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

• URL: http://arxiv.org/abs/2507.20506v1
• Date: Mon, 28 Jul 2025 04:01:29 GMT
• Title: An Improved YOLOv8 Approach for Small Target Detection of Rice Spikelet Flowering in Field Environments
• Authors: Beizhang Chen, Jinming Liang, Zheng Xiong, Ming Pan, Xiangbao Meng, Qingshan Lin, Qun Ma, Yingping Zhao,
• Abstract summary: This study proposes a rice spikelet flowering recognition method based on an improved YOLOv8 object detection model.<n>BiFPN replaces the original PANet structure to enhance feature fusion and improve multi-scale feature utilization.<n>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.
• Score: 1.0288898584996287
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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