Corn is an important economic and food crop, and the corn threshing process is an important link in the processing of corn, but the damage rate in the threshing process has always been a problem, causing difficulties in subsequent processing and storage. To address the high damage rate in corn ear threshing, a texture analyzer was used to measure the fracture force of Boyun 88 and Zhengdan 958 corn varieties in the triaxial direction, and a CT scanning imaging system was used to analyze the connection mode between the carpopodium and the corn cob. The connection between the carpopodium and corn cob, as well as the fracture process of the carpopodium, was simulated. Finally, high-speed photography was used to study the corn ear threshing process. The results indicated that the fracture force of the carpopodium under radial tension was significantly greater than that under axial and tangential shear. Additionally, the simulated fracture stress value of the carpopodium exceeded its actual fracture stress value. Under radial stress, the fracture force between the carpopodium and corn cob exhibited more uniformity on the contact surface. When a tangential load was applied, it was observed that the force chain shifted and dissipated along the axis during corn kernel extrusion. High-speed photography on a discrete test bench revealed that corn kernel dispersion, extrusion, and force transfer facilitated the movement and migration of surrounding kernels, with the force transfer process resembling a “trapezoid”. This study offers theoretical guidance for corn threshing with low damage and an analysis of the threshing process.

Maize kernel damage detection is critical for grain quality assessment and post-harvest processing. However, existing deep learning methods struggle to balance accuracy, model complexity, and multi-class recognition under real-world conditions. To address these issues, this paper proposes YOLOv11n_DEG, an improved lightweight detection model based on YOLOv11n. The model uses the first ten pretrained layers as a feature extractor, replaces standard convolutions in the backbone with depthwise separable convolutions to reduce parameters, integrates an Efficient Channel Attention (ECA) module to enhance feature representation, and employs a dual-dropout strategy in the classification head to mitigate overfitting and improve generalization. Additionally, to account for potential discrepancies in damage characteristics between the obverse and reverse sides of maize kernels, a dual-sided synchronous image feature fusion method is introduced. The output layer classifies five target categories for multi-class damage detection. On an independent test set, the proposed model achieves a precision of 92.5%, a recall of 89.6%, and a mean average precision (mAP) of 93.4%, outperforming the original YOLOv11n by 6.2%, 4.0%, and 3.7%, respectively, while reducing parameter count and computational complexity by 32.9% and 6.5%. To validate practical deployability, a custom testbed with dual-camera synchronous acquisition and geometry-based matching was developed. On this platform, the model with dual-sided fusion achieves a single-side recognition accuracy of 94.1% and a dual-sided recognition accuracy of 89.3%, with an average detection time of 0.8 s per batch. These results demonstrate that YOLOv11n_DEG provides an accurate and practical solution for intelligent maize kernel damage detection, with strong potential for real-world deployment in grain inspection systems.

Characteristics and numerical simulation of maize ear threshing under mixed airflow