一种基于改进型CSRNet模型的水稻穗数计数方法。
A method of rice panicle number counting based on improved CSRNet model.
作者信息
Zhu Junyan, Huang Jiaxing, Wang Yiyuan, Wang Jingyu, Wen Yongxian
机构信息
College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China.
College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China.
出版信息
Front Plant Sci. 2025 Apr 3;16:1494564. doi: 10.3389/fpls.2025.1494564. eCollection 2025.
INTRODUCTION
Rice is one of the world's leading food crops, with nearly half of the world's population eating rice as their staple food. Rice yield is directly related to varieties, and the most intuitive agronomic trait of varietal yield is the number of grains per panicle.
METHODS
In this study, rice panicles are taken as the research object, and images of the panicles are captured using a smartphone. The CSRNet counting model based on deep learning is then improved and applied to the problem of counting the number of grains per panicle in rice.
RESULTS AND DISCUSSION
The results show that the method of this study has a mean error value of 3.83% on the final validation set. On this basis, the development of rice per panicle counting APP based on Android terminal and batch counting software RiceGrainCounter based on PC terminal realizes real-time counting on Android terminal and batch counting on PC terminal, which can provide theoretical basis and technical support for rice per panicle counting.
引言
水稻是世界主要粮食作物之一，世界上近一半人口以大米为主食。水稻产量直接与品种相关，而品种产量最直观的农艺性状是每穗粒数。
方法
本研究以水稻穗为研究对象，使用智能手机拍摄穗的图像。然后对基于深度学习的CSRNet计数模型进行改进，并应用于水稻每穗粒数的计数问题。
结果与讨论
结果表明，本研究方法在最终验证集上的平均误差值为3.83%。在此基础上，开发了基于安卓终端的水稻每穗计数APP和基于PC终端的批量计数软件RiceGrainCounter，实现了安卓终端的实时计数和PC终端的批量计数，可为水稻每穗计数提供理论依据和技术支持。
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