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Updated: Sep 17, 2025

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走向精准农业:用于增强害虫识别的元启发模型压缩

Sana Parez¹, Norah Saleh Alghamdi², Tahir Mahmood³

¹Department of Software, Sejong University, Seoul, 05006, South Korea.

Scientific reports

|July 2, 2025

概括

这项研究引入了一种有效的深度学习框架,用于识别作物害虫和疾病. 这种新的方法提高了准确性,并减少了实际农业应用的计算负载.

科学领域:

农业科学农业科学
计算机科学计算机科学
人工智能的人工智能

背景情况:

农作物疾病和害虫导致农业产量大幅下降.
传统的害虫识别方法是劳动密集型的,缺乏稳定性.
现有的深度学习模型用于虫害识别,其计算要求很高,规模也很大.

研究的目的:

开发一种新的,高效的深度学习框架,用于准确识别农作物害虫和疾病.
提高农业深度学习模型的计算效率和可行性.

主要方法:

使用InceptionV3作为功能提取的骨干.
整合了一个道注意力 (CA) 机制,用于功能改进.
采用了一个元启发式优化算法来减少模型复杂性和计算开销.

主要成果:

拟议的模型在CropDP-181数据集上实现了高性能.
与最先进的方法相比,证明了更高的分类准确性和计算效率.
获得了0.932的精度,0.891的回忆,0.911的F1得分,88.50%的精度和0.816.81%的MCC.

结论:

开发的深度学习框架对于准确识别农作物害虫和疾病是有效的.
该模型显示了实时农业监测系统的重大实际潜力.
该方法提供了高性能和计算效率之间的平衡.

关键词:

卷积神经网络是一个卷积神经网络.深度学习是一种深度学习.疾病识别疾病识别机器学习机器学习虫害检测虫害检测虫害检测虫害检测精准农业精准农业精准农业

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