optimizing boiler control strategies and ensuring thermal comfort. While existing data-driven approaches aim to capture temperature dynamics, they often struggle to effectively represent the different time-delayed effects and complex nonlinear characteristics between indoor temperature and multifactorial influences. These models are also characterized by either inadequate predictive performance or poor prediction stability. To address these limitations, this study proposes a novel Transformer-like model: the time-delay cross-correlation Transformer model. In this model, sequence recombination and time-delay cross-correlation blocks were innovatively designed to effectively capture the highly nonlinear interactions between indoor temperature and exogenous inputs with time delays. Historical operational data from an actual scenario were used to evaluate the performance of the proposed model. The results demonstrate that the root mean squared error, mean absolute error, and mean absolute percentage error reached 0.6918, 0.5662, and 0.8842, respectively, which were significantly lower than those of the current data-driven indoor-temperature prediction models. Moreover, compared to other Transformer-like models, the proposed model is more concise, efficient, and convenient for practical applications, achieving better prediction performance and stability in terms of indoor-temperature prediction for individual heating systems. This study provides direct guidance for the optimization of the boiler control strategy.

Hard-shell clams are highly valued for their nutritional and economic benefits, leading to an increase in their aquaculture scale. Harvesting these clams manually leads to low efficiency and high labor intensity; thus, a new type of hard-shell clam harvester has been designed to overcome this challenge. Based on biological characteristics and sediment properties of hard-shell clams, a 3D model of the harvester has been created utilizing SolidWorks software (version 2022), which has a working length of 980 mm, an excavation depth range from 0~12 mm, and an angle of entry of 22 degrees. To optimize the efficiency of the machine, a Discrete Element Method (DEM) simulation trial was conducted through a three-factor three-level experiment using EDEM software. Results indicated an optimal harvest efficiency of 91.17% with the machine achieving a running speed of 0.526 m/s, roller speed of 4.772 r/min, and excavation depth of 73.067 mm. Field experiments verified the feasibility of the harvester, demonstrating high accuracy when compared to simulation results.

With the ongoing transition of the global energy structure and increasingly stringent environmental regulations, biomass boilers, as a form of renewable energy utilization technology, are being widely adopted in the heating sector. However, the operation of traditional biomass boilers still relies heavily on manual experience, resulting in low energy efficiency and high operating costs. To address this issue, this study proposes a novel dynamic intermittent heating mode for small biomass boilers, developed based on heating theory and real-world operational data characteristics. Through orthogonal experimental design, the relationships between key operating parameters-including blower frequency, feed rate, and grate frequency-and the overall operating cost were quantitatively analyzed. A regression-based cost model was established and further optimized using an Adaptive Genetic Algorithm (AGA) to determine the minimum-cost parameter combination. The optimal operating conditions were obtained as follows: blower frequency 23.4 Hz, feed rate 7.0 Hz, and grate frequency 13.5 Hz. Experimental validation on a 2-ton CDZL1.4-80/60-S biomass boiler (fueled with wood pellets) demonstrated that, compared with conventional experience-based operation, the proposed heating strategy ensures stable indoor temperatures while reducing the average operating cost by 29.5 %, corresponding to seasonal savings of USD 13,345.2.

Tea leaf blight (TLB) is a common disease that is widely found in tea gardens. Accurate detection is the key to preventing TLB. Addressing the issues of dense tea leaves, small TLB spots, and inconsistent target distribution in uncrewed aerial vehicle (UAV) images taken at different altitudes, which lead to low detection accuracy in existing methods, this study proposes a detection method for TLB in multialtitude UAV images by integrating style transfer and detection networks. This method generates fake TLB leaf images using the D-CycleGan style transfer network, which reduces the cost of acquiring TLB datasets and increases the diversity of samples. A multialtitude tiny TLB detection network (MTTDNet) is constructed to accurately detect TLB spots in UAV images of different altitudes. Directional Multi-Conv (DMConv) is introduced into MTTDNet to expand the receptive field and enhance the perception of small-target TLBs. A composite structure block (CSB) is incorporated into the feature fusion stage of MTTDNet to reduce false and missed detections in leaf-dense regions by promoting multidimensional information interaction. In this study, the DNIoU loss function is used to guide the training of MTTDNet by calculating the intersection ratio and center distance of the predicted and target bounding boxes, enabling the network to converge quickly and achieve accurate detection. Compared to the baseline network, MTTDNet improved the AP values for the detection results on datasets taken by a UAV at altitudes of 8, 10, and 14 m by 4.6%, 4.1%, and 6.7%, respectively. Compared with advanced target detection methods such as YOLOv11 and Swin-Transformer, the AP values for the detection results of MTTDNet on the datasets taken at 8, 10, and 14 m were improved by more than 7.7%, 6.1%, and 2.8%, respectively. Hence, the proposed method has important applications in the task of TLB detection in UAV images at different altitudes.

