An Improved YOLOv8 Approach for Small Target Detection of Rice Spikelet Flowering in Field Environments

TL;DR

This paper enhances the YOLOv8 object detection model to better identify small rice spikelets in field environments, improving accuracy and speed for automated rice flowering monitoring.

Contribution

It introduces a BiFPN structure and a small-object detection head to improve small target detection in YOLOv8 for rice spikelet flowering recognition.

Findings

Achieved 65.9% mAP@0.5, 67.6% precision, 61.5% recall, 64.41% F1-score.

Model runs at 69 frames per second, suitable for real-time applications.

Outperforms baseline YOLOv8 with significant accuracy improvements.

Abstract

Accurately detecting rice flowering time is crucial for timely pollination in hybrid rice seed production. This not only enhances pollination efficiency but also ensures higher yields. However, due to the complexity of field environments and the characteristics of rice spikelets, such as their small size and short flowering period, automated and precise recognition remains challenging. To address this, this study proposes a rice spikelet flowering recognition method based on an improved YOLOv8 object detection model. First, a Bidirectional Feature Pyramid Network (BiFPN) replaces the original PANet structure to enhance feature fusion and improve multi-scale feature utilization. Second, to boost small object detection, a p2 small-object detection head is added, using finer feature mapping to reduce feature loss commonly seen in detecting small targets. Given the lack of publicly available datasets for rice spikelet flowering in field conditions, a high-resolution RGB camera and data augmentation techniques are used to construct a dedicated dataset, providing reliable support for model training and testing. Experimental results show that the improved YOLOv8s-p2 model achieves an mAP@0.5 of 65.9%, precision of 67.6%, recall of 61.5%, and F1-score of 64.41%, representing improvements of 3.10%, 8.40%, 10.80%, and 9.79%, respectively, over the baseline YOLOv8. The model also runs at 69 f/s on the test set, meeting practical application requirements. Overall, the improved YOLOv8s-p2 offers high accuracy and speed, providing an effective solution for automated monitoring in hybrid rice seed production.