| 曹晓曼， 臧 英， 周志艳，等.基于机器视觉的稻田灌溉用水铜离子浓度检测[J].广东农业科学,2015,42(4):147-152 |
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基于机器视觉的稻田灌溉用水铜离子浓度检测 |
| Detection of copper ion concentration in paddy field irrigation based on machine vision |
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| DOI： |
| 中文关键词: 水稻 灌溉 机器视觉 铜离子浓度 检测 |
| 英文关键词: rice irrigation machine vision copper ion concentration detection |
| 基金项目: |
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| 摘要点击次数: 1462 |
| 全文下载次数: 652 |
| 中文摘要: |
| 为实时、快速地监测稻田灌溉用水的质量，确保粮食生产安全，提出了基于机器视觉的稻田灌溉
用水铜离子浓度检测方法。 利用搭建的机器视觉检测试验装置，设计了不同浓度铜离子溶液的机器视觉检测
试验，通过提取试纸图像的特征值，运用指数回归、对数回归、2 阶多项式回归和线性回归等方法建立铜离子浓
度的预测模型，减少了人为认知对检测结果的影响。 试验结果表明，在 0~300 mg/L 范围内，基于机器视觉的试
纸检测水中铜的方法训练集和预测集的相关系数分别达到 0.9438 和 0.9191， 均方根误差分别为 19.9563、
9.7889 mg/L，误差控制在 8%以下，基本上达到了对稻田灌溉用水进行实时快速检测的要求，为进一步开发实
用的稻田灌溉用水监测设备提供了依据。 |
| 英文摘要: |
| n order to monitor the quality of paddy field irrigation water real-time and fastly, and ensure thesafety of food production, a method based on machine vision for detection of copper ion concentration in the paddyfield irrigation water was proposed in this paper. Detection tests based on machine vision of different concentrationsof copper ion solution was designed using detection tester with machine vision. By extracting the eigenvalues of testimages, methods including index regression, logistic regression, two order polynomial regression and linear regressionwere used to build prediction model of copper ion concentration, which reduced the influence of human cognitive ontesting results. The results showed that, in the range of 0-300 mg/L, the prediction model of copper ion concentrationwas achieved with correlation coefficient of 0.9438 and root mean square error of 19.9563 mg/L for training set andcorrelation coefficient of 0.9191 and root mean square error of 9.7889 mg/L for prediction set. The relative error waskept below 8 percent, basically achieving the rapid detection of paddy field irrigation water in real time. The resultsof this study provide the basis for the further development of paddy field irrigation equipment utility. |
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