Smart agriculture: An intelligent approach for apple leaf disease identification based on convolutional neural network

IF 1.1 4区 农林科学 Q3 PLANT SCIENCES Journal of Phytopathology Pub Date : 2024-08-10 DOI:10.1111/jph.13374
Jiangong Ni
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引用次数: 0

Abstract

Plant diseases pose a significant threat to global agricultural productivity and food safety. Early detection and accurate identification of these diseases are essential for effective disease management strategies. Traditional plant disease identification mainly relies on manual observation and experienced expert judgement, which has the disadvantages of being time-consuming, labour-intensive and low efficiency. Given the above problems, this study proposes a method for identifying apple leaf diseases based on a convolutional neural network combining hybrid attention and bidirectional long short-term memory (BiLSTM). Appropriate apple leaf disease samples were selected from multiple public data sets to form an experimental data set. Then, the data set is imported into the improved convolutional neural network for training. Based on the original ResNet18 model, a new convolutional neural network, AppleNet, is constructed by adding a hybrid attention module and modifying the classifier structure. The experimental results show that the average recognition accuracy of AppleNet is 94.66%, which is 2.47% higher than that of the ResNet18 network. In addition, the training time of the model is only slightly increased. The ablation experiment further verified the effectiveness of the model modification. Compared with other advanced models in recognition accuracy and model training time, the superiority of AppleNet is confirmed. This study verifies that deep learning has great potential and application prospects in plant disease identification and provides a new technical solution for intelligent and convenient plant disease identification.

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智能农业:基于卷积神经网络的苹果叶病智能识别方法
植物病害对全球农业生产力和食品安全构成重大威胁。及早发现和准确识别这些病害对于有效的病害管理策略至关重要。传统的植物病害识别主要依靠人工观察和经验丰富的专家判断,存在费时、费力、效率低等缺点。鉴于上述问题,本研究提出了一种基于卷积神经网络的苹果叶片病害识别方法,该方法结合了混合注意力和双向长短期记忆(BiLSTM)。从多个公共数据集中选取适当的苹果叶病样本,形成实验数据集。然后,将数据集导入改进的卷积神经网络进行训练。在原有 ResNet18 模型的基础上,通过添加混合注意力模块和修改分类器结构,构建了新的卷积神经网络 AppleNet。实验结果表明,AppleNet 的平均识别准确率为 94.66%,比 ResNet18 网络高出 2.47%。此外,模型的训练时间仅略有增加。消融实验进一步验证了模型修改的有效性。与其他先进模型相比,AppleNet 在识别准确率和模型训练时间上的优势得到了证实。本研究验证了深度学习在植物病害识别中的巨大潜力和应用前景,为植物病害识别的智能化和便捷化提供了新的技术解决方案。
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来源期刊
Journal of Phytopathology
Journal of Phytopathology 生物-植物科学
CiteScore
2.90
自引率
0.00%
发文量
88
审稿时长
4-8 weeks
期刊介绍: Journal of Phytopathology publishes original and review articles on all scientific aspects of applied phytopathology in agricultural and horticultural crops. Preference is given to contributions improving our understanding of the biotic and abiotic determinants of plant diseases, including epidemics and damage potential, as a basis for innovative disease management, modelling and forecasting. This includes practical aspects and the development of methods for disease diagnosis as well as infection bioassays. Studies at the population, organism, physiological, biochemical and molecular genetic level are welcome. The journal scope comprises the pathology and epidemiology of plant diseases caused by microbial pathogens, viruses and nematodes. Accepted papers should advance our conceptual knowledge of plant diseases, rather than presenting descriptive or screening data unrelated to phytopathological mechanisms or functions. Results from unrepeated experimental conditions or data with no or inappropriate statistical processing will not be considered. Authors are encouraged to look at past issues to ensure adherence to the standards of the journal.
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