基于叶片图像和环境信息的黄瓜病害识别方法

作物病害严重影响着作物的产量和质量,病害类型识别是病害防治的前提。利用图像处理和统计分析,提出了一种基于病害叶片图像和环境信息的黄瓜病害类别识别方法。采集不同季节、温度和湿度等环境下的病害叶片图像,并记录病害的环境信息;利用属性约简法提取病害叶片的5个环境信息特征向量,对病害叶片图像进行一系列图像处理,提取病斑图像的颜色、形状、纹理等35个统计特征向量。将两者结合得到黄瓜病害的40个特征分量。再利用统计分析系统(statistical analysis system,SAS)的判别分析方法,选择10个分类能力强的特征分量,计算作物病害的聚类中心分类特征向量。最后,利用最大隶属度准则识别病害叶片的病斑类别。对黄瓜的霜霉病、褐斑病和炭疽病3种叶部病害的识别率高达90%以上。试验结果表明,该方法能够有效识别作物叶部病害类别,可为田间开放环境下实现作物病害的快速自动识别提供依据。

Recognition of cucumber diseases based on leaf image and environmental information

Abstract: Crop disease is one of the main disasters for Chinese agriculture and it seriously affects the yield and quality of crops, and causes economic losses to farmers. Early detection and prevention of crop diseases is critical to control the diseases, improve crop yields, reduce the economic losses and control pesticide pollution. Therefore, the research of recognition methods for crop diseases is necessary. In this study, a disease recognition method of cucumber disease, based on leaf image and environmental information, is proposed. In this method, the cucumber disease features, including environmental classifying features and disease leaf classifying features, were extracted by image processing and statistical analysis methods. The classifying features were then selected by SAS discriminant analysis, and the cucumber diseases were identified by using the rule of maximum membership degree. The cucumber leaves and their environmental information of Downy mildew, Brown Speck, and Anthracnose were collected for disease recognition. The diseased cucumber leaf images were processed by using a series of image pre-processing methods, such as image transforming, smoothing, and segmentation. White was chosen as the background of the diseased leaf images, a median filter was applied to effectively remove the disturbance of noise, and the color image segmentation method, based on statistical pattern recognition, was applied to separate the disease spot images from the diseased leaf images. The five features of environmental information were extracted, and the 35 statistical eigen vectors of color, shape, and texture of the diseased leaf images could be extracted by statistical analysis. Then, 40 disease union classifying features were obtained. Ten strong classifying features were then selected by the SAS discriminant analysis method. The feature vectors of the clustering center were then computed. Finally, three kinds of cucumber diseases were recognized by the maximum membership degree. The disease recognition method proposed in this study differs essentially from the traditional ones. Traditional methods only take into account features extracted from diseased leaf images, which makes the recognition rate of traditional methods low because the diseased leaf image is quite complex. However, the proposed disease recognition method contains not only the leaf image information, but also the environmental information, which improves the robustness and recognition rate of the proposed method. The recognition results of three kinds of cucumber diseases by the proposed method were more than 93 percent. The experiment results show that cucumber diseased leaf images obtained under different conditions were effectively recognized by the integrated application of image processing technology, analysis of image texture, color and figure characteristics, and analysis of the disease environmental information, etc. It has provided a technical basis and support for the automatic recognition of crop diseases with diseased leaf images and environmental information obtained in the field. The analysis and experimental results in this paper demonstrated that the crop disease recognition method is feasible by computer vision, statistics, and comprehensive utilization technology of spot color, texture, shape information, and crop environment information. As there are many factors affecting crop diseases, various diseases in different periods will show different symptoms. Therefore, how to use the computer vision technology and the disease environmental information to build a powerful and practical crop disease recognition method still needs further study.