计算机视觉技术在水稻氮素营养诊断中应用的研究进展

Application of Computer Vision Technology in Nitrogen Nutrition Diagnosis of Rice: Research Progress

为探究水稻冠层外观特征与水稻氮素营养状况的关系,综述了应用计算机视觉技术进行水稻氮素营养诊断的基本思路及研究进展。主要分述了常规性水稻氮素营养指标的获取,水稻冠层图像的获取、预处理以及特征的提取与优化,水稻氮素营养诊断模型建立的方法等方面的内容。指出近地面水稻冠层图像的获取方法、水稻图像处理方法、多种综合性特征的优化选择方式都有待进一步研究,应用机器学习进行水稻氮素营养诊断建模的方法需要更加深入探究。今后应加大计算机视觉技术在水稻氮素营养诊断相关研究中的应用,将多种图像处理方法、特征优化选择方法与机器学习建模方法相结合,并探寻更为简便易行的方法进行水稻氮素营养诊断。

To explore the relationship between the appearance characteristics of rice canopy and the nitrogen nutrition status of rice, the basic ideas and research progress of the application of computer vision technology in rice nitrogen nutrition diagnosis were summarized. This paper mainly discusses the acquisition of conventional rice nitrogen nutrition index, the acquisition and preprocessing of rice canopy image, the extraction and optimization of features, and the establishment of rice nitrogen nutrition diagnosis model. It is pointed out that the methods of rice canopy image acquisition, rice image processing and optimization of multiple comprehensive characteristics need to be further studied, and the methods of using machine learning to diagnose and model rice nitrogen nutrition need to be further explored. In the future, it is necessary to increase the application of computer vision technology in the research of rice nitrogen nutrition diagnosis, and combine multiple image processing methods, characteristics optimization selection methods and machine learning modeling methods together to explore a more simple and easy method for rice nitrogen nutrition diagnosis.