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深度数据:基于鱼优化和人工神经网络的深度融合模型,用于阿拉伯日期分类

Nour Eldeen Mahmoud Khalifa¹, Jiaji Wang², Mohamed Hamed N Taha¹

¹Information Technology Department, Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt.

|July 30, 2024

概括

一个新的深度学习模型,DeepDate,准确地分类阿拉伯日果,准确率为95.9%. 这种由人工智能驱动的图像识别提高了农业部门对于枣果生产的效率.

科学领域:

农业技术农业技术
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

日期水果生产规模不断扩大,需要有效的分类方法.
传统的分类方法与大量和多种多样的物种作斗争.
图像识别为自动化枣果识别提供了一个潜在的解决方案.

研究的目的:

开发和评估一个深度学习模型来对阿拉伯枣果物种进行分类.
为了提高日果分类的准确性和效率.
为应对农业部门产量增加所带来的挑战.

主要方法:

提出了一个深度融合模型 (DeepDate),结合了鱼优化和人工神经网络.
利用了五个阿拉伯日果图像类的数据集:巴希,哈拉斯,梅内伊菲,纳布特·塞夫和苏拉杰.
采用了三个阶段的过程:特征提取,特征选择和模型培训/测试.

主要成果:

深度数据模型实现了最高的测试准确率95.9%.
与既有模型相比,DeepDate在分类准确性和时间消耗之间取得了更好的平衡.
超过了几种深度转移学习模型,包括Alexnet,Squeezenet,Googlenet,VGG-19,NasNet和Inception-V3.3等.

结论:

深度数据显著提高了日果分类的准确性和效率.
该模型为农业行业提供了一个有前途的AI驱动解决方案.
建议包括技术转让和合作,以提高枣果生产的可持续性和生产率.

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