用深度学习和特征选择，99%准确识别马铃薯晚疫病叶片病变。
Deep Learning-Based Approach for Identification of Potato Leaf Diseases Using Wrapper Feature Selection and Feature Concatenation
- 用深度CNN提取特征后拼接，再通过包裹式选择筛选550个关键特征。
- 在测试集上达到99%准确率，显著优于传统方法。
- 适合农业图像分析、病害自动识别的研究与应用者。
马铃薯是全球广泛种植的作物，近年来种植规模持续扩大。马铃薯易受多种病害影响，尤其是早疫病和晚疫病，严重阻碍其生长发育。早期发现这些病害对提高产量至关重要。本研究提出一种基于图像处理与机器学习的自动化方法，用于检测马铃薯叶片上的晚疫病。该方法包含四个阶段：(1) 使用直方图均衡化提升输入图像质量；(2) 采用深度卷积神经网络（Deep CNN）提取特征，并进行特征拼接；(3) 通过包裹式特征选择方法筛选关键特征；(4) 使用支持向量机（SVM）及其变体进行分类。实验结果表明，该方法在选取550个特征时达到最高99%的分类准确率，表现出优异的识别性能。
原文摘要 · Abstract (English)
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.
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