  In this paper, multiple instance learning (MIL) algorithms to automatically
perform root detection and segmentation in minirhizotron imagery using only
image-level labels are proposed. Root and soil characteristics vary from
location to location, thus, supervised machine learning approaches that are
trained with local data provide the best ability to identify and segment roots
in minirhizotron imagery. However, labeling roots for training data (or
otherwise) is an extremely tedious and time-consuming task. This paper aims to
address this problem by labeling data at the image level (rather than the
individual root or root pixel level) and train algorithms to perform individual
root pixel level segmentation using MIL strategies. Three MIL methods (multiple
instance adaptive cosine coherence estimator, multiple instance support vector
machine, multiple instance learning with randomized trees) were applied to root
detection and compared to non-MIL approches. The results show that MIL methods
improve root segmentation in challenging minirhizotron imagery and reduce the
labeling burden. In our results, multiple instance support vector machine
outperformed other methods. The multiple instance adaptive cosine coherence
estimator algorithm was a close second with an added advantage that it learned
an interpretable root signature which identified the traits used to distinguish
roots from soil and did not require parameter selection.
