Evaluation of the influence of digital camera acquisition parameters for the accuracy of colourimetric measurements
DOI:
https://doi.org/10.21014/actaimeko.v14i2.1973Keywords:
computer vision, colourimetry, food qualityAbstract
Law 9972 of 2000 instituted the classification of vegetable products, by-products, and waste of economic value, making their classification mandatory when intended for human consumption, in purchase and sale operations, and at ports and airports when destined for export. The classification of these vegetables consists of separating the product into different categories of quality according to their peculiarities, forming homogeneous batches, as well as determining the quality based on official standards. Among the quality standards to observe in this classification process are shape, size, weight, defects in the skin, and colour, which will be the focus of this work. Currently, a great portion of this classification process is carried out manually, based on the knowledge and experience of operators. With a focus on automating this type of process, studies in the field of computer vision are being developed with the aim of autonomously reproducing the classification of fruits and vegetables usually carried out by trained operators. However, in various applications in the current literature, digital cameras are used to extract quantitative measurements. These pieces of information require metrological analyses such as any other conventional measurement system. In such sense, this paper proposes a study to evaluate the errors in the application of a digital camera for colour measurement. Different tests compared numerical aperture, exposure time, and ISO parameters with the results of a colourimeter using a colour chart as a measurand. The goal is to evaluate the repeatability and estimate the error curves of a conventional sensor when applied to colour metrology. This study is an important step in ensuring the reliability of colour measurements using digital cameras for automatic fruit and vegetable classification systems.
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Copyright (c) 2025 Pedro Costa, Artur Moura, Laisa Dias, Isadora Amorim

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