Paper Info
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.
Year
DOI
Venue
2016
10.3390/s16050641
SENSORS
Keywords
Field
DocType
computer vision,image segmentation,machine learning,data normalisation,circadian clock
Computer vision,Naive Bayes classifier,Segmentation,Computer science,Support vector machine,Data acquisition,Image segmentation,Data type,Artificial intelligence,Group method of data handling,Machine learning,Kernel (statistics)
Journal
Volume
Issue
ISSN
16
5.0
1424-8220
Citations 
PageRank 
References 
1
0.37
10
Authors
4
Name
Order
Citations
PageRank
P. j. Navarro1374.24
Fernando Pérez2101.62
Julia Weiss3121.27
Marcos Egea-Cortines4121.61