Machine Learning for Plant Phenotyping Needs Image Processing

We found the article by Singh et al. [1] extremely interesting because it introduces and showcases the utility of machine learning for high-throughput data-driven plant phenotyping. With this letter we aim to emphasize the role that image analysis and processing have in the phenotyping pipeline beyond what is suggested in [1], both in analyzing phenotyping data (e.g., to measure growth) and when providing effective feature extraction to be used by machine learning. Key recent reviews have shown that it is image analysis itself (what the authors of [1] consider as part of pre-processing) that has brought a renaissance in phenotyping [2]. At the same time, the lack of robust methods to analyze these images is now the new bottleneck 3, 4 and 5 – and this bottleneck is not easy to overcome. As the following aims to illustrate, it is coupled not only to the imaging system and the environment but also to the analytical task at hand, and requires new skills to help deal with the challenges introduced.A successful high-throughput image-based phenotyping system starts with the imaging approach itself. The choices are to image many plants simultaneously or one plant at a time, requiring movable systems to bring the plant to the camera or vice versa. These systems add cost but have the benefit of isolating the object of interest. In turn, this simplifies its processing, for example facilitating object segmentation, in other words the image analysis process isolating the plant from background (e.g., soil), as Figure 1A shows (many image-processing tasks are related to how we perceive and analyze an object of interest, such as segmentation, detection, tracking, and many others)