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在Cordia Dichotoma中使用DeepSVM模型对圆顶胆的解剖学研究和早期诊断

Said Khalid Shah¹, Mazliham Bin Mohd Su'ud², Aurangzeb Khan¹

¹Department of Computer Science, University of Science and Technology, Bannu, Khyber Pakhtunkhwa, Pakistan.

Frontiers in artificial intelligence

|January 21, 2026

概括

一个新的DeepSVM模型使用人工智能 (AI) 改进了植物疾病检测. 这种混合方法增强了模型的稳定性和通用性,用于准确地早期识别疾病,如Cordia二分法中的圆顶胆.

科学领域:

农业科学农业科学
计算机科学计算机科学
植物病理学植物病理学

背景情况:

人工智能 (AI) 和深度学习 (DL) 对自动植物疾病检测有希望,以提高作物产量.
训练人工智能模型的挑战包括过度拟合和有限的概括,特别是在真实世界的现场数据和传统的卷积神经网络 (CNN) 中.
在特定领域的叶子数据集上进行转移学习也可能导致训练不稳定.

研究的目的:

开发和评估一个强大的人工智能模型来检测圆顶胆,一种影响Cordia二分法叶的疾病.
为了解决传统CNN的局限性,并将学习转移到现实现场条件中.
为了制定一个对健康与疾病的二元分类任务,Cordia二分法使用自定义数据集.

主要方法:

通过将支持矢量机 (SVM) 与修改后的ResNet-50 CNN集成,开发了一个混合的DeepSVM架构.
使用L2调节和用SVM替换西格激活层,以减轻过度拟合和稳定训练.
使用图像预处理和增强技术来增强数据的可变性并防止过拟合.

主要成果:

在400张图像的测试组中,DeepSVM模型实现了高性能,精度为94.50%,F1得分为94.47%.
该模型在重复运行中表现出稳定的性能,表明了强度.
在检测Cordia二分法叶病方面,DeepSVM的表现优于VGG-16,InceptionResNetv2和MobileNet-V2等既有模型.

结论:

与传统的CNN分类器相比,提出的DeepSVM方法提供了优越的概括性和训练稳定性.
这种混合模型显示了在精密农业中有效的自动化疾病监测的潜力.
这些发现有助于推进人工智能用于可持续作物管理和产量改善的应用.

关键词:

皮带二分化症是指皮带二分化症.在DeepSVM中使用.复网-50 复网-50 复网-50 复网-50 复网在SVM中,SVM是SVM.这是分类分类的分类.圆顶胆圆顶胆圆顶胆微调微调的微调.转移学习转移学习

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