Paper翻译：《Apple Leaf Diseases Recognition Based on An Improved Convolutional Neural Network》

• 论文名称：《Apple Leaf Diseases Recognition Based on An Improved Convolutional Neural Network》
• 论文作者：Yan Q , Yang B , Wang W , et al.
• 发表期刊：Sensors, 2020, 20(12):3535.
• 论文总结：

1. Research Gap:
   改进VGG16（VGG16 + BN + GAP）对苹果叶部病害进行检测
2. Importance：
   VGG16模型的模型参数上可以减少89%
   对4类苹果叶片的ACC为99.01%

• 论文地址： https://www.researchgate.net/publication/342401878_Apple_Leaf_Diseases_Recognition_Based_on_An_Improved_Convolutional_Neural_Network
• 论文目录
  

Abstract

摘要

原文	译文
Abstract: Scab, frogeye spot, and cedar rust are three common types of apple leaf diseases, and the rapid diagnosis and accurate identification of them play an important role in the development of apple production. In this work, an improved model based on VGG16 is proposed to identify apple leaf diseases, in which the global average poling layer is used to replace the fully connected layer to reduce the parameters and a batch normalization layer is added to improve the convergence speed. A transfer learning strategy is used to avoid a long training time. The experimental results show that the overall accuracy of apple leaf classification based on the proposed model can reach 99.01%. Compared with the classical VGG16, the model parameters are reduced by 89%, the recognition accuracy is improved by 6.3%, and the training time is reduced to 0.56% of that of the original model. Therefore, the deep convolutional neural network model proposed in this work provides a better solution for the identification of apple leaf diseases with higher accuracy and a faster convergence speed.	摘要： 黑星病、蛙眼斑病和雪松锈病是苹果常见的三种叶片病害，对其的快速诊断和准确鉴定对苹果生产的发展具有重要意义。在这项工作中，提出了一种基于VGG16的改进模型来识别苹果叶片病害，其中使用全局平均极化层代替全连接层以减少参数，并添加批量归一化层以提高收敛速度。迁移学习策略用于避免长时间的训练。实验结果表明，基于该模型的苹果叶片分类总体准确率可达99.01%。与经典VGG16相比，模型参数降低89%，识别准确率提升6.3%，训练时间减少至原模型的0.56%。因此，本文提出的深度卷积神经网络模型为苹果叶片病害识别提供了更好的解决方案，具有更高的准确率和更快的收敛速度。
Keywords: apple leaf diseases; transfer learning; deep learning; convolutional neural networks	关键词：苹果叶病； 迁移学习； 深度学习； 卷积神经网络

