A Diverse and Interpretable Benchmark for Viti- and Vini-cultural Visual Understanding

Shengli Hu

Keywords: fine-grained visual categorization, dataset, viticulture, viniculture, geolocalization, retrieval

TL;DR: We collected four fine-grained visual categorization datasets for viticultural and vinicultural visual understanding with a diverse range of tasks and interpretable annotations informed by domain expertise, as well as baseline experimental results.

Abstract: We present four new datasets for viticultural and vinicultural visual understanding: iVineyard, iCellar, iGrapevine, and VinePathology. We designed, gathered data for, cleaned, and provided numerical and natural language annotations for these datasets in collaboration with domain experts with the aim of (1) accelerating AI adoption in the realms of viticulture and oenology; (2) improving data efficiency and interpretability with data collection, task formulation, and annotation processes informed by domain expertise; (3) benchmarking the performance of representation learning algorithms on a suite of challenging downstream viti- and vini-cultural tasks that go beyond standard species classification. We provide analyses of qualitative and quantitative results of downstream tasks including fine-grained visual categorization, fine-grained image retrieval, image geo-localization, and object discovery, thus shedding light on the strengths and weaknesses of feature representations across a diverse set of tasks that are of scientific importance to viticulturists and oenologists.