Improved lightweight YOLOv5n-based network for bruise detection and length classification of asparagus

Abstract

Efficient and accurate online quality recognition is crucial for asparagus production. To address the slow manual detection speed and low recognition efficiency in asparagus grading, as well as the limitations of traditional single-label classification algorithms in identifying bruise locations and classifying lengths, this study proposes an asparagus length classification and bruise detection method using the You-Only-Look-Once (YOLOv5n) convolutional neural network. Two lightweight, improved models—YOLOv5n with a spatial grouping strategy (YOLOv5n-SGS) and YOLOv5n with a global enhancement strategy (YOLOv5n-GES)—were connected in series to perform length classification and bruise detection of asparagus. The YOLOv5n-SGS model incorporated the ShuffleNet backbone, ghost spatial convolution (GSConv), and VoVNet ghost spatial cross-stage partial (VoV-GSCSP) modules, reducing computational complexity while improving efficiency in length classification. The YOLOv5n-GES model integrated the GhostNet backbone, GhostConv, and C3Ghost modules to enhance detection speed. A simple parameter-free attention module (SimAM) was added to improve semantic feature extraction, while an efficient intersection over union (EIoU) loss function was employed to enhance convergence and recognition accuracy. Test results demonstrated that the YOLOv5n-SGS model achieved a mean average precision (mAP) of 96.5 %, reducing computational complexity to 10 % of that of YOLOv5n. The YOLOv5n-GES model attained an mAP of 97.3 %, with complexity reduced to 59 %. The combined performance of both models outperformed similar approaches. By connecting both models with a cropping layer, overall classification and detection accuracy exceeded 95 % with total number of parameters only 1.221 M. The proposed method significantly improves high-precision length classification and bruise detection in asparagus, advancing industrial automation and enhancing the economic value of asparagus production.