Paper: Towards Deep Viticultural Representations: Joint Region and Grape Variety Embeddings (ICLR-2024)

Reviewer comment (Reproducibility):
The sampling approach and sampling-based evaluation are not very clear. It is unclear whether each variety and region gets one embedding vector or a whole distribution, whether this distribution can be multimodal, and why figures in the Appendix show multiple points per variety/region that are very unimodal. Clarity is lacking on whether this is expected or desired.

Author response:
*Each variety and region gets a multi-normal distribution that represents it. Varieties and regions share the space in which they are projected, regions and varieties that frequently occur together are pushed together by the joining losses so their distribution should overlap in the latent space. We do expect the distributions to be quite uni-modal in the dimensionality-reduced latent space, particularly when comparing “far apart” varieties or regions.*

Reviewer comment (Reproducibility):
Many technical details are unclear, such as the value of K used for K-means clustering and the reasoning behind it, as well as the meaning of the A/B/C classes used for classification.

Author response:
*The A/B/C classes used for classification come from the EU legislation that determines which practices are lawful in certain regions and which aren’t. They use primarily climate data to assess this and therefore we make the claim that they implicitly contain terroir information (both through legislation and since they are based on climate and elevation features). We have clarified and expanded the explanation as to why we use this as the test set in the paper. Please see the discussion section 3.1, paragraph 1. For the K we set it to the number of labels, such that it corresponds to the number of countries, the number of varieties or the number of regions, whichever we are clustering to. We have clarified this in the figure labels and as a footnote (this section is now in the appendix, we explain more below).*

Reviewer comment (Evaluation):
The evaluation does not convincingly demonstrate the meaningfulness of the representations. Tables 1 and 2 only assess how clustered the representations are, without showing their significance. Table 3 shows VAEs only slightly outperforming a very simple model based on latitude and longitude, Table 4 uses only a dummy baseline, and Table 5 shows no statistically significant wine/region pairings. The only result suggesting meaningful embeddings (Table 3) is relatively weak.

Author response:
*We have adapted the SVD baseline you propose below in the new version of table 3 (table 1 now), as well as using a PCA decomposition of climate averages used in the only comparable study. As you can see we outperform these baseline much more clearly than even the previously used ones. The meaningfulness of Table 4 (now table 2) we have improved by using a new baseline based on SVD and the co-occurrence matrix as well. For Table 5 (now table 3), instead of the correlation we show the recall score in classifying varieties already grown in these regions as fitting varieties which is a more meaningful metric, in addition to the Spearman correlation significance. The non-important tables and their associated discussion we have moved to the appendix in favor of the new Qualitative Analysis section.*

Reviewer comment (Experiments):
No alternative baseline is used; it focuses on feature ablations of the proposed method.

Author response:
*Yes, we now compare with a baseline using roughly the same inputs as our model that uses an SVD approach as suggested by reviewer 4, and a global region classification based on climate data and PCA analysis from the only comparable study to our knowledge. We outperform both significantly on region classification and 6/9 models outperform SVD for variety classification. Please see section 3.1 and 3.2 for this and Tables 1 and 2 for the results.*

Reviewer comment (Experiments):
Can the authors formulate a machine learning challenge of this problem that is specialized for viticulture?

Author response:
*In the revision of this paper we have clarified the challenges that we aimed to overcome and gave a more concise and clear explanation of the state of this problem in viticulture currently. See this in the Introduction section 1.1, and the Abstract.*

Reviewer comment (Experiments):
Can the authors compare with other models?

Author response:
*Yes, we now compare with a baseline using roughly the same inputs as our model that uses an SVD approach as suggested by reviewer 4, and a global region classification based on climate data and PCA analysis from the only comparable study to our knowledge. We outperform both significantly on region classification and 6/9 models outperform SVD for variety classification. Please see section 3.1 and 3.2 for this and Tables 1 and 2 for the results.*

Reviewer comment (Novelty):
The English of the manuscript must be improved. There are problems with context transition and logical cohesion, and there are errors in the use of proper nouns. The methods and framework employed in the paper exhibit limited originality and innovation.

