As an important branch of production scheduling, the flexible job shop scheduling problem (FJSP) is a typical NP-hard problem. Researchers have adopted many intelligent algorithms to solve the FJSP problem, nonetheless, the task of dynamically adapting its essential parameters during the computational process is a significant challenge, resulting in the solution efficiency and quality failing to meet the production requirements. To this end, this paper proposes an adaptive gray wolf fast optimization algorithm (SS-GWO), which adopts the gray wolf algorithm (GWO) as the basic optimization method, and the algorithm adaptively selects the global search or local search according to the degree of agglomeration of individuals. Firstly, a non-linear convergence factor strategy is employed to control the global exploration and local exploitation capabilities of the algorithm at different stages. This enhances optimization precision and accelerates convergence speed, achieving a dynamic balance between the two. Secondly, the spiral search mechanism of Whale Optimization Algorithm is used in GWO to improve the exploration capability of Gray Wolf Optimization Algorithm. Finally, the effectiveness of SS-GWO model is verified by comparison experiments. The comparison demonstrates the superiority of SS-GWO over the other five state-of-the-art algorithms in solving the 22 classical benchmark test functions. SS-GWO is applied to solve FJSP by means of the standard test function bandimarte calculus. The optimal solution and performance of SS-GWO for solving FJSP are compared with other algorithms. The experimental results show that the SS-GWO algorithm has good optimization performance, and the maximum completion time is reduced by 19% and 37% compared with that of IGWO and GWO, respectively, and the proposed SS-GWO algorithm achieves a better solution effect on flexible job shop scheduling instances, which can satisfy the actual production scheduling needs.

Corn harvester row alignment is a critical factor to improve harvesting quality and reduce cob drop loss. In this paper, a corn harvester automatic row alignment system is designed with a self-propelled corn combine harvester, incorporating a front-end touch row alignment mechanism of the cutting table and a harvester steering control system. Corn stalk lateral deviation from the reference row during harvesting is detected by a front-end touch-row alignment mechanism that serves as an input to the automatic alignment system. The harvester steering control system consists of Hirschmann PLC controller, electric steering wheel, steering wheel deflection angle detection device, display module and mode selection module, etc. The adaptive fuzzy PID control algorithm is used to determine the desired turning angle of the harvester steering wheel by combining with the harvester kinematic model, and the model is simulated and analyzed by Matlab/Simulink software. The automatic row alignment system was mounted on a 4LZ-8 self-propelled corn harvester for field tests, and the test results showed that the average percentage of deviation of corn stalks from the center of the row alignment cutting path within ±15 cm during the automatic row alignment process was 95.4% at harvester speeds ranging from 0 to 4.6 km/h, which could meet the requirements of the corn harvester for row alignment harvesting. The test results meet the requirements of the corn harvester for row alignment and serve as a benchmark for the research on automatic row alignment of corn harvesters.

Reducing ear damage is the key to improving the quality of maize harvests. In order to reduce the impact and damage of the ear caused by the ear snapping mechanism, this paper proposes a method to convert ear deformation energy during collision into elastic potential energy in the ear snapping mechanism. According to the above method, a low-damage maize snapping mechanism was designed. In order to verify the feasibility of energy conversion in reducing damage, the dynamic model of the contact between the ear and the snapping plate was established, and a dynamic simulation analysis was carried out based on the finite element method (FEM). In order to obtain better parameters for the improved mechanism, a test rig was established, after which a performance test was carried out on the test rig. The results showed that the primary and secondary order that affected the ear damage rate was the rotational speed of the snapping roller, the spring stiffness and the forward speed. The data processing software Design Expert was used to optimize the parameters, it was concluded that when the rotational speed was 805 r·min⁻¹, the forward speed was 1.29 m·s⁻¹, the spring stiffness was 33.5 N·mm⁻¹, the model predicted that the ear damage rate was 0.023%. Therefore, this paper could provide further reference for research into maize low-damage ear snapping technology.

