Abstract
Phenotyping involves the quantitative assessment of the anatomical, biochemical, and physiological plant traits. Natural plant growth cycles can be extremely slow, hindering the experimental processes of phenotyping. Deep learning offers a great deal of support for automating and addressing key plant phenotyping research issues. Machine learning-based high-throughput phenotyping is a potential solution to the phenotyping bottleneck, promising to accelerate the experimental cycles within phenomic research. This research presents a study of deep networks’ potential to predict plants’ expected growth, by generating segmentation masks of root and shoot systems into the future. We adapt an existing generative adversarial predictive network into this new domain. The results show an efficient plant leaf and root segmentation network that provides predictive segmentation of what a leaf and root system will look like at a future time, based on time-series data of plant growth. We present benchmark results on two public datasets of Arabidopsis (A. thaliana) and Brassica rapa (Komatsuna) plants. The experimental results show strong performance, and the capability of proposed methods to match expert annotation. The proposed method is highly adaptable, trainable (transfer learning/domain adaptation) on different plant species and mutations.
Highlights
Plant phenotyping is defined by Li et al [1] as the assessment of complex plant traits growth, resistance, architecture, physiology, ecology, and the essential measurement of individual quantitative parameters
It is commonly argued that deep learning methods for image segmentation, feature extraction, and data analysis are the key for progress in image-based high-throughput plant phenotyping [3]
To assess whether the Generative Adversarial Networks (GANs) model forecast was biologically accurate and produced meaningful images rather than merely look plausible from human observation, the average percentage of leaf growth over given sequences was quantitatively calculated by measuring the number of increased pixels of each predicted frame at different time step compared to a baseline of the last frame of the input sequence
Summary
Plant phenotyping is defined by Li et al [1] as the assessment of complex plant traits growth, resistance, architecture, physiology, ecology, and the essential measurement of individual quantitative parameters. Image-based phenotyping has been proposed as a solution to this bottleneck, as it has shown great potential in increasing the scale, throughput, efficiency, and speed of phenomic research. Several deep learning-based approaches have appeared that are available to measure different plant traits efficiently and power genetic discovery [3,6,7]. Such approaches have exhibited improved performance when compared with traditional image-based phenotyping approaches, and biologists are relying more and more on these outstanding results to capture complex features and structures of plants. Deep learning refers to a group of statistical machine learning techniques used to learn feature hierarchies [30]—such methods demonstrate outstanding potential for noninvasive studies, such as image-based phenotyping
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