用多尺度特征融合提升小麦病害分类准确率至99.75%。
A Multi-Scale Feature Extraction and Fusion Deep Learning Method for Classification of Wheat Diseases
- 结合多尺度特征提取与图像分割，集成Xception等模型
- 在2020年小麦病害数据集上达99.75%准确率
- 适合农业智能诊断与病害识别研究者参考
小麦是重要的膳食纤维和蛋白质来源，但其生长受多种病害威胁。本文聚焦小麦散黑穗病、叶锈病和根茎腐烂病的识别难题，提出一种融合多尺度特征提取与先进图像分割技术的新方法。通过在2020年小麦病害分类数据集上训练Xception、Inception V3和ResNet 50等神经网络模型，并结合投票与堆叠等机器视觉分类器进行集成学习。实验表明，该方法分类准确率达到99.75%，显著优于现有主流方法。其中，Xception深度学习集成模型表现最佳。
原文摘要 · Abstract (English)
Wheat is an important source of dietary fiber and protein that is negatively impacted by a number of risks to its growth. The difficulty of identifying and classifying wheat diseases is discussed with an emphasis on wheat loose smut, leaf rust, and crown and root rot. Addressing conditions like crown and root rot, this study introduces an innovative approach that integrates multi-scale feature extraction with advanced image segmentation techniques to enhance classification accuracy. The proposed method uses neural network models Xception, Inception V3, and ResNet 50 to train on a large wheat disease classification dataset 2020 in conjunction with an ensemble of machine vision classifiers, including voting and stacking. The study shows that the suggested methodology has a superior accuracy of 99.75% in the classification of wheat diseases when compared to current state-of-the-art approaches. A deep learning ensemble model Xception showed the highest accuracy.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。