A miniaturised bionic electronic nose system was developed to solve the problems of expensive equipment and long response time for soil pesticide residue detection. The structure of the bionic electronic nasal cavity is designed based on the spatial structure and olfactory principle of the sturgeon nasal cavity. Through experimental study, the structure of the nasal cavity of the sturgeon was extracted and analyzed. The 3D model of the bionic electronic nasal cavity was constructed and verified by Computational Fluid Dynamics (CFD) simulation. The results show that the gas flow distribution in the bionic chamber is more uniform than that in the ordinary chamber. The airflow velocity near the sensor in the bionic chamber is lower than in the ordinary chamber. The eddy current intensity near the bionic chamber sensor is 2.29 times that of the ordinary chamber, further increasing the contact intensity between odor molecules and the sensor surface and shortening the response time. The 10-fold cross-validation method of K-Nearest Neighbor (K-NN), Random Forest (RF) and Support Vector Machine (SVM) was used to compare the recognition performance of the bionic electronic nasal cavity with that of the ordinary electronic nasal cavity. The results showed that, when the bionic electronic nose detection system identified the concentration of pesticide residues in soil, the recognition rate of the above three recognition algorithms reached 97.3%, significantly higher than that of the comparison chamber. The bionic chamber electronic nose system can improve the detection performance of electronic noses and has a good application prospect in soil pesticide residue detection.

The peanut, a globally important oil and economic crop, has thin, brittle pods that are prone to breakage under external forces during mechanical harvesting, transportation, and processing. To minimize this loss and reduce production costs, we conducted an in-depth study of the pod-breaking process by integrating manual and automatic filling approaches within the discrete element method (DEM) with the Hertz–Mindlin with bonding model. A breakable layered bonding model for peanut pods was developed, which is capable of precisely characterizing the disparities in the mechanical properties of peanut pod shells and kernels. Physical tests were performed to obtain the relevant contact parameters of peanut pods. Compression tests combined with calibration approaches were employed to identify the bonding parameters of peanut pods, which are not easily accessible via direct experimental measurements. The optimal combination of simulation parameters for the model was determined via a Plackett–Burman test, steepest ascent test, and Box–Behnken test. The results indicated that the critical normal stress between pod shells is the most significant influencing factor. The optimal parameter combination for the proposed model is as follows: the normal stiffness per unit area between pod shells is 7.81 × 10¹⁰ N/m³, the shear stiffness per unit area between pod shells is 9.00 × 10⁸ N/m³, the critical normal stress between pod shells is 2.17 × 10⁵ N/m³, and the critical shear stress between pod shells is 2.25 × 10⁵ N/m³. The established layered bonding model for breakable peanut pods was validated using both cylinder-lifting simulation tests and physical shelling experiments. The relative error in the angle of repose between the cylinder-lifting simulation and physical tests was 1.6%, while the deviation in the shelling experiment was only 0.7%. This model provides a theoretical foundation for the design and optimization of machinery used in peanut pod harvesting, transportation, and processing.