Timely county-level estimation of wheat harvesting area was essential for cross-regional machinery dispatching, operational resource coordination, and emergency response during the concentrated harvest season. However, conventional monitoring workflows based on post-processing of agricultural machinery trajectories usually produced statistical results by noon of the following day, which limited their value for same-day decision-making. To address this time-lag problem, a rapid prediction method for county-level wheat harvesting area was developed by integrating heatmaps derived from operation trajectories with meteorological information. The study used operation trajectory data from the national agricultural machinery trajectory big data platform, county-level daily harvested area generated by standardized trajectory post-processing, and meteorological data derived from the European Centre for Medium-Range Weather Forecasts ERA5-Land dataset. For each county and day, operation trajectory points were converted into heatmaps through kernel density estimation. A Residual Network-152 model was then used as a fixed feature extractor after removal of the final fully connected classification layer, and deep spatial features were obtained from the heatmaps. These heatmap features were fused with cumulative precipitation, average air temperature, and average humidity aggregated up to 11:00 a.m. A random forest regression model was subsequently established to estimate the final cumulative harvested area of the same day from morning information. The model was trained and evaluated using data from 40 counties distributed across 8 major winter-wheat-producing provinces in China, namely Shanxi, Shaanxi, Henan, Hebei, Jiangsu, Anhui, Hubei, and Shandong. The observation window was uniformly defined from May 25 to June 25 in 2021–2023. Samples from 2021 and 2022 were used for model training, while samples from 2023 were used for testing. The results showed that the proposed method achieved stable prediction performance across all 40 counties. The coefficient of determination exceeded 0.6 in every county, with an average of 0.71 and a maximum of 0.91. The normalized root mean square error ranged from 0.06 to 0.21, with an average of 0.14. Better performance was observed in large and contiguous plain areas, where operation hotspots in the heatmaps were more coherent and easier to characterize. For example, the model performed particularly well in Quanjiao County, Feixi County, Wenshang County, Wudi County, Hua County, and Qing County. In contrast, prediction accuracy was relatively lower, though still acceptable, in fragmented hilly and mountainous counties such as Hengshan District, Lantian County, Yingshan County, Tongshan County, and Jianshi County. This decrease in accuracy was associated with more dispersed field patterns, weaker aggregation structures in the heatmaps, and possible heterogeneity in terminal coverage. The ablation analysis further clarified the contribution of each input source. When only heatmap features were used, the average coefficient of determination reached 0.62 and the average normalized root mean square error was 0.17, indicating that the heatmaps already captured most of the effective information related to harvesting intensity and spatial concentration. When only meteorological inputs were used, the average coefficient of determination dropped sharply to 0.07 and the average normalized root mean square error increased to 0.29, suggesting that meteorological conditions alone were insufficient to describe daily harvesting dynamics. When heatmap features and meteorological factors were combined, the average coefficient of determination increased to 0.71 and the average normalized root mean square error decreased to 0.14, confirming that meteorological information effectively complemented the operation-state information represented by the heatmaps. In addition, the sensitivity analysis on the Gaussian smoothing scale showed that the best overall performance was obtained when the smoothing parameter was set to 7 pixels, which balanced local noise suppression and preservation of spatial differences. Overall, the proposed method shifted county-level wheat harvesting area estimation from next-day statistics to same-day midday prediction while maintaining good agreement with trajectory-derived reference values. The result output time was advanced by about 20 hours, which demonstrated clear practical value for same-day machinery scheduling and rapid operational response during the wheat harvest season. At the same time, the results indicated that future improvements should further consider terminal coverage heterogeneity and complex terrain conditions to enhance model robustness in fragmented production regions.

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.