Machine learning classification of plant genotypes grown under different light conditions through the integration of multi-scale time-series data.

Abstract

In order to mitigate the effects of a changing climate, agriculture requires more effective evaluation, selection, and production of crop cultivars in order to accelerate genotype-to-phenotype connections and the selection of beneficial traits. Critically, plant growth and development are highly dependent on sunlight, with light energy providing plants with the energy required to photosynthesize as well as a means to directly intersect with the environment in order to develop. In plant analyses, machine learning and deep learning techniques have a proven ability to learn plant growth patterns, including detection of disease, plant stress, and growth using a variety of image data. To date, however, studies have not assessed machine learning and deep learning algorithms for their ability to differentiate a large cohort of genotypes grown under several growth conditions using time-series data automatically acquired across multiple scales (daily and developmentally). Here, we extensively evaluate a wide range of machine learning and deep learning algorithms for their ability to differentiate 17 well-characterized photoreceptor deficient genotypes differing in their light detection capabilities grown under several different light conditions. Using algorithm performance measurements of precision, recall, F1-Score, and accuracy, we find that Suport Vector Machine (SVM) maintains the greatest classification accuracy, while a combined ConvLSTM2D deep learning model produces the best genotype classification results across the different growth conditions. Our successful integration of time-series growth data across multiple scales, genotypes and growth conditions sets a new foundational baseline from which more complex plant science traits can be assessed for genotype-to-phenotype connections.

Plant phenotyping relevance

植物の時系列成長データを用いた遺伝子型分類について、複数の機械学習・深層学習手法を比較評価しており、表現型データの自動取得・解析ワークフローが研究の中心です。

Code and data availability

The paper's authors explicitly state that all code implementing their ML/DL genotype-classification models is publicly available on GitHub at the authors' repository URL. PlantCV is a generic third-party library, not a paper-specific asset, and no separate phenotype dataset deposit is described beyond this repository.