To achieve the rapid and accurate classification and identification of soybean components, this study selected soybeans harvested by the 4LZ-1.5 soybean combine harvester as the research subject. Hyperspectral images of soybean samples were collected using the Pika L spectrometer, and spectral information was extracted from the regions of interest (ROI) in the images. Eight preprocessing methods, including baseline correction (BC), moving average (MA), Savitzky–Golay derivative (SGD), normalization, standard normal variate transformation (SNV), multiplicative scatter correction (MSC), first derivative (DS), and Savitzky–Golay smoothing (SGS), were applied to the raw spectral data to eliminate irrelevant information. Feature wavelengths were selected using the successive projections algorithm (SPA) and the competitive adaptive reweighted sampling (CARS) algorithm to reduce spectral redundancy and enhance model detection performance, retaining eight and ten feature wavelengths, respectively. Subsequently, a random forest (RF) model was developed for soybean component classification. The model parameters were optimized using particle swarm optimization (PSO) and differential evolution (DE) algorithms to improve performance. Experimental results showed that the RF classification model based on SPA-BC preprocessed spectra and DE-tuned parameters achieved an optimal prediction accuracy of 1.0000 during training. This study demonstrates the feasibility of using hyperspectral imaging technology for the rapid and accurate detection of soybean components, providing technical support for the assessment of breakage and impurity levels during soybean harvesting and storage processes. It also offers a reference for the development of future machine-harvested soybean breakage and impurity detection systems.

Aiming at the demand by unmanned farms for unmanned operation in the entire process of field management, an unmanned plant protection robot for field management was developed based on a platform comprising a traditional high-clearance spray rod sprayer, integrated unmanned driving technology, image recognition technology, intelligent control technology, and precision operation technology. According to the agricultural machinery operation mode, agricultural machinery path planning, linear path tracking, and header path tracking algorithms were developed. Based on the overall structure and working principle of the chassis, the robot control system, steering control system, and operation control system were set. Based on the YOLOv5 image recognition algorithm, the crop–weed recognition model was developed. After 6000 rounds of training, the accuracy, recall, and mean average precision of the model were 87.7%, 84.5%, and 79.3%, respectively. Finally, a field experiment was carried out with the unmanned plant protection robot equipped with a complete system. Results show that the average lateral error of the robot is 0.036 m, the maximum lateral error is 0.2 m, the average root mean square error is 0.053 m, the average velocity error is 0.034 m/s, and the average root mean square error of velocity is 0.045 m/s when the robot works in a straight line. In weeding operations, the area ratio of weedy zones to field is 25%, which saves 75% of the herbicide compared to that dispensed in full spraying mode. The unmanned plant protection robot designed in this study effectively achieves machinery’s autonomous operation, providing valuable insights for research in unmanned farming and autonomous agricultural machinery.

In the context of automatic driving, the analysis of the steering motion characteristics is critical for enhancing the efficiency of crawler harvesters. To address issues such as the low transmission efficiency and the large steering radius encountered by traditional crawler harvesters featuring hydrostatic drives, a driven steering crawler harvester chassis was designed. This involved analysis of the chassis transmission system structure and its steering characteristics under several conditions, including differential steering, differential direction reversal, and unilateral braking steering. The steering parameters were determined based on real-time kinematic positioning–global navigation satellite system (RTK-GNSS) measurements, and they were compared with theoretical predictions based on the crawler harvester steering kinematics. The slip rates and modified models of the crawler chassis for various steering modes were then obtained. The results indicated that the increase in the ratio between the running input and steering input speeds led to larger track steering radii and smaller average rotational angular velocities. Remarkably, the slopes of the linear fits of the tracked chassis steering parameters varied significantly under differential direction reversal and differential steering modes. Compared with the actual results, the correlation coefficient of the tracked chassis steering parameters fitting model is close to 1. The steering parameter model was deemed suitable for actual operational requirements. The results provide a valuable reference for designing navigation and steering models of crawler harvesters operating on different road surfaces.

Development of the electric automatic steering system for agricultural vehicles