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多种模型视觉特征嵌入和选择方法,以支持精准农业的有效害虫分类

Vikas Khullar¹, Isha Kansal¹, Shyama Barna Bhattacharjee²

¹Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India.

Scientific reports

|August 28, 2025

概括

农业5.0中的昆虫害虫识别自动化系统使用深度学习模型和线性差异分析 (LDA) 进行高效的特征选择. 这种方法在较低的计算资源下实现了高精度,有利于精准农业.

科学领域:

农业科学
计算机科学
机器学习

背景情况:

农业5.0在很大程度上依赖于作物种植,需要有效的虫害识别方法.
手动识别害虫是劳动密集型,容易出现错误,影响作物产量和质量.
人们越来越需要自动化,资源高效的远程病虫检测系统.

研究的目的:

开发一种具有高效率和低资源要求的昆虫害虫识别自动化系统.
创建一个能够处理大量害虫类别的分类模型.
与手动方法相比,提高害虫识别的速度和精度.

主要方法:

使用预训练的深度学习模型 (DenseNet201,EfficientNetB3,InceptionResNetV2) 来进行视觉特征提取.
应用线性差异分析 (LDA) 用于从组合的害虫数据集 (19类) 中有效选择特征.
部署了一个轻量级的密集神经网络进行最终分类.

主要成果:

实现了99.99%的准确性,100%的验证,以及99.99%的回忆,损失微不足道.
拟议的混合特征选择方法比传统的转移学习更有效.
该系统在减少计算和内存需求的情况下表现出高效率.

结论:

开发的系统提供了低资源,高效的多类昆虫害虫分类解决方案.
这种方法非常适合在精密农业环境中使用.
混合特征选择策略提高了分类效率,而不需要广泛的再培训.

关键词:

农作物保护功能选择害虫的分类精准农业预训练的深度学习智能农业

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