The hilly farmland in China is characterized by small farmland areas and dense farmland distribution, and the working environment is three-dimensional topographic farmland, so the working conditions in the field are relatively complex. In this working environment, the coverage path planning technique of a farmland autonomous task is harder than that of 2D farmland autonomous task. Generally, the path planning problem of 2D farmland is to construct the path cost model to realize the planning of agricultural machinery driving route, while for the path planning problem of three-dimensional terrain farmland in the hilly region, this paper proposes a covering path planning scheme that meets the requirements of autonomous work. Based on the energy consumption model, the scheme searches the optimal driving angle of agricultural machinery, prioritizes solutions to the problem of covering path planning within the scattered fields in the working area, and then searches through the genetic algorithm for the optimal order of traversing the paths of each field to complete the coverage path planning in the working area. On the one hand, the scheme optimizes the planning route in the fields from the angle of optimal energy consumption; on the other hand, through the genetic algorithm, the fields are connected in an orderly manner, which solves the comprehensive problems brought by the unique agricultural environment and farming system in China’s hilly areas to the agricultural machinery operation. The algorithm program is developed according to the research content, and a series of simulation experiments are carried out based on the program using actual farmland data and agricultural machinery parameters. The results show that the planned path obtained at the cost of energy consumption has a total energy consumption of 4771897.17J, which is 17.4% less energy consumption than the optimal path found by the path cost search; the optimization effect is evident.

Here we present a high-precision method for predicting threshing drum clogging in drum harvesters, with strong applicability for longitudinal axial flow in crawlers. This method can be used to determine the alarm threshold. It entails placing a wireless vibration sensor at the outer end of the rotary shaft of the harvester to collect axial vibration signals from the drum to detect early fault characteristics. The method employs the Hilbert-Huang transform-analysis method to obtain time-frequency spectrum characteristics of the blocking process, thus diagnosing the speed reduction necessary to shut down when the threshing drum is clogged. The traditional method of extracting fault characteristic frequency by Fourier transform is compared with the proposed method. This method can not only diagnose the critical value of the rotating speed when the drum is clogged but also accurately and clearly reflect the trend of the drum clogging fault. The fault is within 3 standard deviations above or below the mean characteristic frequency, and abnormality is within 1 standard deviation above or below the mean characteristic frequency. Based on our experimental analysis, we introduce the concept of the confidence interval, andwe consider crop type and the diversity of working conditions. The proposed method improves the adaptability of forecasting to specific environments and combines agricultural machinery with agronomy. The trend of blocking can be predicted at least 1 s in the early stage of blockage by using a sliding window to process the vibration data collected in real time. This predictive ability makes the method superior to speed-sensor analysis in precision