论文标题
卷积神经网络的合奏，以检测苹果植物中的叶面疾病
An Ensemble of Convolutional Neural Networks to Detect Foliar Diseases in Apple Plants
论文作者
论文摘要
苹果疾病（即使没有及早诊断）可能会导致大量资源损失，并对食用受感染苹果的动物构成严重威胁。因此，重要的是要尽早诊断这些疾病，以管理植物健康并最大程度地降低与之相关的风险。但是，监测植物疾病的常规方法是手动侦察并分析植物叶的特征，质地，颜色和形状，从而导致诊断和错误判断的延迟。我们的工作提出了一个结合的X受体系统，InceptionResnet和Mobilenet架构，以检测5种不同类型的苹果植物疾病。该模型已接受了公开可用的植物病理学2021数据集的培训，可以在给定的植物叶中对多种疾病进行分类。该系统在多级和多标签分类中取得了出色的成果，可以在实时环境中使用，以监视大型苹果种植园，以帮助农民有效地管理其产量。
Apple diseases, if not diagnosed early, can lead to massive resource loss and pose a serious threat to humans and animals who consume the infected apples. Hence, it is critical to diagnose these diseases early in order to manage plant health and minimize the risks associated with them. However, the conventional approach of monitoring plant diseases entails manual scouting and analyzing the features, texture, color, and shape of the plant leaves, resulting in delayed diagnosis and misjudgments. Our work proposes an ensembled system of Xception, InceptionResNet, and MobileNet architectures to detect 5 different types of apple plant diseases. The model has been trained on the publicly available Plant Pathology 2021 dataset and can classify multiple diseases in a given plant leaf. The system has achieved outstanding results in multi-class and multi-label classification and can be used in a real-time setting to monitor large apple plantations to aid the farmers manage their yields effectively.
