Targeting the problems of low precision and heavy workload in conventional screening of filled and unfilled grain in single-plant rice testing, a screening system for filled and unfilled grain was designed based on the coupling of the wind and gravity fields. In this study, the motion state of filled and unfilled grain in the flow field and the results of screening were analyzed and combined with aerodynamics. In order to reveal the influence law of the structural and working parameters of the screening system on the screening performance and determine the optimal parameter combination, this study conducted a quadratic regression orthogonal rotating center combination test with four factors and three levels based on the DEM–CFD coupling method. The relationship between air inlet wind speed, air cross-section shape, horizontal distance, vertical distance, and removal rate was studied. The results showed that, in a certain range, the removal rate was positively correlated with the section width of the outlet, positively correlated with the wind speed, and negatively correlated with the vertical distance and horizontal distance of the seed-drop outlet. The optimization results showed that, when the section width of the outlet was 75.44 mm, the wind speed was 8.90 m·s⁻¹, the transverse distance was 198.78 mm, and, when the vertical distance was 34.87 mm, the screening rate of the screening system could reach 99.6%.

Excessive milling of rice kernels will result in nutrient loss and grain waste. To avoid grain waste, multibreak milling systems have been widely used in large-scale commercial rice mills. However, there is still no reasonable breakpoint planning method to guide the multibreak milling process. To construct a reasonable multibreak milling system, in this research, taking rice milling, a typical heterogeneous cereal-kernel milling process, as an example, the multivariate analysis method was used to comprehensively analyze the characteristic changes of milled rice during the whole milling process. A breakpoint planning method was established, including planning the number of breakpoints, determining the degree of milling or milling time corresponding to each breakpoint, and estimating the actual breakpoint to which the milled rice belongs. The verification results showed the rationality and high accuracy of the planning method. The presented work will help operators to plan the multibreak milling system of rice efficiently and objectively so as to significantly improve the commercial value of milled rice.

After undergoing peeling-machine operations, skinned garlic cloves affect subsequent processing, and their manual removal is harmful to health. In this paper, an intelligent garlic-clove-removal test bench was designed, which mainly included a hopper, lifter, vibration conveyor, conveyor belt, visual system, removal robot, control cabinet, frame, etc. A technical method based on machine vision technology to distinguish whether or not garlic cloves had a skin was explored to ensure that the test bench could complete the recognition of the skinned garlic cloves, and to check that the test bench could also complete the removal of skinned garlic cloves. Tests were carried out to check the success rate of machine vision and the removal robot, and to optimize the parameters of the test bench. The results showed that the average success rate of machine vision was 99.15%, and the average success rate of the removal robot was 99.13%. The results also showed that the order of the three factors influence index was the conveying speed, the conveying volume, and the removal period. The regression analysis showed that when the conveying speed was 0.1 m·s⁻¹, the grasping period was 1.725 s, the conveying volume was 104.4 kg·h⁻¹, the qualified rate of the finished product was 97.15%, and the verification test result was 97.02%, which had no significant difference from the analysis result. The research results of this paper are conducive to the development of intelligent detection technology of garlic cloves, and to the development of garlic-planting technology and deep processing technology.

Accurate ripeness assessment of tomatoes in natural orchard settings is challenged by severe occlusion, dense clustering, and scale variation among fruits. This paper introduces YOLOv8n–TiD, a lightweight detection framework designed to overcome these obstacles. The architecture enhances the YOLOv8n backbone with a novel C2f-iRD module, which integrates re-parameterized dilated convolutions and inverted residual blocks to enlarge the receptive field while retaining fine-grained texture details. For feature fusion, we propose C2f-iRMB in the neck to ensure cross-scale consistency. To address semantic drift during upsampling, we replace standard interpolation with the DySample operator. The detection head is re-engineered as TADAH (Task-Adaptive Dynamic Alignment Head), enabling genuine task decoupling via deformable convolutions and conditionally shared features. Additionally, we introduce F–PIoUv2, a regression loss that emphasizes medium-quality predictions and curbs excessive bounding box expansion. Evaluations on a custom dataset show that YOLOv8n–TiD cuts parameters by 45%, FLOPs by 19%, and model size by 41%, while raising mAP@0.5 by 1.9 points—all in real time. On Android devices, it sustains 30 FPS inference and generalizes effectively to a distinct cherry tomato cultivar. These findings confirm the method’s robustness in discriminating occluded, small, and visually similar maturity stages, providing a practical vision system for robotic harvesting and field-based grading.

Grain breakage serves as a primary causative factor for microbial infestation and oxidative deterioration, significantly diminishing product value and resulting in substantial grain waste and economic losses. The grain discharging process represents the most extensively involved and primary breakage-inducing stage throughout harvest handling and processing operations. However, impact and impact-induced breakage behavior during grain discharge are still poorly understood. To elucidate the impact-induced breakage behavior during grain discharge, this study first employed the discrete element method (DEM) to numerically simulate the discharging process, thereby quantifying the variation patterns of grain kinematic characteristics (e.g., velocity and attitude). Building upon the simulated kinematic data, a dedicated impact testing platform was constructed to investigate single-grain breakage. This enabled the determination of critical unit mass impact energy (along 90°: 106.4 J kg⁻¹; along 0°: 57.28 J kg⁻¹) and critical breakage velocity (along 90°: 14.59 m s⁻¹; along 0°: 10.70 m s⁻¹) under two extreme impact attitude conditions. By integrating the DEM-derived kinematics with the experimentally obtained breakage thresholds, a breakage probability zoning diagram for both large-scale and small-scale discharge processes was developed. Finally, leveraging this comprehensive understanding of the flow and breakage mechanics, theoretical models were successfully established to predict key engineering design parameters, including mass flow rate, impact force, and impact pressure. All models were validated and demonstrated excellent predictive capabilities. The research result is of guiding significance for the design of relevant parameters of discharge systems to minimize grain breakage loss to the greatest extent possible.