Load identification method for pepper harvesting drum based on dynamic chaotic characteristics of vibration-torque coupling

Abstract

Abstract

To improve the accuracy, robustness, and interpretability of load-state identification for pepper harvesting drums under complex field disturbances, this study proposes a load identification method based on the chaotic dynamics of vibration-torque coupling. Vibration signals reflect the structural dynamic response of the drum, whereas torque signals reflect load variations caused by crop-drum interaction. By modeling their nonlinear coupling, the proposed method captures load-sensitive dynamic evolution that is more discriminative than single-signal or conventional statistical features. This study aims to obtain reliable real-time drum load-state information to support adaptive adjustment of harvesting parameters, improve operational stability, and reduce fruit damage. A six-dimensional nonlinear coupled dynamical system was constructed by integrating Lorenz and Rössler models, explicitly representing drum load evolution through the cross-coupled nonlinear behavior of vibration and torque. A hybrid framework combining genetic algorithms and Gauss-Newton iteration was used for parameter identification, and the maximum Lyapunov exponent was extracted to quantify trajectory divergence under different load conditions, establishing a chaos-based and physically interpretable load characterization scheme. Based on Central Composite Design (CCD) experiments, the optimal operating parameters were determined as a drum rotational speed of 150 r/min and a forward speed of 0.42 m/s. Under these conditions, the picking rate reached 99.05%, the fruit damage rate was 2.35%, and the average load identification accuracy reached 90.47%. The AUC values for no-load, light-load, normal-load, and overload states were 0.992, 0.981, 1.000, and 0.947, respectively. Comparative experiments verified that the proposed method outperformed conventional load identification methods, and its successful implementation on embedded hardware demonstrated its applicability for on-machine real-time load identification. Overall, this method provides a robust and physically interpretable solution for pepper harvesting drum load identification and supports the optimization of pepper harvester operation.