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Sensors 2016, 16(5), 641; doi:10.3390/s16050641

Machine Learning and Computer Vision System for Phenotype Data Acquisition and Analysis in Plants

1
DSIE, Universidad Politécnica de Cartagena, Campus Muralla del Mar, s/n. Cartagena 30202, Spain
2
Genética, Instituto de Biotecnología Vegetal, Universidad Politécnica de Cartagena, Cartagena 30202, Spain
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Gonzalo Pajares Martinsanz
Received: 3 March 2016 / Revised: 25 April 2016 / Accepted: 26 April 2016 / Published: 5 May 2016
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Spain 2015)
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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. View Full-Text
Keywords: computer vision; image segmentation; machine learning; data normalisation; circadian clock computer vision; image segmentation; machine learning; data normalisation; circadian clock
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Navarro, P.J.; Pérez, F.; Weiss, J.; Egea-Cortines, M. Machine Learning and Computer Vision System for Phenotype Data Acquisition and Analysis in Plants. Sensors 2016, 16, 641.

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