本文提出了一种新的卷积神经网络(CNN)模型，结合对比度拉伸预处理和模糊c均值(FCM)聚类算法，用于识别和分类苹果叶病害。使用400张苹果叶图像（200张健康，200张患病）进行训练和验证，模型达到了98.06%的准确率。与传统方法相比，该模型在较小的数据集上实现了高性能，展示了其在植物病害识别领域的潜力。
- 论文名称：《A Novel Convolutional Neural Network Based Model for Recognition and Classification of Apple Leaf Diseases》
- 论文作者：Yadav, D. , Akanksha, and A. K. Yadav .
- 发表期刊：Traitement du Signal 37.6(2020):1093-1101.
- 论文总结：
- Research Gap:
数据增强与CNN对苹果叶部病害进行检测 - Importance：
使用了对比度拉伸和FCM聚了算法进行数据增强
对4类苹果叶片的ACC为98.06%
Abstract
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Plants have a great role to play in biodiversity sustenance. These natural products not only push their demand for agricultural productivity, but also for the manufacturing of medical products, cosmetics and many more. Apple is one of the fruits that is known for its excellent nutritional properties and is therefore recommended for daily intake. However, due to various diseases in apple plants, farmers have to suffer from a huge loss. This not only causes severe effects on fruit’s health, but also decreases its overall productivity, quantity, and quality. A novel convolutional neural network (CNN) based model for recognition and classification of apple leaf diseases is proposed in this paper. The proposed model applies contrast stretching based pre-processing technique and fuzzy c-means (FCM) clustering algorithm for the identification of plant diseases. These techniques help to improve the accuracy of CNN model even with lesser size of dataset. 400 image samples (200 healthy, 200 diseased) of apple leaves have been used to train and validate the performance of the proposed model. The proposed model achieved an accuracy of 98%. To achieve this accuracy, it uses lesser data-set size as compared to other existing models, without compromising with the performance, which become possible due to use of contrast stretching pre-processing combined with FCM clustering algorithm. |
植物在维持生物多样性方面发挥着重要作用。这些天然产品不仅推动了他们对农业生产力的需求，还推动了医疗产品、化妆品等的制造。苹果是以其卓越的营养特性而闻名的水果之一，因此建议每天摄入。然而，由于苹果植株的各种病害，农民不得不蒙受巨大的损失。这不仅会对水果的健康造成严重影响，还会降低其整体生产力、数量和质量。本文提出了一种新的基于卷积神经网络（CNN）的苹果叶病害识别和分类模型。所提出的模型应用基于对比度拉伸的预处理技术和模糊 c 均值 (FCM) 聚类算法来识别植物病害。即使使用较小的数据集，这些技术也有助于提高 CNN 模型的准确性。已使用 400 个苹果叶图像样本（200 个健康的，200 个患病的）来训练和验证所提出模型的性能。所提出的模型达到了 98% 的准确率。为了达到这种精度，与其他现有模型相比，它使用更小的数据集大小，而不会影响性能，由于使用对比度拉伸预处理与 FCM 聚类算法相结合，这成为可能。 |
| Keywords: plants, apple, contrast stretching, fuzzy c means, CNN, disease diagnosis | 关键词：植物，苹果，对比拉伸，模糊c均值，CNN，疾病诊断 |
1.Introduction
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| The Indian economy is heavily dependent on efficient agriculture. The detection of diseases in plants therefore plays an important role in agriculture [1]. The use of automated disease detection techniques is advantageous for the fast identification of diseases in plants [2]. For instance, black rot is one of the most prevalent and serious diseases that plagues apple trees. They appear as brown spots, which expand in concentrated circles and finally turn black, decaying the fruits. Later, the disease spreads to the roots of the tree causing cancers that can ultimately kill the tree. Early-stage detection of these diseases in such situations could have been helpful. | 印度经济严重依赖高效农业。 因此，植物病害检测在农业中起着重要作用[1]。 自动病害检测技术的使用有利于快速识别植物中的病害 [2]。 例如，黑腐病是困扰苹果树的最普遍和最严重的疾病之一。 它们表现为褐色斑点，呈集中圆形扩大，最后变黑，使果实腐烂。 后来，这种疾病蔓延到树的根部，导致最终可以杀死树的癌症。 在这种情况下早期发现这些疾病可能会有所帮助。 |
| To prevent large losses, different techniques for diagnosing diseases have been developed in the past. Techniques developed in microbiology and immunology offer correct recognition of the causative agents. Nonetheless, for many farmers, these approaches are inaccessible and require a thorough knowledge of the region or a large amount of money and energy to carry out. As per the United Nations Food and Agriculture Organization, most farms in the world are small and managed by families in developing countries such as India [3]. Such families grow food for a large proportion of the population of the country. Even so, hunger and food scarcity are not unusual and, market access and resources are constrained. For the above reasons, much work has been done in order to develop methods that are sufficiently reliable and available to the majority of farmers. The techniques of digital image processing increase the chance of early identification of diseases in plants, so that the required preventive steps can be taken [4]. | 为了防止大的损失，过去已经开发了不同的疾病诊断技术。微生物学和免疫学中开发的技术可以正确识别病原体。尽管如此，对于许多农民来说，这些方法是无法获得的，需要对区域有透彻的了解，或者需要大量的资金和精力才能实施。根据联合国粮食及农业组织的数据，世界上大多数农场都很小，由印度等发展中国家的家庭管理[3]。这些家庭为该国大部分人口种植粮食。即便如此，饥饿和粮食短缺并不罕见，市场准入和资源受到限制。由于上述原因，已经做了很多工作来开发足够可靠且可供大多数农民使用的方法。数字图像处理技术增加了早期识别植物病害的机会，从而可以采取所需的预防措施[4]。 |
