YOLOv5-lotus an efficient object detection method for lotus seedpod in a natural environment

Accurate detection of lotus seedpods in a nature environment is essential for agronomic applications for auto-mated harvesting and yield mapping. Traditional detection methods are based on grower's experience, which is inefficient for the large-scale production. To improve the efficiency of harvesting lotus seedpods, this study proposes a YOLOv5-lotus method to effectively detect overripe lotus seedpods. The lotus seedpods image dataset is firstly created. An improved YOLOv5 network model based on coordinate attention (CA) module is then presented, namely YOLOv5-lotus model, where CA module is developed to strengthen the model inter-channel relationships and capture long-range dependencies with precise positional information, thus improving the detection accuracy of the algorithm. In order to reveal the feasibility and robustness of the proposed method, a number of case studies are presented on the detection of overripe lotus seedpods in various scenarios, including different poses, illuminations and degrees of occlusion. Compared with the classical YOLOv5s network, the average precision of YOLOv5-lotus model is increased by 0.7 % and average detection time is reduced by 0.7 ms. Compared to other state-of-the-art networks, our detection model is able to achieve the highest average precision value, faster efficient detection speed and higher F1 score, with the average precision being 98.3 %, the recall rate being 96.3 %, the precision rate being 97.3 %, F1 score being 0.968 and average detection time being 19.4 ms. Through case studies and comparisons, the effectiveness and superiority of the proposed approach are demonstrated. These research results can be applied to the detection of upwardly-growing conical fruit. It creates a prerequisite for the development of automatic harvesting equipment.

Automated summary

In this study, a YOLOv5-lotus method is proposed to accurately detect overripe lotus seedpods. Through the use of an improved YOLOv5 network model and a coordinate attention module, the detection accuracy is significantly improved. Case studies and comparisons demonstrate the effectiveness and superiority of the proposed approach, providing a foundation for the development of automated harvesting equipment.