Machine Learning and Computer Vision System for Phenotype Data Acquisition and Analysis in Plants.
Machine Learning and Computer Vision System for Phenotype Data Acquisition and Analysis in Plants.
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DOI：
10.3390/s16050641
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发表时间：
2016-05-05
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通讯作者：
Egea-Cortines M
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文献类型：
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作者：
Navarro PJ;Pérez F;Weiss J;Egea-Cortines M
表型组学是一种以技术为导向的获取生物系统无偏数据的有前途的方法。图像采集相对简单。然而，与抽样能力相比，数据处理和分析还不够发达。提出了一种基于机器学习算法和计算机视觉的植物材料表型数据自动分析系统。我们开发了一种能够容纳各种大小物种的生长室。夜间图像采集需要近红外闪电。对于ML过程，我们测试了三种不同的算法：K-近邻(KNN)、朴素贝叶斯分类器(NBC)和支持向量机。每个ML算法使用不同的核函数执行，并使用原始数据和两种类型的数据归一化进行训练。通过计算不同的指标来确定机器学习算法的最优配置。对于RGB图像，我们在KNN上获得了99.31%的性能，在支持向量机上获得了99.34%的NIR性能。我们的结果表明，ML技术可以加快现象数据的分析速度。此外，RGB和NIR图像都可以成功分割，但可能需要不同的ML算法进行分割。
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.