While researchers have worked rigorously to identify plant diseases using different methods such as RNA/DNA, sensor techniques, etc. [5] but the field of machine vision to identify manifestations of fruit leaf diseases is still less examined. Apples are one of the widely consumed fruits, a great source of phytochemicals mostly expressing pertinent antioxidant abilities in vitro, and scientific studies have related apple ingestion to a lower chance of certain cancers, cardiovascular disease, asthma, and diabetes [6, 7]. The consolidated list of abbreviations used in the manuscript is as shown in Table 1. |
虽然研究人员已经使用不同的方法（如 RNA/DNA、传感器技术等）进行了严格的工作以识别植物病害 [5]，但机器视觉领域识别果叶病害的表现仍然很少被研究。 苹果是一种广泛食用的水果，是植物化学物质的重要来源，主要在体外表现出相关的抗氧化能力，科学研究表明，摄入苹果与某些癌症、心血管疾病、哮喘和糖尿病的几率较低有关 [6, 7]。 手稿中使用的缩略的综合列表如表 1 所示。 |
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| The main contributions in this work are stated as follows: 1.A novel convolutional neural network based model for recognition and classification of apple leave disease is proposed. The proposed model utilizes contrast stretching based pre-processing and fuzzy c-means clustering for image segmentation. Both these approaches boost the performance of CNN classier even on lesser size of training data as compared to other state of the art methods. 2.A comprehensive discussion on the existing work is presented to elaborate the research gaps. 3. Extensive computer simulations are performed to determine the effectiveness of the proposed system. A benchmark dataset (Kaggle) which is composed of 4-types of apple leaves are used for simulations. Simulation result reveals that the proposed system showed competitive performance over the other state-of-the-art methods. |
这项工作的主要贡献如下： 1.提出了一种新的基于卷积神经网络的苹果叶病识别和分类模型。 所提出的模型利用基于对比度拉伸的预处理和模糊 c 均值聚类进行图像分割。 与其他最先进的方法相比，这两种方法即使在较小规模的训练数据上也能提高 CNN 分类器的性能。 2.对现有工作进行全面讨论，阐述研究差距。 3. 进行广泛的计算机模拟以确定所提议系统的有效性。 由 4 种苹果叶组成的 基准数据集 (Kaggle) 用于模拟。 仿真结果表明，与其他最先进的方法相比，所提出的系统表现出具有竞争力的性能。 |
2. RELATED WORK
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Traditional ways for identifying as well as analyzing the diseases in fruit leaves are manual. However, these manual processes take time, are cumbersome and also very subjective [8]. Several methods have been developed in recent years incorporating computer vision to detect and identify agricultural and horticultural crop diseases to address the manual techniques issues [9, 10]. Image collection, retrieval of features, filtering of features and classification analysis with parametric or non-parametric statistics are fundamental steps in those processes. Image processing techniques and classification mechanisms are the main concern for the efficient functioning of the computer vision system. |
识别和分析果叶病害的传统方法是手动的。 然而，这些手动过程需要时间、繁琐且非常主观[8]。 近年来已经开发了几种方法，结合计算机视觉来检测和识别农业和园艺作物病害，以解决手动技术问题 [9, 10]。 图像收集、特征检索、特征过滤和使用参数或非参数统计的分类分析是这些过程中的基本步骤。 图像处理技术和分类机制是计算机视觉系统有效运行的主要问题。 |
Research on the identification of plant disease using machine learning is on the rise. The main reason it may be that expert eye observation of scientists has often proved to be very impractical for such systems and moreover, constant surveillance is needed, which is very costly when dealing with large farms [11]. In some areas, farmers do not have appropriate facilities or even the knowledge that they can seek experts. Under these conditions, automatic disease detection by seeing the signs on the leaves of plants makes the system much faster, easier and cheaper. This also promotes machine vision |
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