Non-Destructive Post-Harvest Tomato Mass Estimation Model Based on Its Area via Computer Vision and Error Minimization Approaches

Abstract

Tomato commercialization in Mexican and Latin-American markets is economically affected by three main physical aspects of the fruit: ripening time, size, and mass. Digital image processing combined with mathematical models and machine learning approaches allows the development of prediction models to minimize fruit waste, among other applications. Particularly crossed validation, linear and non-linear adjustment by quadratic mean least error approximation, and digital image processing are used to obtain a post-harvest mass loss estimation model based upon the fruit’s area. A database for fruit characterization of 97,200 images and mass (kg) and area (cm2) measurement entries over a continuous post-harvest timeline of 54 days was considered in the methodology. Results from the linear (polynomial) adjustment model presented an efficiency of 94.65%, while the non-linear (exponential and potential) adjustment models gave in their turn efficiencies of 99.21 and 99.82%, respectively. It was concluded that the best mass loss estimation model was the potential adjustment one, with an approximation error of just 0.18% between actual and estimated data.

Keywords

• Agricultural engineering
• digital images
• estimation error
• linear approximation
• machine learning
• prediction models