ABSTRACT

Plant disease classification using image processing techniques is a prominent and challenging area of research. We have developed a novel classification technique to classify, especially spot and blight diseased leaf images of four different plant species. In this technique, we have dealt with the infection patterns manifested on leaves. The infection patterns seem to correlate with diseases. Both these diseases cause similar patterns on leaves, and hence they are hard to distinguish. The proposed technique succeeded in handling the task to a reasonable extent. Statistical texture features derived from Grey-Level-Co-occurrence-Matrix (GLCM) are considered as features. The final feature set contains strongly correlated features. An impact level of each feature is derived from its standard deviation for the image set. The novel classification technique makes use of these impact levels. A 74% disease classification accuracy is achieved in the best-case scenario and identified an optimal threshold range that helps us classify the diseases.

摘要

使用图像处理技术对植物病害进行分类是一个突出且具有挑战性的研究领域。我们已经开发出一种新颖的分类技术，可以对四种不同植物物种的斑点和枯萎病叶片图像进行分类，尤其是斑点和枯萎病。在这项技术中，我们处理了叶片上出现的感染模式。感染方式似乎与疾病有关。这两种疾病在叶片上引起相似的模式，因此很难区分。所提出的技术在合理范围内成功地完成了任务。从灰度共生矩阵（GLCM）派生的统计纹理特征被视为特征。最终功能集包含高度相关的功能。每个特征的影响度是从图像集的标准偏差得出的。新颖的分类技术利用了这些影响程度。在最佳情况下，可以达到74％的疾病分类准确性，并且可以确定有助于我们对疾病进行分类的最佳阈值范围。