用叶脉图案识别植物种类，EfficientNetB0表现最佳。
Transfer Learning-Based CNN Models for Plant Species Identification Using Leaf Venation Patterns
- 用ResNet50、MobileNetV2和EfficientNetB0分析叶脉图像
- EfficientNetB0测试准确率达94.67%，各项指标超94.6%
- 适合需要高精度的植物分类自动化系统
本研究评估了三种深度学习架构——ResNet50、MobileNetV2和EfficientNetB0——在基于叶脉图案的植物物种自动分类中的有效性，该特征具有重要的分类学意义。实验使用瑞典叶部数据集（包含15个物种，每物种75张图像，共1,125张），通过标准性能指标评估模型在训练与测试阶段的表现。ResNet50训练准确率为94.11%，但出现过拟合，测试准确率降至88.45%，F1分数为87.82%。MobileNetV2展现出更好的泛化能力，测试准确率达93.34%，F1分数为93.23%，适用于轻量化实时应用。EfficientNetB0优于前两者，测试准确率达到94.67%，精确率、召回率和F1分数均超过94.6%，表明其在基于叶脉的分类任务中具有更强鲁棒性。结果证明，尤其是EfficientNetB0，可为基于叶脉特征的自动化植物分类提供高效、可扩展的工具。
原文摘要 · Abstract (English)
This study evaluates the efficacy of three deep learning architectures: ResNet50, MobileNetV2, and EfficientNetB0 for automated plant species classification based on leaf venation patterns, a critical morphological feature with high taxonomic relevance. Using the Swedish Leaf Dataset comprising images from 15 distinct species (75 images per species, totalling 1,125 images), the models were demonstrated using standard performance metrics during training and testing phases. ResNet50 achieved a training accuracy of 94.11% but exhibited overfitting, reflected by a reduced testing accuracy of 88.45% and an F1 score of 87.82%. MobileNetV2 demonstrated better generalization capabilities, attaining a testing accuracy of 93.34% and an F1 score of 93.23%, indicating its suitability for lightweight, real-time applications. EfficientNetB0 outperformed both models, achieving a testing accuracy of 94.67% with precision, recall, and F1 scores exceeding 94.6%, highlighting its robustness in venation-based classification. The findings underscore the potential of deep learning, particularly EfficientNetB0, in developing scalable and accurate tools for automated plant taxonomy using venation traits.
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