基于多图像特征定位和自适应过的融合化的害虫识别
Yanan Chen1, Miao Chen1, Minghui Guo1,2
1National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China.
Frontiers in plant science
|December 4, 2023
概括
这项研究引入了一种多图像融合方法,用于准确识别害虫,改进了单图像限制. 这种方法提高了实际农业应用的虫害识别准确度.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 精确的虫害识别对于有效的虫害防治至关重要,但由于外观变化,数据质量和复杂的环境而面临挑战.
- 现有的单图像识别模型在准确性方面扎,IP102数据集的最高准确率仅达到75%.
研究的目的:
- 提出和评估一种新的多图像融合识别方法,以提高在实际农业环境中害虫识别的准确性.
- 为了提高农民对同一害虫的多张图像的轻松访问,以提高识别能力.
主要方法:
- 使用卷积神经网络 (CNN) 来从同一害虫的多个图像中提取特征地图.
- 采用有效的特征定位模块 (EFLM) 来识别和定位害虫特征.
- 集成了自适应过融合模块 (AFFM) 与特征选择和融合的注意力机制,随后是软投票 (SV) 分类器进行最终分类.
主要成果:
- 提出的方法实现了高的识别精度:73.9%的IP102,99.8%的D0,和99.7%的ETP使用单个图像.
- 多图像融合显著提高了精度,达到最先进的水平:IP102上的96.1% (5图像),D0上的100% (2图像) 和ETP上的100% (2图像).
- 开发的网络应用程序展示了农民的实际应用,有助于可靠的害虫识别.
结论:
- 多图像融合方法有效地克服了单图像虫害识别的局限性.
- 该方法的高精度和实际应用性支持智能农业和农民援助的发展.
- 集成EFLM,AFFM和SV模块有助于融合模型的卓越性能.
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