Author response:
*We have, during the current revision, worked to improve this and are going over the spelling and grammar until the final “printer-ready” submission deadline as well. Thank you for pointing this out.*

Reviewer comment (Presentation):
Section 1.1 extensively discusses the current research status in the field of viticulture, which is less relevant to the research of this paper.

Author response:
*We have largely removed this information and replaced it with more relevant information regarding the problem we are seeking to address. We further combined Section 1.1 and Section 1.3 into a new section called “Motivation”. It is shorter and contains only the essential points of the previously too lengthy and unspecific sections.*

Reviewer comment (Novelty):
Section 1.2 introduces the development process of embedding in detail, lacking an introduction to related technologies and methods.

Author response:
*We have adjusted this section accordingly and now focus more on the methodology rather than “general concept” of the approaches we mention.*

Reviewer comment (Presentation):
The section 1.3 on "REGION AND VARIETY EMBEDDING REQUIREMENTS" is excessively lengthy and lacks emphasis on key points. It occupies an entire page, which is excessive.

Author response:
*This section has been removed and key points have been instead mentioned in the “Motivation” section.*

Reviewer comment (Presentation):
The formulas used in this paper are not numbered.

Author response:
*Formulas are now numbered. Thank you for pointing this out.*

Reviewer comment (Writing):
There are errors in the description of variable v_i.

Author response:
*We are a bit unsure what you mean regarding the variable descriptions, we have gone over them. Please let us know whether it is still unclear.*

Reviewer comment (Experiments):
The authors should compare their methodology with existing approaches, both qualitatively and quantitatively, to demonstrate its advantages or innovations in the field.

Author response:
*We have changed the baseline that we are using to a classical approach (i.e., SVD and PCA on climate data for the tests) more similar to the one currently used in the field and are outperforming them (Table 1 and 2 and section 3.1 and 3.2). Additionally, we have added a qualitative evaluation section (section 3.3) examining the reduced latent-spaces of selected models. Lastly, we establish a new baseline for a data-based variety recommender system, which is the first of its kind, though it leaves room for improvement (section 3.4).*

Reviewer comment (Experiments):
The authors claim to contribute by overcoming the lack of detailed data, but a quantitative comparison supporting this argument is hard to find in the discussion about the effectiveness in the experiments. The authors are strongly suggested to show the strength of the article in overcoming the lack of detailed data.

Author response:
*The lack of detailed data is partially overcome by our methods as can be seen by it out-performing the climate-based PCA on climate-based region classification (see Table 1 and section 3.1). Furthermore, we explain that using a similar sampling approach used here being also applied to other modalities and terroir characteristics we can further improve our result, not by knowing how many grapes grow on which soil but by knowing which regions have primary which varieties and primarily which soil and therefore match grape varieties to soil indirectly. Which is why this approach is so important, as it demonstrates the underlying methodology for an expandable terroir embedding and grape recommendation system.*

Reviewer comment (Evaluation):
Time complexity or computation overhead analyses need to be discussed properly.

Author response:
*Time complexity is comparable to previously published research on joint VAEs, depending mainly on the loss-function calculation. We have now pointed this out in the methodology (section 2.3). For the computation requirements we included the training time and hardware in a table in the appendix for each model. Unfortunately we did not have the space to also go into more detail about this in the discussion section.*

Reviewer comment (Writing):
The writing of the paper could be improved as it is sometimes difficult to follow the paper.

Author response:
*Thank you for pointing this out, we are revising the grammar of the paper and have also re-written the abstract to be clearer. We are double checking throughout while implementing the other changes.*

Reviewer comment (Experiments):
I find the Results & Discussion section difficult to follow. There is no explicit introduction to the datasets, baselines, experimental setups and research questions.

Author response:
*The discussion and results were revised according to your and other reviewer comments. See section 3.1, 3.2 and 3.4 for the experiments and baselines.*