The potato-soil separation device comprising five function areas (i.e., shearing, bending, throwing, cleaning, and conveying) was developed to address the mentioned problems (e.g., soil with high moisture content, large viscosity, and poor separation efficacy) in the harvesting process for winter idle paddy fields of Southern China. Given the kinetic analyses for the potato-soil mixture of five function areas, the crucial factors of the effect of potato-soil separation were determined. Subsequently, the orthogonal test of 4 factors and 3 levels was performed, and the percentage of small soil λ, the percentage of soil adhering to potato tuber γ, and damage rate of potato tuber K were adopted as assessment indexes. Regression analyses and programming for the orthogonal test result were conducted to optimize the device run parameters, and the optimal run parameters included the rotational speed of the soil-crushing mechanism of 84.18 r/min, linear velocity of second-stage lifting chain of 0.75 m/s, and inclination angle of lifting chain of 15.87°. The field experiments were performed with the modified potato harvester. As indicated from the result, the visible potato rate was 94.31% and damage rate of potato was 0.89%, and the requirement of the national standard was satisfied. This study presented technical feasibility data and equipment for potato-soil separation under sticky soils condition.

Grain threshing is aimed at separating the grain from the inedible chaff. However, mechanical forces often damage grains, impacting their quality, market value, and germination ability. This comprehensive review examines theories and models developed to study and predict grain damage during threshing. These include contact theory, fracture mechanics models, discrete element modeling, and finite element analysis. This review delves into how these theories elucidate the influence of grain characteristics, such as moisture content and kernel size, on susceptibility to damage. It assesses how different machine parameters like threshing speed drum design and concave settings contribute to damage such as breakage, fissures, and internal cracks. We delve deeply into utilizing contact theory to estimate stress distribution when metal grains collide, employing fracture mechanics to understand crack initiation and propagation, and utilizing DEM and FEA to simulate how grains move within the thresher. By synthesizing knowledge from these modeling approaches, this review offers an understanding of the multifaceted nature of grain damage during threshing. They emphasize the significance of tuning settings and implementing suitable pre and post-threshing techniques to reduce waste and maintain top-notch grain quality for eating and seeding. This in-depth evaluation offers insights for scientists, engineers, and farming experts dedicated to enhancing the productivity and eco-friendliness of grain cultivation methods.

Accurate and robust recognition of daylily flower buds at the pre-bloom stage is essential for timely harvesting and quality preservation, yet remains highly challenging under natural field conditions due to the buds’ slender morphology, diverse orientations, dense distribution, and frequent occlusion by foliage. Existing horizontal-box object detectors struggle to capture the orientation and geometric structure of daylily buds, leading to inaccurate localization and unreliable guidance for automated harvesting. To address these challenges, we propose DDM-YOLO, a lightweight orientation-aware detection model tailored for daylily production environments. The model integrates three key components: (i) a Multi-scale Adaptive Feature Pyramid Network (MAFPN) that enhances the extraction and fusion of multi-dimensional features for densely distributed and occluded slender buds; (ii) a Lightweight Adaptive Direction-aware Head (LADH) that dynamically optimizes angle regression for rotated bounding boxes, improving orientation stability and reducing localization bias; and (iii) an Adaptive Down-sampling module (Adown) that preserves structurally critical spatial cues while reducing model complexity. Experiments conducted on a custom daylily field dataset demonstrate that DDM-YOLO achieves 96.8% precision and 98.1% mAP50, outperforming the baseline YOLOv11n-OBB by 1.3 percentage points in mAP while reducing model parameters by 17.0% to 2.2M. Deployment verification using a PySide5-based visualization prototype demonstrated a total system-level latency of less than 0.2 s, a duration encompassing the cumulative overhead of image input and output, pre-processing, post-processing including non-maximum suppression, and interface rendering. Furthermore, physical deployment on an NVIDIA Jetson AGX Orin embedded platform utilizing TensorRT optimization achieved an impressive inference speed of 114.5 FPS, corresponding to approximately 8.7 ms per frame. This performance confirms that the model meets the stringent real-time requirements for edge computing in mobile agricultural robotics. The model efficiently and accurately performs oriented detection and harvesting pose estimation for daylily buds, providing critical technical support for the visual perception system of harvesting robots.

