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.

With the application of new energy technology, hybrid agricultural machinery has been developed. This article designs a hybrid tractor energy management method to solve the problem of high energy consumption caused by significant load fluctuation of the tractor in field operation. This article first analyzes the characteristics of the hydrogen fuel cell, power battery, and ultracapacitor and designs a hybrid energy system for the tractor. Second, the energy management strategy (EMS) of multi-layer decoupling control based on the Haar wavelet and logic rule is designed to realize the multi-layer decoupling of high-frequency, low-frequency, and steady-state signals of load demand power. Then, the EMS redistributes the decoupled power signals to each energy source. Finally, a hardware-in-loop simulation experiment was carried out through the model. The results show that, compared with single-layer control strategies such as fuzzy control and power-following control, the multi-layer control strategy can allocate the demand power more reasonably, and the efficiency of the hydrogen fuel cell is the highest. The average efficiency of the hydrogen fuel cell was increased by 2.87% and 1.2%, respectively. Furthermore, the equivalent hydrogen consumption of the tractor was reduced by 17.06% and 5.41%, respectively, within the experimental cycle. It is shown that the multi-layer control strategy considering power fluctuation can improve the vehicle economy based on meeting the power demanded by the whole vehicle load.

In this study, an energy-saving control strategy based on instantaneous optimization is proposed to improve the energy efficiency of hybrid tractors. Using a parallel diesel–electric hybrid tractor as the research object, the topological and working characteristics were analyzed, and a coupled dynamic model of rotary tillage and tractor plow was constructed. Aiming to minimize the equivalent fuel consumption of the entire machine, the motor and diesel engine torques were taken as the control variables, and the state of charge of the power battery was taken as the state variable. Subsequently, an energy-saving control strategy based on instantaneous optimization is proposed. Finally, a simulation experiment was carried out using MATLAB to verify the effectiveness of the energy-saving control strategy based on instantaneous optimization. Compared with the energy-saving control strategy based on power-following, the results show that energy-saving control strategy based on instantaneous optimization can reasonably control the operating state of the diesel engine and motor. Therefore, the diesel engine and motor work in the high-efficiency area, and effectively reduce the equivalent fuel consumption of the tractor during field operation. Under rotary tillage and plowing conditions, equivalent fuel consumption is reduced by 4.70% and 6.31%, respectively.

In light of the challenges posed by the complex structural characteristics and significant coupling of vibration signals in rotating machinery, this study proposes an adaptive noise reduction method called Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). Additionally, an enhanced threshold screening Singular Value Decomposition (SVD) algorithm is introduced to address the issues pertaining to noise identification and feature extraction in the context of vibration signals from rotating machinery, which are subjected to complex noise interference. The effectiveness of the proposed approach is substantiated through the evaluation of key metrics, such as the signal-to-noise ratio (SNR), as well as the utilization of advanced signal analysis techniques, including Short Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT). The experimental results validate the finding that the combination of the improved CEEMDAN and the enhanced threshold screening SVD algorithm effectively reduces noise interference in vibration signals from rotating machinery. This integrated denoising approach successfully preserves the informative characteristics of the vibration signals, thereby laying a foundation for the subsequent fault diagnosis of rotating machinery.

In response to the issues of hybrid tractors’ energy management strategies, such as reliance on experience, difficulty in achieving optimal control, and incomplete analysis of typical operating conditions of tractors, an energy management strategy based on dynamic programming is proposed in combination with various typical operating conditions of tractors. This is aimed at providing a reference for the modeling and energy management strategies of series hybrid tractors. Taking the series hybrid tractor as the research object, the tractor dynamics models under three typical working conditions of plowing, rotary tillage, and transportation were established. With the minimum total fuel consumption of the tractor as the optimization target, the engine power as the control variable, and the state of charge of the power battery as the state variable, an energy management strategy based on a dynamic programming algorithm was established and simulation experiments were conducted. The simulation results show that, compared with the power-following energy management strategy, the energy management strategy based on the dynamic programming algorithm can reasonably control the operating state of the engine. Under the three typical working conditions of plowing, rotary tillage, and transportation, the battery SOC consumption increased by approximately 8.37%, 7.24%, and 0.77%, respectively, while the total fuel consumption decreased by approximately 25.28%, 21.54%, and 13.24%, respectively.

In order to improve the electrical conversion efficiency of an electric tractor motor, a load torque based control strategy (LTCS) is designed in this paper by using a particle swarm optimization algorithm (PSO). By mathematically modeling electric-mechanical performance and theoretical energy waste of the electric motor, as well as the transmission characteristics of the drivetrain, the objective function, control relationship, and analytical platform are established. Torque and rotation speed of the motor’s output shaft are defined as manipulated variables. LTCS searches the working points corresponding to the best energy conversion efficiency via PSO to control the running status of the electric motor and uses logic and fuzzy rules to fit the search initialization for load torque fluctuation. After using different plowing forces to imitate all the common tillage forces, the simulation of traction experiment is conducted, which proves that LTCS can make the tractor use electrical power efficiently and maintain agricultural applicability on farmland conditions. It provides a novel method of fabricating a more efficient electric motor used in the traction of an off-road vehicle.