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Development of a Lightweight Model for Rice Plant Counting and Localization Using UAV-Captured RGB Imagery

文献类型： 外文期刊

作者： Sun, Haoran 1 ; Tan, Siqiao 1 ; Luo, Zhengliang 2 ; Yin, Yige 1 ; Cao, Congyin 1 ; Zhou, Kun 2 ; Zhu, Lei 1 ;

作者机构： 1.Hunan Agr Univ, Coll Informat & Intelligence, Changsha 410128, Peoples R China

2.YueLuShan Labortory, Changsha 410128, Peoples R China

3.Hunan Rice Res Inst, Changsha 410125, Peoples R China

关键词： rice plant counting; pruning; lightweight architecture; deep learning; UAV

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 15 卷 2 期

页码：

收录情况： SCI

摘要： Accurately obtaining both the number and the location of rice plants plays a critical role in agricultural applications, such as precision fertilization and yield prediction. With the rapid development of deep learning, numerous models for plant counting have been proposed. However, many of these models contain a large number of parameters, making them unsuitable for deployment in agricultural settings with limited computational resources. To address this challenge, we propose a novel pruning method, Cosine Norm Fusion (CNF), and a lightweight feature fusion technique, the Depth Attention Fusion Module (DAFM). Based on these innovations, we modify the existing P2PNet network to create P2P-CNF, a lightweight model for rice plant counting. The process begins with pruning the trained network using CNF, followed by the integration of our lightweight feature fusion module, DAFM. To validate the effectiveness of our method, we conducted experiments using rice datasets, including the RSC-UAV dataset, captured by UAV. The results demonstrate that our method achieves a MAE of 3.12 and an RMSE of 4.12 while utilizing only 33% of the original network parameters. We also evaluated our method on other plant counting datasets, and the results show that our method achieves a high counting accuracy while maintaining a lightweight architecture.

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