Iulia Floristeanu,  Cindy Chan , Steven Burgess (0000-0003-2353-7794), Charles Pignon, Stephanie Cullum, Isla Causon, Pietro Hughes
Abstract
In the preprint "StomataCounter: a deep learning method applied to automatic stomatal identification and counting” (doi: https://doi.org/10.1101/327494) Fetter et al. introduced a reliable and automated stomata counting program that is more efficient and accurate than human counting and existing algorithms, with a low false positive rate. The authors report the algorithm can be used for previous uncharacterised species and has a 94.2% transfer accuracy when used on untrained datasets. In addition they provide a publically available webtool which could be very beneficial for researchers working on stomata.
Review
We really enjoyed the paper and found it to be of high interest as the method presented could make the work of a lot of people easier. We were particularly impressed with the precision score of StomataCounter which compares well with other existing algorithms, especially in terms of the transfer accuracy. To our knowledge this is a novel approach, as there are no automated stomata counting technologies available, and it outperforms existing methods. The DCCN appears to overcomes challenges of stomata counting and it correctly identified stomata showing minimal false positives on non-plant and non-stomata covered tissue.
The article was well written and easy to follow. We liked the choice of a Deep Convolutional Neural Network (DCNN) for machine learning and felt the authors could have highlighted the benefits of this method by providing a more detailed justification of why it is superior to other algorithms - as this is what made the paper so interesting to us. The text might be further improved by being more specific about results in the abstract and a clearer statement on supplementary data and sampling used.
We were interested to know how the algorithm performs on grass species, as they have “dumbbell-shaped” guard cells and companion cells. It was unclear to us whether grass stomata have been used among the training images so we wondered if the accuracy would be the same for this particular shape of stomata. Including some text in the discussion about this would be illuminating. In addition for the sake of reproducibility and to aid readers comprehension it would be useful to provide (1) a complete list of samples analysed and (2) ideally the whole training dataset in a public repository such as Zendo or Dryad, as it could greatly benefit future comparative studies
Questions we had it would help to clarify
Minor comments