文献类型： 外文期刊

作者： Tian, Fuyang ¹ ; Zhang, Yinuo ¹ ; Soomro, Shakeel Ahmed ³ ; Wang, Qiang ⁴ ; Zhang, Shuaiyang ⁵ ; Zhang, Ji ¹ ; Yang, Qinglu ¹ ; Yan, Yunpeng ¹ ; Yu, Zhenwei ¹ ; Song, Zhanhua ¹ ;

作者机构： 1.Shandong Agr Univ, Coll Mech & Elect Engn, Tai An 271018, Peoples R China

2.Shandong Prov Engn Lab Agr Equipment Intelligence, Tai An 271018, Peoples R China

3.Sindh Agr Univ, Dept Farm Struct & Postharvest Engn, Tandojam 70060, Pakistan

4.Chinese Acad Agr Mechanizat Sci Co Ltd, Hohhot Branch, Hohhot 010000, Peoples R China

5.China Acad Agr Mechanisat Res Grp Ltd, Beijing 100083, Peoples R China

6.Agr Technol Promot Ctr Jiangxi Prov, Nanchang 330046, Peoples R China

关键词： Alfalfa seed pod; Object detection; Maturity; Semi-supervised; Small object detection

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2025 年 236 卷

页码：

收录情况： SCI

摘要： The current study was conducted to address the challenges of recognizing alfalfa seed pod maturity in complex field environments, and the significant impact of the quantity of labeled samples on the performance of object detection algorithms. A method for identifying the maturity of alfalfa seed pod clusters was proposed using an unmanned aerial vehicle (UAV) and a semi-supervised deep learning model SSOD-MViT (Semi-Supervised Object Detection based on the MViTNet). To enhance the model's capability to extract key feature information, an improved lightweight general vision transformer MobileViT (Mobile Vision Transformer) was firstly employed as the backbone. The deep integration of ScConv (Spatial and Channel Reconstruction Convolution) was additionally employed to reduce redundant information within the channels, thereby decreasing the computational load of the model. Secondly, a small object detection layer was incorporated into the Neck, and the Efficient Multi-Scale Attention Module (EMA) was added to the C2f structure. The SAHI (Slicing Aided Hyper Inference) algorithm was integrated during the inference process, which improves the detection accuracy of small-sized alfalfa seed pod clusters and enhances the model's resistance to interference. Finally, the concept of Consistency Regularization was incorporated into the model to reduce its dependency on sample data. The experimental results revealed that SSOD-MViT achieved a mAP@0.5 of 92.23 %. When compared to the YOLOv8 object detection model, the mAP@0.5 had improved by 12.31 %. When compared to the Faster R-CNN object detection model, the average detection time reduced by 175.81 ms. The proposed model MViTNet (MobileViT Network) had a storage size of 5.3 MB, and an average detection time of 82.34 ms, providing favorable conditions for subsequent deployment on embedded devices. This research effectively improved the detection performance of existing models in detecting alfalfa seed pod maturity in complex field environments. This advancement also aids in determining the optimal harvesting period for alfalfa seeds, thereby providing technical support to enhance productivity and reduce production costs in the alfalfa seed production industry.