Traditional detection of lodging in rice (Oryza sativa L.) and wheat (Triticum aestivum L.) mostly uses statistical methods or routine ground surveys. These methods have a low degree of automation and poor accuracy. In order to improve the detection of rice and wheat lodging, this study combined artificial intelligence technology and a template matching algorithm to construct a rice and wheat lodging detection system. Moreover, this study used perceptual hash technology for image matching processing, built an intelligent recognition model on the basis of improved algorithms, and combined this with camera technology to build the system's functional framework. The constructed system could perform image matching for rice and wheat, and detected rice and wheat lodging on the basis of image matching. Finally, this study analyzed the system's function structure based on the actual situation of rice and wheat lodging, and designed experiments to verify the effect of the system. The experimental research results showed that the system constructed in this study can play an effective role in the detection of rice and wheat lodging.

中文翻译：

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基于人工智能技术和模板匹配算法的稻麦倒伏检测理论研究

水稻（Oryza sativa L.）和小麦（Triticum aestivum）的传统倒伏检测L.) 主要使用统计方法或常规地面调查。这些方法自动化程度低，准确性差。为了提高水稻、小麦倒伏的检测能力，本研究结合人工智能技术和模板匹配算法，构建了水稻、小麦倒伏检测系统。此外，本研究利用感知哈希技术进行图像匹配处理，在改进算法的基础上构建智能识别模型，并结合摄像头技术构建系统的功能框架。构建的系统可以对水稻和小麦进行图像匹配，并在图像匹配的基础上检测水稻和小麦的倒伏。最后，本研究结合稻麦倒伏的实际情况分析了该系统的功能结构，并设计了实验来验证系统的效果。实验研究结果表明，本研究构建的系统能够在水稻、小麦倒伏检测中发挥有效作用。