Traditional biomass boiler operations predominantly rely on manual control, with adjustments made based on operator experience. This approach not only demands considerable manpower but also leads to suboptimal resource utilization. Addressing the specific operational dynamics of biomass boilers, this study introduces a controller designed around the STM32 microcontroller. This device employs a modular architecture and incorporates cost-effective chips and peripheral components. To enhance energy efficiency and the overall user experience, the controller utilizes pulse width modulation pulse modulation alongside a fuzzy PID control strategy. Capable of toggling between manual and automatic modes for local operations, it also supports data logging directly on the device or in the cloud. Tailored to meet diverse user requirements, the controller facilitates remote monitoring and management. It employs a strategic control mechanism for various motors, ensuring precise hot water temperature measurements within the boiler and enabling data uploads to cloud platforms. This not only conserves energy and reduces labor costs but also promotes environmental sustainability and operational efficiency. Through ongoing refinement and rigorous testing, the controller has achieved a level of maturity that underscores its readiness for widespread implementation.

The intelligent appearance quality classification method for Auricularia auricula is of great significance to promote this industry. This paper proposes an appearance quality classification method for Auricularia auricula based on the improved Faster Region-based Convolutional Neural Networks (improved Faster RCNN) framework. The original Faster RCNN is improved by establishing a multiscale feature fusion detection model to improve the accuracy and real-time performance of the model. The multiscale feature fusion detection model makes full use of shallow feature information to complete target detection. It fuses shallow features with rich detailed information with deep features rich in strong semantic information. Since the fusion algorithm directly uses the existing information of the feature extraction network, there is no additional calculation. The fused features contain more original detailed feature information. Therefore, the improved Faster RCNN can improve the final detection rate without sacrificing speed. By comparing with the original Faster RCNN model, the mean average precision (mAP) of the improved Faster RCNN is increased by 2.13%. The average precision (AP) of the first-level Auricularia auricula is almost unchanged at a high level. The AP of the second-level Auricularia auricula is increased by nearly 5%. And the third-level Auricularia auricula AP is increased by 1%. The improved Faster RCNN improves the frames per second from 6.81 of the original Faster RCNN to 13.5. Meanwhile, the influence of complex environment and image resolution on the Auricularia auricula detection is explored.

Shellfish culture heavy soils are suitable for the cultivation of marine organisms and are essential for the development of marine fisheries. To study both the interaction between heavy soil particles and that between the soil and soil-engaging components of agricultural machinery in shellfish culture, the simulation parameters in the model were determined. To study the interaction between soil particles in the viscous soil of shellfish culture with moisture content of 26.51% ± 1%. Discrete element method is used to establish the accumulation simulation experiment; the contact parameters between soil particles were calibrated. The response surface optimization technique was used to create the accumulation angle regression model. To study the interaction between the soil and soil-engaging components, the static friction coefficient between the heavy soil and soil-engaging components was determined by static friction experiment. The contact parameters between the soil and soil-engaging components were calibrated by the slope simulation experiment; the rolling distance regression model was established by response surface optimization methodology. The findings demonstrate that the optimized soil model can simulate the actual soil, and reflect the interaction between the heavy soil particles, soil, and the soil-engaging components of agricultural machinery, which not only provides a theoretical basis for the design and optimization of soil-engaging components of agricultural machinery in heavy soil, but also provides a new way for the research and development of agricultural machinery in a complex environment.

The small biomass boiler heating system (SBBHS) offers a cost-effective, convenient, safe, and environmentally friendly heating solution for small-scale users, providing notable social and economic advantages. Temperature prediction and control methods can enable SBBHS to operate more intelligently and autonomously, further minimizing heating expenses. This study focuses on a small biomass boiler heating system in Xinyang, Shandong, utilizing data-driven methods to analyze SBBHS performance in supply water temperature prediction and optimization. To achieve precise temperature predictions, an enhanced artificial neural network model is developed, trained, and validated, with the Levenberg-Marquardt optimization algorithm applied to adjust network weights and thresholds. Additionally, a feedback neural network is employed for short-term, 24-hour temperature predictions of the SBBHS. Experimental results demonstrate that this temperature prediction and control strategy ensures long-term indoor temperature stability and comfort while reducing heating costs. This research contributes to the intelligent upgrading and transformation of small biomass boiler control systems, enabling on-demand heating and reducing carbon emissions.

Fuel types are essential for the control systems of briquette biofuel boilers, as the optimal combustion condition varies with fuel type. Moreover, the use of coal in biomass boilers is illegal in China, and the detection of coals will, in time, provide effective information for environmental supervision. This study established a briquette biofuel identification method based on the object detection of fuel images, including straw pellets, straw blocks, wood pellets, wood blocks, and coal. The YoloX-S model was used as the baseline network, and the proposed model in this study improved the detection performance by adding the self-attention mechanism module. The improved YoloX-S model showed better accuracy than the Yolo-L, YoloX-S, Yolov5, Yolov7, and Yolov8 models. The experimental results regarding fuel identification show that the improved model can effectively distinguish biomass fuel from coal and overcome false and missed detections found in the recognition of straw pellets and wood pellets by the original YoloX model. However, the interference of the complex background can greatly reduce the confidence of the object detection method using the improved YoloX-S model.

