Estimation of combine harvester throughput using multisensor data fusion

• A multi-sensor fusion method for estimating combine harvester throughput is proposed. • Developed a multi-sensor data monitoring and acquisition system. • A robustness analysis method is proposed: moving average-Mahalanobis distance-KNN. • A multi-sensor fusion estimation model based on PCA-WOA-SVR is proposed. • Experiments show that multi-sensor fusion improves throughput estimation accuracy. Throughput is a key indicator of a combine harvester’s operating performance and efficiency. In response to the challenge that throughput estimation models often struggle to achieve high accuracy due to the imperfect architecture of throughput monitoring systems and the insufficient monitoring on operational parameters, a multi-sensor data fusion-based throughput estimation method is proposed. Firstly, a multi-sensor data monitoring and acquisition system for combine harvester was developed to enable online monitoring and the acquisition of multi-sensor parameters from feeding, threshing, travel, and engine units. Secondly, a multi-sensor fusion estimation model based on PCA-WOA-SVR was introduced. Principal component analysis (PCA) first removes redundant and weakly correlated features to reduce dimensionality, then Support Vector Regression (SVR) estimates throughput from the reduced inputs, and Whale Optimization Algorithm (WOA) optimizes the SVR hyperparameters for optimal estimation performance. Finally, field tests were conducted, and the results showed that the system demonstrated high robustness under varying operating conditions. The MAE of PCA-WOA-SVR in the test set was 0.258 kg/s. The R 2 , MSE, RMSE and MAPE were 0.985, 0.099, 0.315, and 5.3 % respectively, showing high estimation accuracy and strong generalization ability. The ablation study results show that the MAE of PCA-WOA-SVR is reduced by 0.367 kg/s, R 2 is increased by 6.7 %, MSE, RMSE and MAPE are reduced by 0.434, 0.415 and 7.4 %, respectively, compared to using SVR alone, demonstrating that WOA and PCA effectively enhance the estimation performance of the SVR model. The estimation results of different unit combination inputs show that as the number of input units increases, the model estimation effect gradually improves, among which the engine unit contributes the most. The MAE of field online monitoring is 0.29 kg/s, the continuous fluctuation range of the online monitoring data is within [−0.02, 0.015], and the single group monitoring time is 24.31 ms, which meets the requirements of online monitoring accuracy, stability and real-time performance. In summary, the throughput estimation method proposed in this study has good robustness, estimation accuracy and generalization ability, providing important technical support for the online monitoring and feedback control of the throughput for combine harvesters.