Recent Data Augmentation Strategies for Deep Learning in Plant Phenotyping and Their Significance

Abstract

Plant phenotyping concerns the study of plant traits resulted from their interaction with their environment. Computer vision (CV) techniques represent promising, non-invasive approaches for related tasks such as leaf counting, defining leaf area, and tracking plant growth. Between potential CV techniques, deep learning has been prevalent in the last couple of years. Such an increase in interest happened mainly due to the release of a data set containing rosette plants that defined objective metrics to benchmark solutions. This paper discusses an interesting aspect of the recent best-performing works in this field: the fact that their main contribution comes from novel data augmentation techniques, rather than model improvements. Moreover, experiments are set to highlight the significance of data augmentation practices for limited data sets with narrow distributions. This paper intends to review the ingenious techniques to generate synthetic data to augment training and display evidence of their potential importance.

Code and data availability

This is a review paper on data augmentation for plant phenotyping. It references the CVPPP dataset and the Leaf Segmentation Challenge benchmark, and notes that a synthetic dataset from prior work [13] was used in its experiments, but no authors' public URL, code deposit, or paper-specific dataset link is provided in a

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