Yu Jia-Jia, He Yong
College of Electrical and Electronics Engineering, Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou 310053, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2013 Aug;33(8):2168-71.
The present paper put forward the technology route for feature images extraction of grey mold sick on tomato leaves based on SIMCA--combination image extraction based on MLR-grey mold sick information extraction based on minimum distance method. Firstly, through the 680-740 nm band's variance image and the discrimination power parameter, the feature band images was found, then the feature bands information was used as the input of MLR analysis, and under the 0.5 accuracy threshold value, 99% accuracy was obtained, which showed the discrimination power of the features bands for grey mold sick tomato leaf detection, and using the MLR regression coefficient to extract a band combination image, and through the minimum distance method, tomato grey mold sick information was found. The result shows that the proposed method has a very good prediction ability and greatly reduces the hyperspectral data processing time.
本文提出了基于SIMCA的番茄叶片灰霉病特征图像提取技术路线——基于MLR的组合图像提取——基于最小距离法的灰霉病信息提取。首先，通过680 - 740nm波段的方差图像和判别力参数，找到特征波段图像，然后将特征波段信息作为MLR分析的输入，在0.5精度阈值下，获得了99%的准确率，这表明特征波段对番茄灰霉病叶片检测具有判别力，并利用MLR回归系数提取一个波段组合图像，通过最小距离法，找到番茄灰霉病信息。结果表明，该方法具有很好的预测能力，大大减少了高光谱数据处理时间。
