Abstract
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.
Abstract (translated)
这项研究评估了三种深度学习架构（ResNet50、MobileNetV2和EfficientNetB0）在基于叶片脉络模式的植物种类自动分类中的有效性。叶脉是一种具有重要分类学意义的关键形态特征。使用包含来自15种不同物种图像的数据集（每个物种75张图片，总计1,125张图片），瑞典叶数据集，在训练和测试阶段分别通过标准性能指标展示了这些模型的性能。 ResNet50在训练过程中达到了94.11%的准确率，但表现出了过拟合的现象，这反映在其较低的测试准确率为88.45%，F1分数为87.82%。MobileNetV2则显示了更好的泛化能力，其测试准确率为93.34%，F1得分为93.23%，表明该模型适合轻量级和实时应用。EfficientNetB0在所有模型中表现最佳，在测试阶段达到了94.67%的准确率，并且精度、召回率以及F1分数都超过了94.6%，这说明其在基于脉络分类的应用中具有较强的鲁棒性。 研究结果强调了深度学习，尤其是EfficientNetB0，在使用叶脉特征开发可扩展和精确的自动化植物分类工具方面的潜力。
URL
https://arxiv.org/abs/2509.03729
