Prior to dispatch from manufacturing facilities, seeders require rigorous performance evaluations for their seeding capabilities. Conventional manual inspection methods are notably less efficient. This study introduces a wheat seeding detection approach anchored in an enhanced YOLOv5s image-processing technique. Building upon the YOLOv5s framework, we integrated four CBAM attention mechanism modules into its model. Furthermore, the traditional upsampling technique in the neck layer was superseded by the CARAFE upsampling method. The augmented model achieved an mAP of 97.14%, illustrating its ability to elevate both the recognition precision and processing speed for wheat seeds while ensuring that the model remains lightweight. Leveraging this advanced model, we can effectively count and locate seed images, enabling the precise calculation and assessment of sowing uniformity, accuracy, and dispersion. We established a sowing test bench and conducted experiments to validate our model. The results showed that after the model was improved, the average accuracy of wheat recognition was above 97.55% under different sowing rates and travel speeds. This indicates that this method has high precision for the total number of seed particles. The sowing rate and sowing travel speed were consistent with manual measurements and did not significantly affect uniformity, accuracy, or dispersion.

In this paper, a prediction method of the heat transfer coefficient of composite vacuum glazing (CVG) is proposed. By analyzing the heat transfer process of CVG, the theoretical calculation formula for the heat transfer coefficient of CVG is established. CVG temperature variation under the test conditions specified in the national standard is simulated using ANSYS. The CVG heat transfer coefficient is calculated by combining the theoretical formula and simulation results. The simulation results are then verified by comparison to a physical experiment. The results show that the deviations between the experimental and predicted values are ≤3.8%, verifying the accuracy of the simulation results and proving that the model can be used in engineering practice. Furthermore, the effects of different coating positions on the heat transfer performance of CVG are studied. The results show that different coating positions have a significant impact on the heat transfer coefficient of CVG. The heat transfer coefficient is shown to be lowest to highest under the following conditions: when the Low-E coatings are located on both sides of the vacuum layer (2LC-V), followed by Low-E coatings on the side of glass pane II near the vacuum layer (1LC-V), Low-E coatings located on the side of glass pane I near insulating layer (1LC-I), and finally, when there are no Low-E coatings (NLC) on the glass panes. Overall, this model is an effective and accurate analysis method of the heat transfer coefficient.

To address the lack of specialized machinery for the mechanical weeding of SCSCP in the Huang Huai Hai region, this study designs a mechanized inter-row weeding machine for SCSCP. The machine features a reciprocating weeding shovel and an adaptive contouring mechanism for cultivation and soil loosening. This paper details the machine’s principles by analyzing the geometric relationship and mechanical model between the corresponding profiling quantities, which determine the relevant parameters for adaptive contouring to ensure stable operation on undulating ground. Furthermore, by optimizing the design of the weeding shovel’s reciprocating motion mechanism, combining EDEM simulation with the weeding shovel–soil interaction, it has been determined that, at various PTO shaft speeds, the optimal weeding efficacy is achieved with a blade-type weeding shovel structure when operating at a forward speed of 3.5 km/h. Field experiments were conducted with different PTO shaft speeds and weeding depths, using weeding and seedling injury rates as performance indicators. The results showed that, based on the optimal speed, the PTO shaft speed is 760 r/min, the operating depth is 3–5 cm, and the average row weeding rate is 90.4%. The average soybean and corn seedling injury rate is 3.4% and 4.2%, meeting the technical requirements for mechanical weeding.

This study aimed to assess the feasibility of a novel weeding and fertilization scheme, namely, mechanical weeding plus a one-time deep application of a reduced amount of slow-release fertilizer for rice cultivation. The effects of the weeding and fertilization method on rice yield and quality were investigated using a split plot test as the research method. Two weeding methods, namely, chemical weeding (CW) and mechanical weeding (MW), and four fertilization methods were tested, including the conventional fertilization method (quantitative split broadcast application of fast-release N fertilizer (CK)), the quantitative split broadcast application of 80% fast-release N fertilizer (LCK), the one-time base application of slow-release fertilizer (SR), and the one-time deep application of 80% slow-release fertilizer (LSR). The results showed that the rice yield under MW with LSR treatment can maintain a high level—higher than 9.2 t ha⁻¹ per year. This was attributed to the slow-release fertilizer and deep fertilization, which increased the number of stems and tillers in the pre-fertility and spike rate, respectively, resulting in a high panicle number with a 20% reduction of N fertilizer. Furthermore, mechanical weeding improved the seed-setting rate, resulting in a higher number of grains per panicle, a higher panicle number, and an increased thousand-grain weight, thereby maintaining a high yield. At the same time, the quality of rice under MW with LSR treatment improved, specifically reflected in the significant improvement of the processing and appearance quality of rice, a slight increase in protein content, and a reduction in the amylose content, thereby improving its nutritional quality while maintaining good cooking quality.

Current approaches to topdressing and weeding operations for rice cultivation present several disadvantages, such as poor precision, low efficiency, serious environmental pollution, and so on. This paper presents a row-controlled fertilizing–weeding machine to improve the precision of fertilizing and weeding operations and to reduce the heavy pollution associated with rice cultivation. A proportional–integral–derivative algorithm was adopted to realize accurate fertilization control, and an automatic driving system for agricultural machinery based on BeiDou navigation was used for accurate row-controlled operation. Accuracy testing and field experiments were carried out. The results show that the fertilization control system can stabilize the speed to within 0.55 s of the desired speed with a standard deviation of around 0.32 r·min⁻¹. The row-controlled operation ensures the lateral deviation is within ± 5 cm at operating speeds below 5 km·h⁻¹. The high uniformity and accuracy of fertilization meet agronomic requirements and rice cultivation standards, and the weeding performance is acceptable at working speeds below 5 km·h⁻¹.