An Analysis of Plant Diseases Identification Based on Deep Learning Methods.

IF 1.8 3区 农林科学 Q2 PLANT SCIENCES Plant Pathology Journal Pub Date : 2023-08-01 DOI:10.5423/PPJ.OA.02.2023.0034
Xulu Gong, Shujuan Zhang
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引用次数: 1

Abstract

Plant disease is an important factor affecting crop yield. With various types and complex conditions, plant diseases cause serious economic losses, as well as modern agriculture constraints. Hence, rapid, accurate, and early identification of crop diseases is of great significance. Recent developments in deep learning, especially convolutional neural network (CNN), have shown impressive performance in plant disease classification. However, most of the existing datasets for plant disease classification are a single background environment rather than a real field environment. In addition, the classification can only obtain the category of a single disease and fail to obtain the location of multiple different diseases, which limits the practical application. Therefore, the object detection method based on CNN can overcome these shortcomings and has broad application prospects. In this study, an annotated apple leaf disease dataset in a real field environment was first constructed to compensate for the lack of existing datasets. Moreover, the Faster R-CNN and YOLOv3 architectures were trained to detect apple leaf diseases in our dataset. Finally, comparative experiments were conducted and a variety of evaluation indicators were analyzed. The experimental results demonstrate that deep learning algorithms represented by YOLOv3 and Faster R-CNN are feasible for plant disease detection and have their own strong points and weaknesses.

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基于深度学习方法的植物病害识别分析
植物病害是影响作物产量的重要因素。植物病害种类多、病况复杂,造成严重的经济损失,也制约着现代农业的发展。因此,快速、准确、早期识别作物病害具有重要意义。深度学习的最新发展,特别是卷积神经网络(CNN),在植物病害分类方面显示出令人印象深刻的表现。然而,现有的植物病害分类数据集大多是单一背景环境,而不是真实的田间环境。此外,分类只能获得单一疾病的类别,无法获得多种不同疾病的位置,限制了实际应用。因此,基于CNN的目标检测方法可以克服这些缺点,具有广阔的应用前景。在本研究中,首先构建了一个真实田间环境下的带注释的苹果叶病数据集,以弥补现有数据集的不足。此外,我们还训练了Faster R-CNN和YOLOv3架构来检测数据集中的苹果叶片病害。最后进行对比实验,分析各种评价指标。实验结果表明,以YOLOv3和Faster R-CNN为代表的深度学习算法对于植物病害检测是可行的,且各有优缺点。
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来源期刊
Plant Pathology Journal
Plant Pathology Journal 生物-植物科学
CiteScore
4.90
自引率
4.30%
发文量
71
审稿时长
12 months
期刊介绍: Information not localized
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