Fruit size is a key trait in small-fruit breeding, yet its measurement remains labor intensive and prone to human error. To address this, we developed a non-destructive, automated size measurement system based on machine vision and LabVIEW, designed for small fruits such as cherry, blueberry, and walnut. The system integrates a modular architecture, including flat-field correction, calibration, and pattern matching sub-VIs, to ensure user-friendly operation. These sub-VIs also enable the system’s core data analysis, such as real-size conversion through calibration and noise reduction for data accuracy through flat-field correction. An optimized image processing pipeline (grayscale conversion, Canny edge detection, morphological operations) enables precise contour extraction, even for fruits with stems or irregular surfaces. The system supports multi-species adaptation through lightweight parameter adjustments, without hardware modification. Experiments involved 15 samples per species (cherry ‘Tieton’, blueberry ‘Northland’, walnut ‘Xiangling’). A gold-standard protocol was established using a pre-calibrated digital caliper operated by two experienced technicians, with the mean of six replicates per fruit defined as the true value. Results demonstrated low root mean square errors, with coefficients of determination (R²) exceeding 0.98. Paired t-tests confirmed no significant differences from the gold standard. The system achieved a measurement speed of 0.4 s per fruit, six times faster than manual methods, and complied with the precision requirements of GB/T 26906-2024 (Sweet Cherry). This system offers a cost-effective, high-throughput solution for fruit breeding and phenotyping, effectively overcoming the limitations of manual measurement.

Aiming at the problems of inter-row straw congestion, soil accumulation, and consequent uneven seeding depth during high-speed sowing with narrow row spacing under the summer maize no-tillage sowing mode in the Huang-Huai-Hai region, this study proposed a maize seedling belt pre-sowing treatment device suitable for narrow row spacing operation by analyzing the physical properties of straw and soil in the region. Dynamic analysis of the mechanical device was carried out, and the key factors affecting the straw removal effect of the seedling belt and the degree of soil disturbance were identified as machine offset distance, traction speed, and straw-cleaning wheel angle. Discrete element method simulation experiments were conducted via EDEM-ADAMS coupling; the key factors were simulated and optimized, and the optimal parameter combination of the device was determined as follows: machine offset distance of 165 cm (the relative distance between the front and rear positions of the right wheel of adjacent unit cleaning components), traction speed of 11 km/h, and straw-cleaning wheel angle of 44°. Field validation tests of the prototype were performed. The test results showed that the overall straw removal rate of the seedling belt reached 95%, and no large-scale straw and soil accumulation caused by pushing was observed between rows. Compared with the simulation results, the error of straw removal rate was only 0.5%. Sowing comparison tests were conducted, and the results indicated that the device could significantly improve the uniformity of seeding depth and meet the seedling belt quality requirements for high-speed sowing with narrow row spacing of summer maize. This study provides new ideas and methods for the design of straw-cleaning mechanisms in no-till seeding systems.

For small to medium-sized fruits, such as cherries and blueberries, phenotypic data such as fruit size and various diameters are key indicators for fruit breeding, providing essential references for the breeding process. Currently, manual measurements that are widely used are inefficient and introduce significant uncertainties. To address this, this study designed breeding equipment that leveraged machine vision technology to capture fruit images and perform size measurements and evaluations using LabVIEW programming. Using cherries as an example, the RGB images were converted to grayscale using a weighted average method, followed by flat-field correction to remove artifacts and noise. The Canny algorithm was then applied to extract the fruit contour, and the Otsu algorithm was employed for automatic thresholding in the image binarization. Morphological processing separated the fruit from the stem, and template-matching technology was used to define the region of interest (ROI) for measurement. The system achieved an average relative error of only 0.35%, with a standard deviation of 0.0828 and a measurement time of 2.547 s per cherry. This method significantly improved the efficiency and saved time compared to traditional manual measurements. The device successfully measured fruits such as cherries and blueberries, providing a new tool for fruit size measurement in the breeding process and offering valuable insights for the design of future equipment.