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Aggregates Classification

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...

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GradCAM-PestDetNet:一个基于深度学习的混合模型,具有可解释的AI,用于病虫检测和分类.

Ramitha Vimala¹, Saharsh Mehrotra¹, Satish Kumar^1,2

¹Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, Maharashtra, India.

|August 13, 2025

概括

本研究介绍了GradCAM-PestDetNet,这是一种使用深度转移学习模型有效检测害虫的AI系统. 它实现了提高准确性和可解释性,这对于农业和生态监测至关重要.

关键词:

注意力机制注意力机制卷积神经网络是一个卷积神经网络.组合模型模型组合模型可解释的人工智能虫害检测虫害检测虫害检测虫害检测转移学习转移学习

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科学领域:

农业科学农业科学
计算机科学计算机科学
生态生态学生态生态学

背景情况:

害虫检测对于粮食安全,农业生产力和经济发展至关重要.
传统的害虫检测方法往往是缓慢的,不准确的,需要专家知识.
人工智能和计算机视觉方面的进步为更有效的害虫检测系统提供了潜力.

研究的目的:

使用深度转移学习模型开发一个高效和可解释的害虫检测系统.
评估各种对象检测和转移学习模型的性能,以识别害虫.
通过梯度加权类激活映射 (Grad-CAM) 增强模型的解释性.

主要方法:

使用了对象检测模型 (YOLOv8n,YOLOv8s,YOLOv8m) 和转移学习技术 (VGG16,ResNet50,EfficientNetB0,MobileNetV2,InceptionV3,DenseNet121) 的应用.
探索视觉变压器 (ViT) 和游泳变压器,用于复杂的图案处理.
集成的Grad-CAM可用于可视化模型预测和改善可解释性.

主要成果:

YOLOv8n模型提供了最快的推理速度1.86 ms/img,适合低资源环境.
一个整体模型 (ResNet50,DenseNet,MobileNet) 实现了67.07%的准确性,66.3%的F1分数和68.1%的回忆.
这比基线CNN的21.5%准确率有了显著的改进,表明了更普遍和更强大的模型.

结论:

GradCAM-PestDetNet提供了一个可行的和可解释的解决方案,用于自动检测害虫.
深度转移学习和Grad-CAM的整合提高了检测准确性和模型透明度.
这种人工智能驱动的方法支持农业和生态研究中的有效害虫管理.