Abstract
The potato is a widely grown crop in many regions of the world. In recent decades, potato farming has gained incredible traction in the world. Potatoes are susceptible to several illnesses that stunt their development. This plant seems to have significant leaf disease. Early Blight and Late Blight are two prevalent leaf diseases that affect potato plants. The early detection of these diseases would be beneficial for enhancing the yield of this crop. The ideal solution is to use image processing to identify and analyze these disorders. Here, we present an autonomous method based on image processing and machine learning to detect late blight disease affecting potato leaves. The proposed method comprises four different phases: (1) Histogram Equalization is used to improve the quality of the input image; (2) feature extraction is performed using a Deep CNN model, then these extracted features are concatenated; (3) feature selection is performed using wrapper-based feature selection; (4) classification is performed using an SVM classifier and its variants. This proposed method achieves the highest accuracy of 99% using SVM by selecting 550 features.
Abstract (translated)
土豆是世界上许多地区广泛种植的一种作物。近年来，土豆的栽培在全球范围内得到了极大的推广。然而，土豆容易受到多种病害的影响，这些病害会抑制其生长发育。这里有一种植物似乎患上了严重的叶部疾病。早疫病和晚疫病是影响土豆植株的两种常见叶部疾病。及早发现这些病症将有助于提高作物产量。 理想的解决方案是利用图像处理技术来识别和分析这些问题。在这里，我们提出了一种基于图像处理和机器学习的自主方法，用于检测影响土豆叶片的晚疫病。所提出的这种方法包含了四个不同的阶段： 1. **直方图均衡化**：用于改善输入图像的质量。 2. **特征提取**：利用深度卷积神经网络（Deep CNN）模型进行特征提取；然后将这些提取出的特征进行串联。 3. **特征选择**：采用基于包装器的方法来进行特征选择。 4. **分类**：使用支持向量机（SVM）分类器及其变体进行分类。 该方法通过选择550个特征，利用SVM实现了高达99%的最高准确率。
URL
https://arxiv.org/abs/2502.03370
