论文标题
使用卷积神经网络对图像上的植物疾病识别：系统评价
Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review
论文作者
论文摘要
植物疾病被认为是影响粮食生产并最大程度减少生产损失的主要因素之一，至关重要的是作物疾病具有快速的检测和识别。深度学习方法的最新扩展发现了其在植物疾病检测中的应用，提供了具有高度准确结果的强大工具。在这种情况下，这项工作对文献进行了系统的综述，旨在确定使用卷积神经网络（CNN）在植物疾病的识别和分类过程中使用卷积神经网络（CNN）的现状，划分趋势并指示差距。从这个意义上讲，我们介绍了过去十年中选择的121篇论文，采用不同的方法来处理与疾病检测，数据集的特征，作物和病原体有关的方面。从系统评价的结果来看，可以了解有关使用CNN在识别植物疾病中的创新趋势，并确定需要研究界注意的差距。
Plant diseases are considered one of the main factors influencing food production and minimize losses in production, and it is essential that crop diseases have fast detection and recognition. The recent expansion of deep learning methods has found its application in plant disease detection, offering a robust tool with highly accurate results. In this context, this work presents a systematic review of the literature that aims to identify the state of the art of the use of convolutional neural networks(CNN) in the process of identification and classification of plant diseases, delimiting trends, and indicating gaps. In this sense, we present 121 papers selected in the last ten years with different approaches to treat aspects related to disease detection, characteristics of the data set, the crops and pathogens investigated. From the results of the systematic review, it is possible to understand the innovative trends regarding the use of CNNs in the identification of plant diseases and to identify the gaps that need the attention of the research community.