Black fungus, with high nutritional and medicinal value, has been cultivated in China for a long time, and Heilongjiang alone accounts for about 40% of the global output. At present, the cultivation of black fungus derives mainly from the inheritance of relatively primitive practices and experience of farmers, resulting in inconsistent quality of fungus. In this study, a smart control system for the precision cultivation of black fungus was designed by using intelligent detection and control technology. The system includes a precision culture test environment and remote control system. The precision cultivation environment contains four sub-independent environments. The key parameters such as temperature, humidity, and light behavior were collected and can be adjusted individually, according to the precision cultivation stages. The intelligent remote control system included a controller cabinet, sensors unit, temperature control unit, humidity control unit, light control unit, and information transmitting unit. The controller cabinet includes a key controller which can auto-control the temperature, and humidity, and lightly adjust components according to the precision cultivation conditions and processing. The temperature sensors were installed in a 3D array close to the fungus bags about 5 cm in rooms. The light tape was installed on the six walls and also had three colors (Red, Blue, and Green) which could be controlled independently in each room. The control strategy through the analysis of the data collected by all sensors, the current cultivate situation of the cultivation environment was obtained, and the heater, fan, light, and nozzle were regulated according to the strategy to maintain a suitable precision cultivation environment for fungus. To verify the feasibility of the precision cultivation processing and control system, the test result shows that the error of temperature control was about 0 degrees C-1 degrees C, the error of humidity control was about 1%-4%, and the error of illuminance control was about 0-50 lx; All the verification results show that the control system for precision cultivation has high precision and can meet the needs of exploring the "Black 29" fungus cultivation experiment environment. Based on the orthogonal experiment, the best combination of the temperature and humidity for each growth stage was also investigated in this study, further proving the reliability and feasibility of the control system for the precision cultivation of Auricularia auricula.

This study presents a real-time identification method for improving the accuracy and efficiency of counting hard clam larvae, overcoming the limitations of traditional manual methods. The proposed approach enhances the YOLOX-S network by incorporating attention mechanism modules into feature layers and up-sampling processes. The proposed model is subjected to comparative evaluations with YOLOv3, YOLOv4, YOLOv5, and Faster R-CNN networks, demonstrating its superior performance. After 150 epochs, the model achieves a training loss of approximately 1.42 and a mean average precision of 96.50%, outperforming models without attention mechanisms. It accurately identifies the quantity, survival rate, length, and width of hard clam larvae, providing valuable insights for quality prediction in the aquaculture industry. These findings contribute to the advancement of shellfish farming practices, offering a precise and efficient tool for larval assessment and promoting sustainable aquaculture development.

In order to improve the solar energy utilization rate and output power of the solar power generation device,this paper takes the parabolic trough thermoelectric generation device as the research object,it proposes a new type of solar power generation device,which uses PLC as the controller and MCGS touch screen as the configuration interface.Through the feedback information of the illumination sensor,the step motor controls the concentrator,so that the condenser rotates around the north,south,east and west sides to track the sun’s height angle and azimuth angle,so as to improve the photoelectric conversion efficiency of the solar power generation device.In order to verify the feasibility of the tracking control system of the trough type solar thermal power generation device,the power generation capacity of the device was measured.The test results showed that the device supplied power to the load of the solar greenhouse for 10consecutive days,the total power generation time was 52.01h,and the total power generation was 2.74kW·h,which could meet the daily power consumption of the solar greenhouse seedlings.The structure of the device is flexible,and the power generation of the device can be further improved by increasing the number of series parallel hybrid connected thermoelectric generators.

In order to monitor the early growth status of clam seedlings and meet the demands of precision seedling cultivation in a factory setting, this study improves the YOLOv5s model and proposes a high-precision and lightweight method for detecting biological features of clam seedlings. Firstly, dataset of clam seedlings under the microscope was constructed. Secondly, we established a clam seedling detection model based on four different attention mechanisms and analyzed the differences in the features focused on by different attention mechanisms in clam seedling detection. The accuracy of the anchors was optimized using the K-means++ al-gorithm, and the Soft-NMS method was employed to address the issue of missed detections caused by dense stacking of clam seedlings. The proposed approach has the optimal comprehensive performance by comparing various lightweight networks. The results show that the AP-health, AP-death and mAP of this approach are 98.15%, 93.87% and 96.01%, which can meet the standard for high precision. With a transmission speed of 86 FPS, an average response time of 11.54 ms, and a model size of 27.59 MB, the approach satisfies the requirements for efficiency and portability. Finally, a biological feature detection software of clam seedlings was developed, which can automatically calculate phenotypic information such as the number of clam seedlings, survival rate, and average size. This paper provides a basis for real-time and accurate assessment of clam seedlings biological features while offering technical support for the automation and intelligence of clam seedlings production in factories.