基于Fisher判别分析的玉米叶部病害图像识别

 【目的】利用计算机视觉技术实现玉米叶部病害的自动识别诊断。【方法】在大田开放环境下采集病害图像样本,综合应用基于H阈值分割、迭代二值化、图像形态学运算、轮廓提取等算法处理病害图像,抽取病斑,提取病害图像的纹理、颜色、形状等特征向量,采用遗传算法优化选择出分类特征,并利用费歇尔判别法识别普通锈病、大斑病和褐斑病3种玉米叶部病害。【结果】研究中提取了墒、相关信息测度、分形维数、H值、Cb值、颜色矩、病斑面积、圆度、形状因子等28个特征向量,利用遗传算法优选出H值、颜色矩、病斑面积、形状因子等4个独立、稳定性好、分类能力强的特征向量,应用费歇尔判别分析法识别病害,准确率达到90%以上。【结论】综合运用数字图像处理技术、图像纹理、颜色、形状特征分析方法、遗传算法、费歇尔判别分析方法可以有效识别基于田间条件下采集的病害图像,为田间开放环境下实现大田作物病虫害的快速智能诊断提供借鉴。

Maize Leaf Disease Identification Based on Fisher Discrimination Analysis

Objective] The recognition and diagnosis methods of main maize leaf diseases using machine vision were studied in this paper. Method] The diseases pictures of different varieties or periods were taken in fields, methods of threshold segmentation based on hue, iteration binarization, image morphological operation and contour extraction were adopted for image processing and image segmentation, then the texture, color and shape features were extracted. Genetic algorithm was used to get approximate features. Finally Fisher discrimination analysis was applied to recognize main maize leaf diseases. Result] In the research, 28 characters including energy, informationization measure, fractal dimension, hue, cb, color moment, disease spot area, rotundity, figure factor, and others were extracted, and four approximate features were selected from 28 primordial features. The results indicated that the precision of the three kinds of maize disease recognition was higher than 90%. Conclusion] Disease image obtained in fields were recognized by the application of digital image processing technology, analysis of image texture, color and figure characters, genetic algorithm and Fisher discrimination analysis. It has provided a technical support for the automatic recognition of crop diseases and insets with disease image obtained in fields.