Can plants fool artificial intelligence? Using machine learning to compare between bee orchids and bees. (3rd October 2021)

• Record Type: Journal Article
• Authors: Fadzly, Nik; Zuharah, Wan Fatma; Jenn Ney, Jenny Wong
• Is Part Of: Plant signaling & behavior. Volume 16:Number 10(2021)
• Publication Date: 2021-10-03
• Subjects: Bee orchids -- bees -- mimicry -- pseudo-copulation -- machine learning -- artificial intelligence; Plant ecophysiology -- Periodicals; Plant cellular signal transduction -- Periodicals; Plant cellular signal transduction; Plant ecophysiology; Periodicals; Electronic journals; 581
• DOI: 10.1080/15592324.2021.1935605
• Languages: English
• ISSNs: 1559-2316

Abstract

ABSTRACT: 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.