Sorghum is an important grain crop in many countries worldwide, yet it often suffers from high levels of fragmentation during harvest due to varying maturity. To this end, a study was conducted to investigate the crushing characteristics of sorghum grains subjected to compression and impact loading at different moisture contents. By configuring sorghum kernels with varying ranges of water and determining their physical parameters, such as length, width, etc., the geometric mean diameter of sorghum kernels was 3.105–3.550 mm, and the sphericity was above 75%. Compression tests were conducted on sorghum kernels in the triaxial direction. The compression energy was calculated to be 13.409–19.229 J on the X-axis, 16.313–21.409 J on the Y-axis, and 17.609–24.741 J on the Z-axis. In contrast, the apparent contact modulus of elasticity was calculated, with the maximum modulus of elasticity up to 72 MPa in the Z-axis direction, and the variations in the X-axis and Y-axis were approximate. Finally, mechanical impact tests were conducted to measure the critical angle of seed breakage, and a mathematical model was established to predict the impact of mechanical breakage force. The error between the predicted and experimental values was within 3%. This paper conducted compression and impact mechanics tests on sorghum seeds at different moisture contents to provide a design basis for sorghum harvesting and processing and other harvesting equipment.

In the foxtail millet field, due to the dense distribution of the foxtail millet ears, morphological differences among foxtail millet ears, severe shading of stems and leaves, and complex background, it is difficult to identify the foxtail millet ears. To solve these practical problems, this study proposes a lightweight foxtail millet ear detection method based on improved YOLOv5. The improved model proposes to use the GhostNet module to optimize the model structure of the original YOLOv5, which can reduce the model parameters and the amount of calculation. This study adopts an approach that incorporates the Coordinate Attention (CA) mechanism into the model structure and adjusts the loss function to the Efficient Intersection over Union (EIOU) loss function. Experimental results show that these methods can effectively improve the detection effect of occlusion and small-sized foxtail millet ears. The recall, precision, F₁ score, and mean Average Precision (mAP) of the improved model were 97.70%, 93.80%, 95.81%, and 96.60%, respectively, the average detection time per image was 0.0181 s, and the model size was 8.12 MB. Comparing the improved model in this study with three lightweight object detection algorithms: YOLOv3_tiny, YOLOv5-Mobilenetv3small, and YOLOv5-Shufflenetv2, the improved model in this study shows better detection performance. It provides technical support to achieve rapid and accurate identification of multiple foxtail millet ear targets in complex environments in the field, which is important for improving foxtail millet ear yield and thus achieving intelligent detection of foxtail millet.

As the millet ears are dense, small in size, and serious occlusion in the complex grain field scene, the target detection model suitable for this environment requires high computing power, and it is difficult to deploy the real-time detection of millet ears on mobile devices. A lightweight real-time detection method for millet ears is based on YOLOv5. First, the YOLOv5s model is improved by replacing the YOLOv5s backbone feature extraction network with the MobilenetV3 lightweight model to reduce model size. Then, using the multi-feature fusion detection structure, the micro-scale detection layer is augmented to reduce high-level feature maps and low-level feature maps. The Merge-NMS technique is used in post-processing for target information loss to reduce the influence of boundary blur on the detection effect and increase the detection accuracy of small and obstructed targets. Finally, the models reconstructed by different improved methods are trained and tested on the self-built millet ear data set. The AP value of the improved model in this study reaches 97.78%, F₁-score is 94.20%, and the model size is only 7.56 MB, which is 53.28% of the standard YoloV5s model size, and has a better detection speed. Compared with other classical target detection models, it shows strong robustness and generalization ability. The lightweight model performs better in the detection of pictures and videos in the Jetson Nano. The results show that the improved lightweight YOLOv5 millet detection model in this study can overcome the influence of complex environments, and significantly improve the detection effect of millet under dense distribution and occlusion conditions. The millet detection model is deployed on the Jetson Nano, and the millet detection system is implemented based on the PyQt5 framework. The detection accuracy and detection speed of the millet detection system can meet the actual needs of intelligent agricultural machinery equipment and has a good application prospect.