1.Introduction

原文	译文
Leaf diseases are one of the main obstacles to apple production. Among them, scab, frogeye spot, and cedar rust are three most common types of apple leaf diseases and have a bad impact on apple growing. Therefore, the detection of apple leaf diseases has attracted more and more attention, and the early identification of apple leaf disease is very important for the intervention of treatment. In the past, disease identification methods were generally divided into manual identification and an expert system. However, both of them are highly dependent on fruit growers and experts and are time-consuming and usually poor in generalization.	叶病是苹果生产的主要障碍之一。 其中，疮痂病、蛙眼斑病和雪松锈病是最常见的三种苹果叶病害，对苹果的生长影响很大。 因此，苹果叶病的检测越来越受到重视，而苹果叶病的早期识别对于干预治疗非常重要。 过去，疾病识别方法一般分为人工识别和专家系统。 然而，两者都高度依赖水果种植者和专家，并且耗时且普遍性较差。
With the development of machine learning methods, some computational models have been proposed for plant disease diagnosis based on different algorithms. Some studies have found diseased regions by K-means clustering-based segmentation and build disease recognition models using supervised learning methods, including the random forest, support vector machine (SVM), and K-nearest neighbor methods [1–3]. Rothe et al. used an active contour model for image segmentation and extracted Hu’s moments as features for the training of an adaptive neuro-fuzzy inference system, by which a classification accuracy of 85% can be achieved [4]. Gupta et al. proposed an autonomously modified SVM-CS model where a SVM model was trained and optimized using the concept of a cuckoo search [5]. However, these classification features are heavily depended on man-made selection and the recognition rates are not satisfactory.	随着机器学习方法的发展，一些基于不同算法的植物病害诊断计算模型被提出。 一些研究通过基于 K 均值聚类的分割发现了病变区域，并使用监督学习方法构建了疾病识别模型，包括随机森林、支持向量机 (SVM) 和 K 近邻方法 [1-3]。 罗特等人使用主动轮廓模型进行图像分割，并提取 Hu 的矩作为特征用于训练自适应神经模糊推理系统，由此可以实现 85% 的分类准确率 [4]。 古普塔等人 提出了一种自主修改的 SVM-CS 模型，其中使用布谷鸟搜索的概念对 SVM 模型进行训练和优化 [5]。 然而，这些分类特征严重依赖于人为选择，识别率并不理想。
In recent years, convolutional neural networks (CNNs) have shown good results in recognition tasks by reducing the need for image preprocessing and improving the identification accuracy [6–13]. Leaf disease recognition based on CNNs has become a new hotspot in the agricultural informatization area [14–16]. Lu et al. proposed a rice disease identification method based on deep CNN techniques and achieved an accuracy of 95.48% on a dataset of 500 natural images of diseased and healthy rice leaves [17]. Zhang et al. proposed the improved GoogLeNet and Cifar10 models and obtained the average identification accuracies of 98.9% and 98.8%, respectively [18]. Liu et al. designed a novel architecture of AlexNet to detect apple leaf diseases, and the experimental results showed that this approach achieved an overall accuracy of 97.62% for disease identification [19]. Although the recognition accuracy of these CNN models is higher than that of traditional machine learning methods, there are still some shortcomings—such as high model complexity, much more parameters, and a long training time—which prevent their application in real environments.	近年来，卷积神经网络（CNN）通过减少对图像预处理的需求和提高识别精度[6-13]，在识别任务中显示出良好的效果。基于CNNs的叶片病害识别已成为农业信息化领域的新热点[14-16]。卢等人提出了一种基于深度 CNN 技术的水稻病害识别方法，在 500 张病叶和健康水稻自然图像的数据集上实现了 95.48% 的准确率 [17]。张等人提出了改进的 GoogLeNet 和 Cifar10 模型，分别获得了 98.9% 和 98.8% 的平均识别准确率 [18]。刘等人设计了一种新的 AlexNet 架构来检测苹果叶病，实验结果表明，该方法对病害识别的总体准确率达到了 97.62% [19]。尽管这些 CNN 模型的识别准确率高于传统机器学习方法，但仍存在一些缺点，例如模型复杂度高、参数多、训练时间长等，阻碍了它们在实际环境中的应用。
In this work, we propose a method for apple leaf disease identification based on an improved deep convolution neural network architecture which can effectively reduce the model complexity and training time. The network proposed in this work adopts the concept of transfer learning to pre-train a VGG16 network and adjusts the network structure by removing three fully connected layers, adding a global average pooling layer, a batch normalization layer, and a fully connected layer. Based on a benchmark dataset, the proposed model, which can reach a 89% reduction in the model parameters of the original VGG16 model, greatly reduced the training time and achieved a higher accuracy rate.	在这项工作中，我们提出了一种基于改进的深度卷积神经网络架构的苹果叶片病害识别方法，可以有效降低模型复杂度和训练时间。 这项工作中提出的网络采用迁移学习的概念来预训练一个 VGG16 网络，并通过移除三个全连接层、添加一个全局平均池化层、一个批量归一化层和一个全连接层来调整网络结构。 基于基准数据集，所提出的模型在原始VGG16模型的模型参数上可以减少89%，大大减少了训练时间并获得了更高的准确率。

2.Methods

2.1 Data

原文

译文

The dataset in this work is from the “2008 ’AI Challenger’ Global Challenge” and includes 10 kinds of plants with 27 categories of diseases. This work addresses the automatic identification of apple leaf diseases, therefore only apple leaves are selected from this dataset. There are four categories of apple leaf images within the dataset, and Figure 1 lists some of them. With the exception of healthy leaves, three types of disease images—i.e., scab, frogeye spot, and cedar rust—are collected within the dataset. Typically, the lesions on scab leaves are gray-brown and nearly round or radial, frogeye spot is tan and the shape is flakes or dots, and cedar rust leaves have round orange-yellow lesions with red edges. Some spot and cedar rust lesions are similar in color and shape, which increases the difficulty in recognition by computational methods.

本作品中的数据集来自“2008‘AI Challenger’全球挑战赛”，包括10种植物27类病害。 这项工作解决