Computer vision-based device for estimating physical parameters of tomato fruits

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Abstract
Computer vision systems have been successfully applied to automate processes related to the selection and classification of food based on quality standards. In this context, this research aimed to develop a computer vision system comprising an image acquisition device and software capable of estimating the physical parameters of tomato fruits from digital images. The software was developed in Python and implemented on a Raspberry Pi microprocessor, enabling both image capture and the calculation of fruit physical parameters (volume, sphericity, and surface area). The physical parameters of 45 tomatoes from three classes (Saladette, Italian, and Cherry) were estimated using the proposed system and compared with reference values obtained through traditional methods. The comparison indices revealed a high correlation and agreement between actual and estimated values for all physical parameters when analyzing fruits across all classes. The models generated from the total set of fruits (without class distinction) achieved R2 values greater than 0.95 for all physical parameter estimates. The computer vision system enabled the measurement of tomato physical parameters, presenting approximate estimates to the reference values obtained by traditional methods. This approach represents a viable alternative for estimating the sphericity, surface area, and volume of tomato fruits.
Funding Information
  • FAPERJ (E-26/010.002732/2019)
  • FAPERJ (88887.708490/2022-00)
  • CAPES (E-26/010.002732/2019)
  • CAPES (88887.708490/2022-00)

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