基于openCV的玉米出苗期和三叶期自动检测系统的设计

发布时间：2018-06-19 04:51

本文选题：轮廓 + 骨架　；参考：《电子测量与仪器学报》2017年10期

【摘要】：为了远程实时动态监测玉米长势,为农事活动提供准确的玉米生长状态信息,提出了基于轮廓和骨架提取的玉米出苗期和三叶期的自动识别算法。该算法实现了对玉米图像的分割,并对图像中轮廓和骨架等图像特征进行提取,根据所提取的图像特征判断玉米是否进入出苗期或三叶期。利用该算法与计算机视觉库openCV进行玉米出苗期和三叶期的检测系统的设计,实现了玉米出苗期和三叶期的自动识别。此外,在VS2013环境下实现了对一个简单的玉米出苗期和三叶期的自动检测系统软件的界面开发。该系统对玉米出苗期和三叶期的识别速度较快,识别结果准确,可以作为玉米全部生长期检测系统的开发基础。
[Abstract]:In order to monitor maize growth in real time and provide accurate information of maize growth status for agricultural activities, an automatic recognition algorithm based on contour and skeleton extraction was proposed for maize seedling and trefoil stage. The algorithm can segment corn image and extract image features such as contour and skeleton. According to the extracted image features we can determine whether maize is in emergence stage or trefoil stage. The algorithm and OpenCV were used to design the detection system of maize seedling emergence and trefoil stage, and the automatic recognition of maize emergence and trefoil stage was realized. In addition, the interface of a simple automatic detection system software for maize seedling emergence and trefoil stage is developed in VS2013 environment. The system can be used as the basis for the development of the whole maize growing period detection system because of its fast recognition speed and accurate recognition result for maize seedling emergence and trefoil stage.
【作者单位】：南京信息工程大学电子与信息工程学院;江苏省无线电科学研究所;
【基金】：国家自然科学基金(61671248) 江苏省产学研联合创新资金计划(BY2013007-02) 江苏省高校自然科学研究重大项目(15KJA460008) 江苏省“六大人才高峰”计划江苏省“信息与通信工程”优势学科资助
【分类号】：S513;TP391.41

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