title: Machine Learning and Computer Vision System for Phenotype Data Acquisition and Analysis in Plants.
abstract: Phenomics is a technology-driven approach with promising future to obtain unbiased data of biological systems. Image acquisition is relatively simple. However data handling and analysis are not as developed compared to the sampling capacities. We present a system based on machine learning (ML) algorithms and computer vision intended to solve the automatic phenotype data analysis in plant material. We developed a growth-chamber able to accommodate species of various sizes. Night image acquisition requires near infrared lightning. For the ML process, we tested three different algorithms: k-nearest neighbour (kNN), Naive Bayes Classifier (NBC), and Support Vector Machine. Each ML algorithm was executed with different kernel functions and they were trained with raw data and two types of data normalisation. Different metrics were computed to determine the optimal configuration of the machine learning algorithms. We obtained a performance of 99.31% in kNN for RGB images and a 99.34% in SVM for NIR. Our results show that ML techniques can speed up phenomic data analysis. Furthermore, both RGB and NIR images can be segmented successfully but may require different ML algorithms for segmentation.
authors.affiliation: DSIE, Universidad Politécnica de Cartagena, Campus Muralla del Mar, s/n. Cartagena 30202, Spain. pedroj.navrro@upct.es.
authors.forename: Pedro J
authors.initials: PJ
authors.lastname: Navarro
authors.affiliation: Genética, Instituto de Biotecnología Vegetal, Universidad Politécnica de Cartagena, Cartagena 30202, Spain. fernando.perez8@um.es.
authors.forename: Fernando
authors.initials: F
authors.lastname: Pérez
authors.affiliation: Genética, Instituto de Biotecnología Vegetal, Universidad Politécnica de Cartagena, Cartagena 30202, Spain. Julia.weiss@upct.es.
authors.forename: Julia
authors.initials: J
authors.lastname: Weiss
authors.affiliation: Genética, Instituto de Biotecnología Vegetal, Universidad Politécnica de Cartagena, Cartagena 30202, Spain. marcos.egea@upct.es.
authors.forename: Marcos
authors.initials: M
authors.lastname: Egea-Cortines
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D010641:Phenotype; D010944:Plants; D060388:Support Vector Machine
keywords: circadian clock; computer vision; data normalisation; image segmentation; machine learning