|
|||||
|
|
| 基于光谱特性分析的冬油菜苗期田间杂草识别 | |
| 引用本文： | 白敬,徐友,魏新华,张进敏,沈宝国.基于光谱特性分析的冬油菜苗期田间杂草识别[J].农业工程学报,2013,29(20):128-134. |
| 作者姓名： | 白敬 徐友 魏新华 张进敏 沈宝国 |
| 作者单位： | 1. 江苏大学现代农业装备与技术教育部重点实验室,镇江,212013 2. 南京农业大学工学院,南京,210031 3. 江苏省联合职业技术学院镇江分院,镇江,212016 |
| 基金项目： | 中国博士后科学基金资助项目（20110491360）；江苏高校优势学科建设工程资助项目（苏财教（2011）8号）；江苏大学高级专业人才科研启动基金资助项目（10JDG026） |
| 摘 要： | 杂草识别是自动除草的关键环节，运用光谱分析技术可以快速识别杂草。该文以冬油菜苗、冬油菜苗期杂草和土壤为研究对象，通过ASD便携式光谱分析仪采集光谱数据。对每个样本连续采集5组数据，经平均、一阶导数、压缩等预处理后，得到368组波长在400～2300 nm范围内的光谱数据。采用逐步判别分析法，按统计量Wilks’ Lambda最小值原则选择变量，选取了710、755、950和595 nm共4个特征波长。运用4个特征波长分别建立了典型判别函数模型和贝叶斯判别函数模型。用这2组模型分别对预测集进行预测，典型判别函数模型的正确识别率为97.78%，在不同的先验概率下贝叶斯判别函数模型的正确识别率分别为98.89%和97.78%。结果表明：当先验概率根据类别大小计算时，以特征波长建立的贝叶斯判别函数模型能较好的识别冬油菜苗期田间杂草，而且模型稳定。该研究结果可为杂草探测光谱传感器的开发提供参考。 |
| 关 键 词： | 图像识别 光谱分析 判别分析 冬油菜苗 杂草识别 贝叶斯判别 |
| 收稿时间： | 4/3/2013 12:00:00 AM |
| 修稿时间： | 2013/9/17 0:00:00 |
Weed identification from winter rape at seedling stage based on spectrum characteristics analysis |
|
| Bai Jing,Xu You,Wei Xinhu,Zhang Jinmin and Shen Baoguo.Weed identification from winter rape at seedling stage based on spectrum characteristics analysis[J].Transactions of the Chinese Society of Agricultural Engineering,2013,29(20):128-134. | |
| Authors: | Bai Jing Xu You Wei Xinhu Zhang Jinmin and Shen Baoguo |
| Institution: | 1. Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, Jiangsu University, Zhenjiang 212013, China;2. College of Engineering, Nanjing Agricultural University, Nanjing 210031, China;1. Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, Jiangsu University, Zhenjiang 212013, China;1. Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, Jiangsu University, Zhenjiang 212013, China;3. Zhenjiang College of Jiangsu Union Technical Institute, Zhenjiang 212016, China |
| Abstract: | Abstract: Weeds are distributed only in patches in fields, but herbicides are applied over entire fields, thus leading to over application and unnecessary pollution. To reduce herbicide application, automatic weed recognition is being developed to treat only weed patches. Weed identification is the key point of automatic weeding, and many research studies have pointed out that the reflectance rate of green plant leaves could be used to identify the varieties. The spectral reflectance of winter rape, soil (dry and wet), and five kinds of weeds (speedwell, thistle, capsella, horseweed, and cerastium viscosum) were measured within the 350-2 500 nm wavelength range by the Analytical Spectral Device (ASD) in a laboratory. Each sample was measured five times continuously, and 370×5 samples were obtained. After rejecting 2×5 samples, a total of 368×5 samples (a 278×5 training set and a 90×5 prediction set) were used for classification, and a training set and a prediction set were randomly selected. The five original spectroscopic data sets were averaged in order to eliminate random noise. First, derivative and compressing were used to pretreat the spectral data. Then, stepwise discriminant analysis was executed to reduce the redundancy spectral information and decrease the amount of calculation and improve the accuracy. Four characteristic wavelengths, 710, 755, 950, and 595 nm were selected. Then canonical discriminant analysis and Bayes discriminant analysis were applied to build recognition models for identifying these weeds, soil, and winter rape based on the four characteristic wavelengths. For the canonical discriminant function, the recognition accuracy of training was 97.84%, two miscalculations were occurred in weeds, and the recognition ratio was 97.78%. The classification accuracy of the Bayesian discriminant model was higher than the canonical discriminant model, one error was observed in the testing set and its recognition ratio was 98.89% on the condition that prior probabilities were computed from group sizes. However, its classification accuracy is the same as canonical discriminant models when prior probabilities were all equal groups. The result of the statistical analysis showed that it was feasible to use the four characteristic wavelengths as input variables to build recognition models, which was an effective approach to simplify the recognition models. Both of the canonical discriminant function and Bayesian discriminant model have a strong ability to differentiate spectra of species of plant. However, the classification accuracy of the Bayesian discriminant model was higher when prior probabilities were computed from group sizes. The research results can provide reference for the development of spectrum sensor. |
| Keywords: | image recognition spectrum analysis discriminant analysis winter rape weed identification Bayesian discriminant |
| 本文献已被 万方数据 等数据库收录！ | |
| 点击此处可从《农业工程学报》浏览原始摘要信息 | |
| 点击此处可从《农业工程学报》下载免费的PDF全文 | |