Design and experimental analysis of real-time detection system for The seeding accuracy of rice pneumatic seed metering device based on the improved YOLOv5n

The acquisition of rice seeding accuracy information could provide adequate support for the operational status of the rice pneumatic seed metering device and field management in the later stages. However, this task proved difficult due to the high speed of rice seeding and the occurrence of non-single seed seeding. In order to achieve real-time detection of seeding accuracy during the rice pneumatic seed metering device operation, a real-time detection system for the seeding accuracy of the device was designed. This paper introduced the system’s main components and working principles in detail and proposed a rice seed accuracy detection algorithm based on the improved YOLOv5n.The algorithm utilised the Faster-Net neural network, replacing the CSPDarknet53 network that served as the backbone of the original algorithm. Additionally, it incorporated the CARAFE operator and introduced the Soft-NMS-CIOU technique, a form of soft non-maximum suppression, along with integrating the CBAM attention mechanism module. These enhancements improved the model’s feature extraction capability on rice seed images, enabling real-time detection of small rice seeds in the dark environment within the rice pneumatic seed metering device. This improved accuracy in recognising small rice seed images and reduced the probability of false detections. Through comparative analysis with different algorithms, test results demonstrated that this algorithm exhibited a higher pass rate and faster response time compared to others. A verification test was conducted to evaluate identification accuracy at various seed sucking plate rotational speeds. The detection accuracies were 96 %, 96 %, 98.65 %, 88.8 % and 91 %, respectively, at seed sucking plate rotational speeds of 10, 20, 30, 40, and 50 r/min, with a suction negative pressure of 1.6 kPa. Based on the experimental findings, the algorithm met the requirements for seeding detection and could serve as a foundation for further research into seeding accuracy detection algorithms for rice pneumatic seed metering devices.