基于图像融合特征的番茄叶部病害的识别

Recognition of tomato leaf disease based on image fusion feature

为了提高基于数字图像识别番茄叶部病害的准确率,适应不同分辨率条件下的应用需求,幵满足实践拍摄条件的不确定性,以番茄晚疫病、花叶病、早疫病叶片图像为研究对象,选择HSV模型中的4维H分量等量分割波段作为颜色特征,基于灰度差分统计的均值、对比度和熵3维特征作为纹理特征,融合7维特征向量作为支持向量机(SVM)分类器的输入,用粒子群算法(PSO)优化SVM模型参数。试验结果表明,融合灰度差分统计与H分量4维特征的病害识别模型准确率可达90%。

In order to improve the accuracy of tomato disease recognition based on digital image, to meet the requirements under different resolution when application, and satisfy the uncertainty of actual shooting conditions, a recognition model of tomato leaf disease was built based on image fusion feature. By using the tomato late blight, Mosaic, and early blight leaf image as the research object, four H-components of HSV color model were selected as color features, and the mean, contrast and entropy of gray difference statistics were used as texture features. The seven dimension feature vector is used as the input of SVM classifier, and particle swarm optimization algorithm is used to optimize the SVM model parameters. The test results show that the accuracy of the model is up to 90%, which recognised tomato leaf disease based on image fusion feature.