No abstract

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.

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.