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首页> 外文期刊>Applied Engineering in Agriculture >RATINGS OF RICE LEAF BLAST DISEASE BASED ON IMAGE PROCESSING AND STEPWISE REGRESSION
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RATINGS OF RICE LEAF BLAST DISEASE BASED ON IMAGE PROCESSING AND STEPWISE REGRESSION

机译：基于图像处理和逐步回归的水稻叶毒病评级

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摘要

In this research, an evaluation method involving digital image processing and stepwise regression was studied to establish an efficient and accurate rating system for studying rice blast disease. For this purpose, the R-G image was segmented by using maximum interclass variance method in which the lesion and naturally withered region was extracted from the leaves. Then, 240 lesion areas and 240 natural yellow areas were selected as samples. During the experiment, ten morphological features and five texture features were extracted. Subsequently, for lesion identification, stepwise regression analysis, SVM and BP neural network were used. In the results, regression analysis of naturally yellow areas showed the highest accuracy in lesion identification, reaching 93.33% for disaster-level assessment of identified lesion areas. On the basis of the results, it is evident that 153 samples were correctly classified into divisions of 160 tested different rice blast leaves, with 95.63% classification accuracy. This study has introduced a new method for objective assessment of leaf blast disease.
机译：在该研究中，研究了涉及数字图像处理和逐步回归的评估方法，以建立用于研究水稻爆炸疾病的有效和准确的评级系统。为此目的，通过使用最大癌细胞变异方法对R-G图像进行分段，其中从叶子中提取病变和天然含有的区域。然后，选择240个病变区和240个天然黄色区域作为样品。在实验期间，提取十种形态特征和五种纹理特征。随后，对于病变鉴定，使用逐步回归分析，SVM和BP神经网络。在结果中，对天然黄色区域的回归分析显示出病变鉴定的最高精度，达到识别的病变区的灾害水平评估的93.33％。在结果的基础上，显然，将153个样品正确分为160个测试的不同稻瘟病叶，分类精度为95.63％。本研究介绍了一种新的叶片爆发性评估方法。

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