Machine learning in plant science and plant breeding

Abstract

Technological developments have revolutionized measurements on plant genotypes and phenotypes, leading to routine production of large, complex data sets. This has led to increased efforts to extract meaning from these measurements and to integrate various data sets. Concurrently, machine learning has rapidly evolved and is now widely applied in science in general and in plant genotyping and phenotyping in particular. Here, we review the application of machine learning in the context of plant science and plant breeding. We focus on analyses at different phenotype levels, from biochemical to yield, and in connecting genotypes to these. In this way, we illustrate how machine learning offers a suite of methods that enable researchers to find meaningful patterns in relevant plant data.

Keywords: Artificial Intelligence; Plant Bioinformatics; Plant Biotechnology.

Figure 1

In plant sciences and plant breeding, variation at the genotype level (genomics data) is linked to phenotypic variation at various biochemical levels of organization (-omics data), at the cellular level, and at the macroscopic level at different scales Both the analysis of phenotypic measurements (sections 2 and 3) and genotype-phenotype prediction (section 4) increasingly rely on ML.

Figure. Machine learning (ML) is a subfield of the broader field of artificial intelligence. Within ML, a major distinction is between supervised and unsupervised methods. Supervised methods use labeled input data. One example is classification, in which discrete labels (green squares vs. red circles) are available, and the model learns to predict the label for new objects. The curve at the right hand side visualizes a decision boundary, which represents what the supervised model has learned. Unsupervised methods do not use labels but find groups or trends in data. One example is clustering, which in the example visualized detects two groups.

Figure 2

Overview of biochemical and cellular measurements A variety of “omics” (genomics, transcriptomics, proteomics, metabolomics) data can be measured. Machine learning is used to analyze these data at various levels and with various goals (bottom).

Figure 3

Overview of plant phenotyping systems Plants can be observed at different levels (development, growth, production) using different types of sensors and sensor systems. Machine learning plays an important role in processing the sensor data to measure traits at the various levels (red box).