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.

Accurately diagnosing blockages in a threshing cylinder is crucial for ensuring efficiency and quality in combine harvester operations. However, in terms of blockage diagnostic methods, the current state of affairs is characterized by model-based approaches that can be highly time-consuming and difficult to implement, while data-driven approaches lack interpretability. To address this situation, we propose a temporal association rule mining (TARM)-based fault diagnosis method for identifying threshing cylinder blockages and discovering knowledge. This study performs field trials by varying the actual feed rate and obtains datasets for three blockage classes (slight, moderate, and severe). Firstly, a symbolic aggregate approximation (SAX) method is employed to reduce the data dimensionality and to construct the transaction set with a sliding window. Next, a cSpade method is used to mine and extract strong association rules by applying improved support, confidence, and lift indicators. With the established strong association rules, this study can comprehensively elucidate the variation pattern of each characteristic under several blockage failure conditions and can effectively identify blockage faults. The results demonstrate that the proposed method effectively distinguishes between three levels of blockage faults, achieving an overall diagnostic accuracy of 0.94. And the method yields precisions of 0.90, 0.92, and 0.99 and corresponding recalls of 0.90, 0.93, and 0.98 for slight, medium, and severe levels of blockage faults, respectively. Specifically, the knowledge acquired from the extracted strong association rules can effectively explain the operational characteristics of a combine harvester when its threshing cylinders are blocked. Furthermore, the proposed approach in this study can provide a reasonable and reliable reference for future research on threshing cylinder blockages.

Design and performance analysis of indoor calibration device for force-measuring system of the tractor three-point hitch