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.

中文翻译：

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一种新的植物叶片斑枯病图像分类技术

摘要使用图像处理技术进行植物病害分类是一个突出且具有挑战性的研究领域。我们开发了一种新的分类技术来对四种不同植物物种的斑点和枯萎病叶图像进行分类。在这项技术中，我们处理了叶子上表现出的感染模式。感染模式似乎与疾病相关。这两种疾病都会在叶子上产生相似的图案，因此很难区分。所提出的技术成功地在合理范围内处理了任务。从灰度共生矩阵 (GLCM) 派生的统计纹理特征被视为特征。最终的特征集包含强相关的特征。每个特征的影响级别从其对图像集的标准偏差得出。新的分类技术利用了这些影响级别。在最佳情况下实现了 74% 的疾病分类准确度，并确定了一个最佳阈值范围，帮助我们对疾病进行分类。