论文标题
帕迪医生：用于自动帕迪疾病分类和基准测试的视觉图像数据集
Paddy Doctor: A Visual Image Dataset for Automated Paddy Disease Classification and Benchmarking
论文作者
论文摘要
帕迪农民面临的关键生物应力因素之一是由细菌，真菌和其他生物引起的疾病。这些疾病严重影响植物的健康，并导致巨大的作物损失。这些疾病中的大多数可以通过定期观察专家监督的叶子和茎来识别。在一个拥有庞大的农业地区和有限的农作物保护专家的国家中，对稻病的手动识别具有挑战性。因此，要添加解决此问题的解决方案，有必要自动化疾病识别过程并提供易于访问的决策支持工具，以实现有效的作物保护措施。但是，缺乏具有详细疾病信息的公共数据集的可用性限制了准确的疾病检测系统的实际实施。本文介绍了\ emph {paddy Doctor}，这是一种可视觉图像数据集，用于识别稻病疾病。我们的数据集包含13个类别（12种疾病和正常叶子）的16,225个带注释的稻叶图像。我们使用卷积神经网络（CNN）和四个基于转移学习的模型（VGG16，Mobilenet，Xpection和resnet34）对\ Emph {Paddy Doctor}进行了基准测试。实验结果表明，RESNET34的F1得分最高为97.50％。我们将数据集和可再现代码发布在开源供社区使用中。
One of the critical biotic stress factors paddy farmers face is diseases caused by bacteria, fungi, and other organisms. These diseases affect plants' health severely and lead to significant crop loss. Most of these diseases can be identified by regularly observing the leaves and stems under expert supervision. In a country with vast agricultural regions and limited crop protection experts, manual identification of paddy diseases is challenging. Thus, to add a solution to this problem, it is necessary to automate the disease identification process and provide easily accessible decision support tools to enable effective crop protection measures. However, the lack of availability of public datasets with detailed disease information limits the practical implementation of accurate disease detection systems. This paper presents \emph{Paddy Doctor}, a visual image dataset for identifying paddy diseases. Our dataset contains 16,225 annotated paddy leaf images across 13 classes (12 diseases and normal leaf). We benchmarked the \emph{Paddy Doctor} dataset using a Convolutional Neural Network (CNN) and four transfer learning based models (VGG16, MobileNet, Xception, and ResNet34). The experimental results showed that ResNet34 achieved the highest F1-score of 97.50%. We release our dataset and reproducible code in the open source for community use.
