Is more data better? A comparison of multi- and hyperspectral imaging in phenotyping.

Spectral imaging of vegetation for phenotyping is a fast-developing field that enables fast, objective and automated assessment of plant traits. Advances in instrumentation allow collection of ever more detailed observations using hyperspectral imaging. This technique captures the reflected light in 100+ wavelengths, compared to multispectral sensors which typically obtain 3 to 5 wavelengths. With machine learning and careful statistical analysis these data can be efficiently transformed into predictions of crop health, yield, etc. However, these instruments are costly to acquire and produce volumes of data which are expensive to handle and archive, and therefore we must ask the question whether/when the investment is worth it. In this case study, we assess the implications of using hyperspectral vs multispectral imaging when monitoring the effects of an invasive fungal pathogen on seedlings of southwestern white pine. We discuss the impacts in terms of the complexity level of the research goals. Firstly, we discuss the impact on the accuracy and timing of infection detection. Pre-symptomatic detection of infection is possible using hyperspectral. To what extent is this possible using multispectral? Next, what is the trade-off between the two spectral methods when predicting for symptom severity? And lastly, the study contains a third level of complexity, a variety in genotypes. Using hyperspectral we can successfully separate the genotypes. However, is there still a significant difference in reflectance between genotypes when using multispectral data? This study shows that the need for hyperspectral depends on the complexity of the research goal, and therefore collecting more data might not always be useful.