论文标题
基于图像的植物疾病诊断，基于颜色的重建性，无监督的异常检测
Image-based Plant Disease Diagnosis with Unsupervised Anomaly Detection Based on Reconstructability of Colors
论文作者
论文摘要
本文提出了一种用于基于图像的植物疾病诊断的无监督异常检测技术。建造包含标有健康和患病作物图像的标记图像的大型公开数据集，导致对计算机视觉技术的兴趣日益增加，以自动植物疾病诊断。尽管基于深度学习的监督图像分类器可能是植物性诊断的有力工具，但它们需要大量的标记数据。异常检测的数据挖掘技术包括无监督的方法，这些方法不需要罕见的训练分类器样本。我们提出了一种基于颜色的可重建性的基于图像植物疾病诊断的无监督异常检测技术。一个深层编码器网络，经过培训，可以重建\ textit {健康}植物图像的颜色，不得重建有症状区域的颜色。我们提出的方法包括一个新的基于图像的植物疾病检测框架，该框架利用了一个名为Pix2Pix的条件对抗网络，以及基于CIEDE2000颜色差的新异常得分。使用PlantVillage数据集进行的实验证明了我们所提出的方法的优越性与现有的异常检测器相比，在准确性，可解释性和计算效率方面识别患病的作物图像。
This paper proposes an unsupervised anomaly detection technique for image-based plant disease diagnosis. The construction of large and publicly available datasets containing labeled images of healthy and diseased crop plants led to growing interest in computer vision techniques for automatic plant disease diagnosis. Although supervised image classifiers based on deep learning can be a powerful tool for plant disease diagnosis, they require a huge amount of labeled data. The data mining technique of anomaly detection includes unsupervised approaches that do not require rare samples for training classifiers. We propose an unsupervised anomaly detection technique for image-based plant disease diagnosis that is based on the reconstructability of colors; a deep encoder-decoder network trained to reconstruct the colors of \textit{healthy} plant images should fail to reconstruct colors of symptomatic regions. Our proposed method includes a new image-based framework for plant disease detection that utilizes a conditional adversarial network called pix2pix and a new anomaly score based on CIEDE2000 color difference. Experiments with PlantVillage dataset demonstrated the superiority of our proposed method compared to an existing anomaly detector at identifying diseased crop images in terms of accuracy, interpretability and computational efficiency.
