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

2025-07-28

AI Summary

To address the challenge of automatic flowering-stage detection in paddy fields—where rice spikelets are small and flowering duration is brief—this paper proposes an enhanced YOLOv8-based detection method. The core contributions include: (1) replacing PANet with BiFPN to strengthen multi-scale feature fusion, and (2) introducing an additional p2 detection head dedicated to small objects to improve localization accuracy for micro-spikelets (<16×16 pixels). Furthermore, a specialized rice panicle flowering dataset is constructed using high-resolution RGB imagery and targeted data augmentation. Experimental results demonstrate that the proposed model achieves 65.9% mAP@0.5 on the test set and operates at 69 FPS, significantly outperforming baseline methods. This performance meets the practical requirements for real-time, precise flowering monitoring in hybrid rice seed production.

Technology Category

• Computer Vision: Object Detection & Categorization
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Detecting small rice spikelets in complex fields
• Improving accuracy for short flowering periods
• Enhancing feature fusion for small target recognition

Innovation

• BiFPN enhances multi-scale feature fusion
• Added p2 head improves small target detection
• High-resolution RGB dataset supports training