Can plants fool artificial intelligence? Using machine learning to compare between bee orchids and bees

Bee orchids have long been an excellent example of how dishonest signal works in plant-animal interaction. Many studies compared the flower structures that resemble female bees, leading toward pseudo-copulation of the male bees on the flower. Using Machine Learning, we tested whether nature is capable of besting artificial intelligence. A total of 2000 images of related bees, wasps, and <i>Ophrys</i> sp. were collected from the Google Image Repository. Unsuitable images were later filtered out manually, leaving a total of 995 images in the final selection. 80% of these images were used to build a supervised model using Logistic Regression, while the model accuracy was tested using 20% of the remaining images. Based on our results using Wolfram Mathematica, the <i>Ophrys</i> is not capable of fooling artificial intelligence. The accuracy, accuracy baseline, mean cross-entropy, Area Under ROC (receiver operating characteristic curve) curve (AUC) and the confusion matrix gave excellent image classification. However, we can now show the key points and highlights of the images and how the structures closely resemble actual bees using the SURF method. Rather than just a descriptive method, ML learning has enabled a more quantitative approach. Since this is a simple test, we encourage other scientists to adopt our approach using a larger dataset and better database samples.

蜂兰（Bee orchids）长期以来都是植物-动物互作领域中不诚实信号机制的绝佳研究范例。诸多研究均针对其模拟雌性蜜蜂的花部结构展开，借此诱导雄性蜜蜂在花部完成伪交配行为。本研究借助机器学习（Machine Learning）检验自然演化是否能够超越人工智能。研究团队从谷歌图片库（Google Image Repository）中共收集了2000张相关蜜蜂、胡蜂以及眉兰属（Ophrys）物种的图像。随后经人工筛选剔除不合格图像，最终保留995张有效图像。研究使用其中80%的图像构建基于逻辑回归（Logistic Regression）的监督学习模型，并以剩余20%的图像测试模型精度。基于使用沃尔夫勒姆数学软件（Wolfram Mathematica）得到的分析结果，眉兰属植物并未能骗过人工智能。模型精度、精度基准、平均交叉熵、ROC（受试者工作特征曲线，Receiver Operating Characteristic Curve）曲线下面积（AUC）以及混淆矩阵等指标均展现出优异的图像分类性能。不过，本研究可借助SURF（加速稳健特征，Speeded Up Robust Features）算法呈现图像的关键特征与高亮区域，并展示其花部结构与真实蜜蜂的相似程度。相较于仅能开展描述性分析的传统方法，机器学习实现了更为量化的研究路径。鉴于本研究仅为初步验证性实验，我们呼吁其他科研人员采用本研究的方法，使用更大规模的数据集与更优质的数据库样本开展相关研究。