Authors:
Andrea Caraffa
1
;
Michele Ricci
2
;
3
;
Michela Lecca
1
;
Carla Maria Modena
1
;
Eugenio Aprea
2
;
3
;
Flavia Gasperi
2
;
3
and
Stefano Messelodi
1
Affiliations:
1
Bruno Kessler Foundation, Digital Industry, Via Sommarive 14, 38123 Trento, Italy
;
2
Edmund Mach Foundation, Research and Innovation Centre, Via E. Mach 1, 38098 San Michele all’Adige (TN), Italy
;
3
University of Trento, Center Agriculture Food Environment, Via E. Mach 1, 38098 San Michele all’Adige (TN), Italy
Keyword(s):
Food Quality Assessment, Cheese Rind Thickness, Machine Learning, Regression.
Abstract:
Checking food quality is crucial in food production and its commercialization. In this context, the analysis of macroscopic visual properties of the food, like shape, color, and texture, plays an important role as a first assessment of the food quality. Currently, such an analysis is mostly performed by experts, who observe, smell, taste the food, and judge it based on their training and experience. Such an assessment is usually time-consuming and expensive, so it is of great interest to support it with automated and objective computer vision tools. In this paper, we present a deep learning method to estimate the rind thickness of Trentingrana cheese from color images acquired in a controlled environment. Rind thickness is very important for the commercial selection of this cheese and is commonly considered to evaluate its quality, together with other sensory features. We tested our method on 90 images of cheese slices, where the ground-truth rind thickness was defined using the meas
ures provided by a panel of 12 experts. Our method achieved a Mean Absolute Error (MAE) of ≈ 0.5 mm, which is half the ≈ 1.2 mm error produced on average by the experts compared to the defined ground-truth.
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