| 基于计算机视觉技术的油菜冠层营养信息监测 |
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| 引用本文： | 袁道军,刘安国,原保忠,胡立勇,刘志雄,张方方.基于计算机视觉技术的油菜冠层营养信息监测[J].农业工程学报,2009,25(12):174-179. |
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| 作者姓名： | 袁道军 刘安国 原保忠 胡立勇 刘志雄 张方方 |
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| 作者单位： | 1. 华中农业大学植物科学技术学院,武汉,430070
2. 华中农业大学植物科学技术学院,武汉,430070;浙江林学院环境科技学院,杭州,311300 |
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| 基金项目： | 国家自然科学基金重点项目（30130120）资助 |
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| 摘 要： | 为了探讨利用计算机视觉技术监测油菜长相长势的可行性，在大田自然条件下，用数码相机构建了计算机视觉系统，获取、分割图像，用逐步回归的方法建立了用颜色值监测叶绿素含量、全氮含量、碳氮比值的最优模型，模型具有较好的预测性。试验结果表明：在大田自然光照条件下，用数码相机采集油菜图像，监测冠层叶绿素含量、全氮含量、碳氮比等生理指标是可行的。
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| 关 键 词： | 计算机视觉，图象处理，图像分割，油菜 |
| 收稿时间： | 2007/3/12 0:00:00 |
| 修稿时间： | 2009/8/11 0:00:00 |
Nutrition information extraction of rape canopy based on computer-vision technology |
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| Yuan Daojun,Liu Anguo,Yuan Baozhong,Hu Liyong,Liu Zhixiong and Zhang Fangfang.Nutrition information extraction of rape canopy based on computer-vision technology[J].Transactions of the Chinese Society of Agricultural Engineering,2009,25(12):174-179. |
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| Authors: | Yuan Daojun Liu Anguo Yuan Baozhong Hu Liyong Liu Zhixiong and Zhang Fangfang |
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| Institution: | 1. College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China,1. College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China,1. College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China,1. College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China,1. College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China and 1. College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China; 2. School of Environment Technology, Zhejiang Forestry University, Hangzhou 311300, China |
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| Abstract: | In order to study the possibility of monitoring the rape canopy' nutrition information based on computer-vision technology in the outdoor ray, the computer-vision system was designed, and the images of the rape canopy were got and segmented. Through the statistical analysis of stepwise regression, the perfect models of chlorophyll content, total-N content, and C/N ratio were found and have a good predictive ability. The result showed that it is possible to estimate the physiological indexes of rape canopy, including chlorophyll content, total-N content, and C/N ratio, with digital camera under the conditions of field natural light. |
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| Keywords: | computer vision image processing image segmentation rape |
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