Rice is a widely cultivated food crop worldwide, and threshing is one of the most important operations of combine harvesters in grain production. It is a complex, nonlinear, multi-parameter physical process. The flexible threshing device has unique advantages in reducing the grain damage rate and has already been one of the major concerns in engineering design. Using the measured test database of the flexible threshing test bench, the rotation speed of the threshing cylinder (RS), threshing clearance of the concave sieve (TC), separation clearance of the concave sieve (SC), and feeding quantity (FQ) are used as the input layer. In contrast, the crushing rate (Y_P), impurity rate of the threshed material (Y_Z), and loss rate (Y_S) are used in the output layer. A 4-5-3-3 artificial neural network (ANN) model, with a backpropagation learning algorithm, was developed to predict the threshing performance of the flexible threshing device. Next, we explored the degree to which the inputs affect the outputs. The results showed that the R of the threshing performance model validation set in the hidden layer reached 0.980, and the root mean square error ($RMSE$) and the average absolute error ($MAE$) were less than 0.139 and 0.153, respectively. The built neural network model predicted the performance of the flexible threshing device, and the regression determination coefficient R² between the prediction data and the experimental data was 0.953. The results showed revealed that the data combined with the ANN method is an effective approach for predicting the threshing performance of the flexible threshing device in rice. Moreover, the sensitivity analysis showed that RS, TC, and SC were crucial factors influencing the performance of the flexible threshing device, with an average relative importance of 15.00%, 14.89%, and 14.32%, respectively. FQ had the least effect on threshing performance, with an average threshing relative importance of 11.65%. Our findings can be leveraged to optimize the threshing performance of future flexible threshing devices.

Robotic pollination represents a pivotal component of smart agriculture, with foundational architectures for target recognition, path planning, and motion control having been progressively established. However, developing an efficient and robust pollination system that integrates perception, decision-making, and execution within real-world scenarios remains confronted with complex challenges. This study systematically reviews recent advancements in the field and distills the core technical issues of greenhouse robotic pollination into three primary domains: target detection and pose estimation, end-effector design, and pollination strategies combined with motion control. Focusing on the visual perception of flowers, actuator architecture, and operational tactics, this review synthesizes existing academic findings to evaluate the state-of-the-art in flower detection and pose estimation, characterize diverse end-effector designs, and analyze the evolutionary trajectory of motion control techniques. Specifically, the analysis encompasses the impact of detection algorithms on recognition accuracy and robustness, the structural classification and performance attributes of pollination mechanisms, and the optimization of control strategies. Furthermore, the study categorizes global research backgrounds, technical methodologies, and paradigmatic system cases, offering a critical evaluation of experiences in constructing automated pollination systems. Despite these advances, current robotic pollination technologies for peppers (chili) face significant bottlenecks characterized by immature methods for precise flower detection and pose estimation, the need for optimized specialized end-effector designs, and insufficient robustness in decision-making systems under dynamic environmental conditions. To address these issues, future development should prioritize constructing diverse, large-scale flower image and pose datasets while developing detection algorithms adaptable to complex environments to achieve high-precision identification. Additionally, implementing this system requires a hierarchical architecture where perception drives adaptive actuation. Deep learning models must localize flower targets and assess maturity in real-time, feeding coordinates to path planners that generate collision-free trajectories through foliage. These trajectories are executed via multimodal motion control, synchronizing the rigid manipulator with soft end-effectors. By embedding tactile feedback into the machine learning loop, the system creates a unified sensorimotor framework. This enables dynamic force modulation based on physical resistance, ensuring precise, non-destructive pollination tailored to chili plants.