The wheat harvesting boundary line is vital reference information for the path tracking of an autonomously driving combine harvester. However, unfavorable factors, such as a complex light environment, tree shade, weeds, and wheat stubble color interference in the field, make it challenging to identify the wheat harvest boundary line accurately and quickly. Therefore, this paper proposes a harvest boundary line recognition model for wheat harvesting based on the MV3_DeepLabV3+ network framework, which can quickly and accurately complete the identification in complex environments. The model uses the lightweight MobileNetV3_Large as the backbone network and the LeakyReLU activation function to avoid the neural death problem. Depth-separable convolution is introduced into Atrous Spatial Pyramid Pooling (ASPP) to reduce the complexity of network parameters. The cubic B-spline curve-fitting method extracts the wheat harvesting boundary line. A prototype harvester for wheat harvesting boundary recognition was built, and field tests were conducted. The test results show that the wheat harvest boundary line recognition model proposed in this paper achieves a segmentation accuracy of 98.04% for unharvested wheat regions in complex environments, with an IoU of 95.02%. When the combine harvester travels at 0~1.5 m/s, the normal speed for operation, the average processing time and pixel error for a single image are 0.15 s and 7.3 pixels, respectively. This method could achieve high recognition accuracy and fast recognition speed. This paper provides a practical reference for the autonomous harvesting operation of a combine harvester.

A new three-dimensionally preserved troodontid specimen consisting of most of the skull, partial mandibles and six articulated cervical vertebrae (PMOL-AD00102) from the Early Cretaceous Yixian Formation of Beipiao, western Liaoning, China is identified asSinovenator changiion the basis of a surangular with a “T”-shaped cross-section. High-resolution computed tomographic data for the skull of this new specimen facilitated a detailed description of the cranial anatomy ofS. changii. New diagnostic features ofS. changiiinclude a well-developed medial shelf on the jugal, a slender bar in the parasphenoid recess, a lateral groove on the pterygoid flange of the ectopterygoid, and the lateral surface of the anterior cervical vertebrae bearing two pneumatic foramina. Our new observation confirms that the braincase ofSinovenatoris not as primitive as previously suggested, although it still shows an intermediate state between derived troodontids and non-troodontid paravians in having an initial stage of the subotic recess and the otosphenoidal crest. Additionally, this new specimen reveals some novel and valuable anatomical information of troodontids regarding the quadrate-quadratojugal articulation, the stapes, the epipterygoid and the atlantal ribs.

Wheat lodging provides important reference information for self-adaptive header control of a combine harvester. Aimed at real-time detection of wheat lodging, this paper proposed a detection method of wheat lodging location and area based on binocular vision. In this method, the angle relationship between the stem and vertical direction when wheat is upright, inclined, and lodging was determined by mechanical analysis. The discrimination condition of the wheat lodging degree was proposed based on the height of the visual point cloud on the surface of wheat crops. The binocular camera was used to obtain the image parallax of wheat within the harvesting region. The binocular camera optical axis parallel model was used to calculate the three-dimensional coordinate of wheat. Then, the height of the wheat stem was obtained by further analysis and calculation. According to the wheat stem height detected by vision, the location and area of wheat lodging within the combine harvester’s harvesting region were analyzed. A field experiment showed that the detection error of the wheat stem height was 5.5 cm and the algorithm speed was under 2000 milliseconds, which enabled the analysis and calculation of the wheat lodging location, contour, and area within the combine harvester’s harvesting region. This study provides key information for adaptive header control of combine harvesters.

The dents and cracks of cabbage caused by mechanical damage during transportation have a direct impact on both commercial value and storage time. In this study, a method for surface defect detection of cabbage is proposed based on the curvature feature of the 3D point cloud. First, the red-green-blue (RGB) images and depth images are collected using a RealSense-D455 depth camera for 3D point cloud reconstruction. Then, the region of interest (ROI) is extracted by statistical filtering and Euclidean clustering segmentation algorithm, and the 3D point cloud of cabbage is segmented from background noise. Then, the curvature features of the 3D point cloud are calculated using the estimated normal vector based on the least square plane fitting method. Finally, the curvature threshold is determined according to the curvature characteristic parameters, and the surface defect type and area can be detected. The flat-headed cabbage and round-headed cabbage are selected to test the surface damage of dents and cracks. The test results show that the average detection accuracy of this proposed method is 96.25%, in which, the average detection accuracy of dents is 93.3% and the average detection accuracy of cracks is 96.67%, suggesting high detection accuracy and good adaptability for various cabbages. This study provides important technical support for automatic and non-destructive detection of cabbage surface defects.

Aiming at the need to prevent agricultural machinery from colliding with obstacles in the operation of unmanned agricultural machinery, an obstacle detection algorithm using 2D lidar was proposed, and a pre-collision system was designed using this algorithm, which was tested on a harvester. The method uses the differences between lidar data frames to calculate the collision times between the farm machinery and the obstacles. The algorithm consists of the following steps: pre-processing to determine the region of interest, median filtering, and DBSCAN (density-based spatial clustering of applications with noise) to identify the obstacle and calculate of the collision time according to the 6σ principle. Based on this algorithm, a pre-collision system was developed and integrated with agricultural machinery navigation software. The harvester was refitted electronically, and the system was tested on a harvester. The results showed that the system had an average accuracy rate of 96.67% and an average recall rate of 97.14% for being able to stop safely for obstacles in the area of interest, with a summed average of 97% for both the accuracy and recall rates. The system can be used for an emergency stop when encountering obstacles in the automatic driving of agricultural machinery and provides a basis for the unmanned driving of agricultural machinery in more complex scenarios.