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 Ophrys 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 Ophrys 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）长期以来都是阐释植物与动物互作过程中欺骗性信号运作机制的绝佳范例。诸多研究均针对其花朵与雌性蜜蜂高度相似的结构特征展开对比分析，进而诱导雄蜂在花上产生伪交配行为。本研究借助机器学习（Machine Learning），探究自然演化的产物是否能够胜过人工智能。我们从谷歌图片库（Google Image Repository）中采集了共计2000张相关蜜蜂、胡蜂以及眉兰属（Ophrys sp.）植物的图像。随后通过人工筛选剔除不合格图像，最终保留995张图像作为有效数据集。我们将其中80%的图像用于构建基于逻辑回归（Logistic Regression）的监督学习模型，并以剩余20%的图像作为测试集评估模型精度。基于使用沃尔夫勒姆Mathematica得到的实验结果，眉兰属植物无法骗过人工智能系统。模型精度、精度基准值、平均交叉熵、受试者工作特征曲线下面积（AUC）以及混淆矩阵的分析结果均显示，该图像分类任务取得了优异表现。不过，我们可借助加速稳健特征（Speeded Up Robust Features，SURF）方法，展示图像的关键特征与视觉亮点，以及花朵结构与真实蜜蜂的相似程度。相较于传统的定性描述方法，机器学习为该研究提供了更为量化的分析路径。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分析框架，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。