Agricultural machinery has been closely related to national food security during farming, sowing, and harvesting. The field harvested acreage of agricultural machinery can be required to accurately calculate the subsidy distribution, fee collection, efficiency, and income evaluation. It is also an urgent need for a precise and effective harvested acreage. This study aims to calculate the harvested acreage of wheat harvesters using spatiotemporal trajectories and grid key points. The calculation was also considered on the positional relationship between boundary grid key points and trajectory vector rectangles. The mathematical expectation of various occupancy scenarios was then proposed to effectively remove the overlapping areas for high accuracy. A grid key point algorithm was introduced to fully analyze the boundary grids, in response to the failure of the grid buffer zone. Probable situations were then determined at the boundary grids, according to the positional relationship between grid vertices and center points with vector rectangles. Mathematical expectation values were computed for each scenario during area calculation. The trajectory data of combined harvesters was obtained in the period of wheat harvest from the National Agricultural Machinery Big Data Platform. Among them, the parameters also included the latitude and longitude of the field, time, vehicle ID, speed, and direction. The trajectory data was selected as the meter-level positioning and the sampling frequency of 0.2 Hz. Data cleaning and segmentation were performed on the fields and roads. The test dataset of trajectory was captured from 50 farmland plots in the major wheat-producing regions (Hebei, Henan, and Shandong) for the years 2022 and 2023. A sensitivity analysis was conducted on the length of the bottom grid edge. Ultimately, the length of the bottom grid edge was determined to be one meter for both the grid buffer zone and the grid key point. A better balance was also obtained to consider the accuracy of area calculation and the speed of the algorithm. Three approaches (distance swath, grid buffer zone, and grid key point) were utilized to evaluate the operational width of the selected combine harvesters and the calculated area of the 50 farmland plots. The average error of calculation was then compared with the true values. Results showed that the grid key point shared an experimental average error of 2.66%, which was 69.45 and 1.25 percentage points lower than the distance swath and the grid buffer zone, respectively. The grid key point also solved the overlapping operation areas in farmlands. The higher accuracy of calculation was then achieved, compared with similar algorithms. Additionally, the versatility of the improved model was achieved by only requiringthe trajectory data and working width during rice harvesting, corn harvesting, or wheat sowing. In conclusion, the accuracy of calculation was effectively improved for the harvested acreage. The finding can also provide a strong reference for the subsidies, cost control, and budget planning in the assessment of agricultural production.

China plants approximately 20.3 million hm² of winter wheat annually. During the recent one-month harvesting period, hundreds of thousands of combine harvesters participated in wheat harvesting from south to north. However, the total number of active harvesters remains a challenge, restricting government policy-making and industry analysis. This study proposed a nonparametric bootstrap estimation model based on big data to dynamically infer the total number of active agricultural machines by analyzing the spatio-temporal trajectories of harvesters. Through Monte Carlo simulation experiments, the performance of four nonparametric bootstrap methods was systematically evaluated from dimensions such as bias, mean squared error, and coverage probability. The results show that the bias-corrected and accelerated bootstrap method (BCa) performs best and was selected as the 95% confidence interval estimation method. The 95% confidence intervals for the total number of active harvesters in 2021, 2022, and 2023 are [447 223, 456 387], [441 708, 447 625], and [436 873, 440 608], respectively, providing a quantitative basis for regulatory supervision and capacity planning in the agricultural machinery industry.