教授
发布时间：2026-09-02
点击次数：
| 影响因子： | 4.1 |
| DOI码： | 10.3390/agronomy15061396 |
| 所属单位： | 长江大学计算机科学学院 |
| 发表刊物： | Agronomy |
| 刊物所在地： | Basel, Switzerland |
| 关键字： | rice panicle; detection; computer vision; lightweight network; precision agriculture |
| 摘要： | Rice panicle detection is a key technology for improving rice yield and agricultural management levels. Traditional manual counting methods are labor-intensive and inefficient, making them unsuitable for large-scale farmlands. This paper proposes FRPNet, a novel lightweight convolutional neural network optimized for multi-altitude rice panicle detection in UAV images. The architecture integrates three core innovations: a CSP-ScConv backbone with self-calibrating convolutions for efficient multi-scale feature extraction; a Feature Pyramid Shared Convolution (FPSC) module that replaces pooling with multi-branch dilated convolutions to preserve fine-grained spatial information; and a Dynamic Bidirectional Feature Pyramid Network (DynamicBiFPN) employing input-adaptive kernels to optimize cross-scale feature fusion. The model was trained and evaluated on the open-access Dense Rice Panicle Detection (DRPD) dataset, which comprises UAV images captured at 7 m, 12 m, and 20 m altitudes. Experimental results demonstrate that our method significantly outperforms existing advanced models, achieving an AP50 of 0.8931 and an F2 score of 0.8377 on the test set. While ensuring model accuracy, the parameters of the proposed model decreased by 42.87% and the GFLOPs by 48.95% compared to Panicle-AI. Grad-CAM visualizations reveal that FRPNet exhibits superior background noise suppression in 20 m altitude images compared to mainstream models. This work establishes an accuracy-efficiency balanced solution for UAV-based field phenotyping. |
| 合写作者： | Zhiliang Zhang, Yu Zhang, Hongshen Guo |
| 第一作者： | Yuheng Guo |
| 论文类型： | 期刊论文 |
| 通讯作者： | Wei Zhan |
| 论文编号： | 20260830-003 |
| 文献类型： | 期刊 |
| 卷号： | 15 |
| 期号： | 6 |
| 页面范围： | 1396 |
| 字数： | 8118 |
| ISSN号： | 2073-4395 |
| 发表时间： | 2025-06-05 |
