增强玉米种子病害分类:利用 MobileNetV2 进行特征增强和迁移学习

IF 1.3 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Frontiers in Applied Mathematics and Statistics Pub Date : 2024-01-03 DOI:10.3389/fams.2023.1320177
Mohannad Alkanan, Yonis Gulzar
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引用次数: 0

摘要

在人工智能(AI)不断进步的时代,其在农业中的应用变得越来越重要。本研究探讨了如何整合人工智能对玉米病害进行鉴别分类,以满足高效农业实践的需求。这项研究利用一个综合数据集,将 21,662 张图像分为四类:破损、变色、丝状切割和纯净。所提出的模型是 MobileNetV2 的增强迭代,战略性地加入了额外的层--平均池化、扁平化、密集化、Dropout 和 softmax--以增强其特征提取能力。包括数据增强、自适应学习率、模型检查点、辍学和迁移学习在内的模型调整技术提高了模型的效率。结果表明,所提出的模型性能卓越,在四个类别中的准确率达到了约 96%。精确度、召回率和 F1 分数指标都证明了该模型的能力,精确度从 0.949 到 0.975 不等,召回率从 0.957 到 0.963 不等。在与最先进(SOTA)模型的比较分析中,所提出的模型在精确度、召回率、F1-分数和准确度方面都优于同行。值得注意的是,拟议架构的基础模型 MobileNetV2 达到了最高值,这肯定了它在准确分类玉米疾病数据集中的实例方面的优越性。这项研究不仅为农业领域日益增多的人工智能应用做出了贡献,还为玉米病害分类提出了一个新颖而有效的模型。所提出的模型性能强大,与 SOTA 模型相比具有竞争优势,因此有望成为推进精准农业和作物管理的解决方案。
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Enhanced corn seed disease classification: leveraging MobileNetV2 with feature augmentation and transfer learning
In the era of advancing artificial intelligence (AI), its application in agriculture has become increasingly pivotal. This study explores the integration of AI for the discriminative classification of corn diseases, addressing the need for efficient agricultural practices. Leveraging a comprehensive dataset, the study encompasses 21,662 images categorized into four classes: Broken, Discolored, Silk cut, and Pure. The proposed model, an enhanced iteration of MobileNetV2, strategically incorporates additional layers—Average Pooling, Flatten, Dense, Dropout, and softmax—augmenting its feature extraction capabilities. Model tuning techniques, including data augmentation, adaptive learning rate, model checkpointing, dropout, and transfer learning, fortify the model's efficiency. Results showcase the proposed model's exceptional performance, achieving an accuracy of ~96% across the four classes. Precision, recall, and F1-score metrics underscore the model's proficiency, with precision values ranging from 0.949 to 0.975 and recall values from 0.957 to 0.963. In a comparative analysis with state-of-the-art (SOTA) models, the proposed model outshines counterparts in terms of precision, recall, F1-score, and accuracy. Notably, MobileNetV2, the base model for the proposed architecture, achieves the highest values, affirming its superiority in accurately classifying instances within the corn disease dataset. This study not only contributes to the growing body of AI applications in agriculture but also presents a novel and effective model for corn disease classification. The proposed model's robust performance, combined with its competitive edge against SOTA models, positions it as a promising solution for advancing precision agriculture and crop management.
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来源期刊
Frontiers in Applied Mathematics and Statistics
Frontiers in Applied Mathematics and Statistics Mathematics-Statistics and Probability
CiteScore
1.90
自引率
7.10%
发文量
117
审稿时长
14 weeks
期刊最新文献
Third-degree B-spline collocation method for singularly perturbed time delay parabolic problem with two parameters Item response theory to discriminate COVID-19 knowledge and attitudes among university students Editorial: Justified modeling frameworks and novel interpretations of ecological and epidemiological systems Pneumonia and COVID-19 co-infection modeling with optimal control analysis Enhanced corn seed disease classification: leveraging MobileNetV2 with feature augmentation and transfer learning
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