Machine Learning Methods to Analyze Arabidopsis Thaliana Plant Root Growth

TL;DR

This study applies advanced machine learning techniques, including SVM kernels and neural network ensembles, to classify Arabidopsis Thaliana plant root growth, improving accuracy and efficiency over traditional phenotypic analysis methods.

Contribution

It introduces a modified feature extraction method using velocity and acceleration, and evaluates hybrid neural network systems and SVM kernels for plant classification.

Findings

MNCE and GNCL improve classifier efficiency

SVM kernels outperform neural network ensembles in accuracy

Kernel methods require less classification time

Abstract

One of the challenging problems in biology is to classify plants based on their reaction on genetic mutation. Arabidopsis Thaliana is a plant that is so interesting, because its genetic structure has some similarities with that of human beings. Biologists classify the type of this plant to mutated and not mutated (wild) types. Phenotypic analysis of these types is a time-consuming and costly effort by individuals. In this paper, we propose a modified feature extraction step by using velocity and acceleration of root growth. In the second step, for plant classification, we employed different Support Vector Machine (SVM) kernels and two hybrid systems of neural networks. Gated Negative Correlation Learning (GNCL) and Mixture of Negatively Correlated Experts (MNCE) are two ensemble methods based on complementary feature of classical classifiers; Mixture of Expert (ME) and Negative Correlation Learning (NCL). The hybrid systems conserve of advantages and decrease the effects of disadvantages of NCL and ME. Our Experimental shows that MNCE and GNCL improve the efficiency of classical classifiers, however, some SVM kernels function has better performance than classifiers based on neural network ensemble method. Moreover, kernels consume less time to obtain a classification rate.