Rice lodging not only brings trouble to harvesting but also reduces yield. Therefore, the effective identification of rice lodging is of great significance. In this paper, we have designed a bilinear interpolation upsampling feature fusion module (BIFF) to decompose the quadruple upsampling of the connected part of encoder and decoder into two double upsampling processes and insert the intermediate feature layer in the backbone network for feature fusion in this process. The global attention mechanism(GAM) attention module is added to the feature extraction network, allowing the network to effectively focus on the lodging regions, thus effectively improving the segmentation effect. The average accuracy of the improved network is 93.55%, mrecall is 93.65%, and mIoU is 88.10%, and the feasibility of the improvement is demonstrated by ablation experiments and comparison with other algorithms. In addition, the angle calculation method is designed by combining the detection algorithm, adding a detection head branch to the output results for reading the distance information from the depth camera, and combining the distance information with mechanical analysis to determine the relationship between the angle of the stalk and the vertical direction when the rice is upright, tilted and fallen. A comparison of the calculated angle with the actual measured angle gives a final average error of approximately 5.364%, indicating that the harvest boundary extraction algorithm in this paper is highly accurate and has value for application in real-time harvesting scenarios.

As rice yield rises, the issue of broken axis windings in mature rice threshing drums is becoming increasingly severe. To disclose the winding characteristics and signal traits of the drums during the threshing process of mature rice, this paper undertakes an analysis of the winding characteristics and signals of the drums in rice with the assistance of a vibration test and analysis system. Since rice can lead to drum winding and shaft breakage, this paper alters the driving mode of the drums to exhibit the influence of rice on them. Firstly, the transfer characteristics of the frame need to be studied and analyzed, followed by subsequent research. The test results indicate that the horizontal displacement of the cylinder axial trajectory rises with the growth of the transmission chain, while the vertical displacement drops with the growth of the transmission chain. Additionally, to investigate the effect of mature rice on the threshing performance of horizontal axis II and horizontal axis III, a control variable method was employed. This approach allowed for the observation of how mature rice influences the threshing roller by systematically adjusting both the threshing gap and the rotational speed of the roller. It can be discerned from the test results that with the reduction of the threshing gap of affected mature rice, the unstripped rate gradually declines, while the entrainment loss rate gradually increases. As the rotational speed of horizontal shaft roller III increases, the rate of uncleared material gradually decreases, while the rate of entrainment loss progressively rises. The research findings can furnish a reference for the winding characteristics of rice on rollers and the enhancement of vehicle driving comfort.

To address the challenges of large morphological variations of weed targets, background interference in close-range agricultural images, and limited computational resources for visual perception models, this paper proposes a sequential visual perception method for weed recognition and short-range 3D localization in controlled or semi-controlled close-range precision weeding scenarios. The proposed method consists of two main stages: weed detection and 3D localization. In the detection stage, a lightweight MCS-YOLOv8 model is constructed based on YOLOv8n. MobileNetV3 is introduced to reduce the number of parameters and computational complexity, while CBAM and Shape-IoU are adopted to enhance weed-related feature representation and improve bounding-box regression for irregular weed targets. In the localization stage, RAFT-Stereo is used as the initial disparity estimator, and a detection-guided adaptive WLS depth optimization strategy is designed by using the detection bounding boxes and confidence scores. This strategy refines the target-region depth information and supports short-range 3D coordinate estimation. Experimental results show that MCS-YOLOv8 contains 1.6 M parameters and requires 4.3 GFLOPs. Its mAP@0.5 and mAP@0.5:0.95 reached 94.1% and 65.0%, respectively, which were 2.0 and 2.7 percentage points higher than those of the YOLOv8n baseline. Meanwhile, the number of parameters and FLOPs were reduced by approximately 46.7% and 46.9%, respectively. In the 3D localization experiments under controlled conditions, the mean absolute errors in the X, Y, and Z directions were 2.2 mm, 2.6 mm, and 3.2 mm, respectively, with an average 3D Euclidean error of approximately 4.7 mm. Dynamic target experiments further demonstrated that the proposed pipeline could complete indoor dynamic target recognition, 3D coordinate updating, and laser pointing verification under controlled conditions. The results indicate that the proposed method shows effective weed detection and short-range 3D localization performance under controlled agricultural close-range imaging conditions, and can provide a reference for visual perception and end-effector pointing in controlled or semi-controlled close-range precision weeding equipment.