Modeling of Orange Mass Based on Dimensions

There are instances in which it is desirable to determine relationships among fruit physical attributes. For example, fruits are often graded on the basis of size and projected area, but it may be more suitable and/or economical to develop a machine which grades by mass. Therefore, a relationship between mass and dimensions or projected areas and/ or volume of fruits is needed. Various grading systems, size fruits on the basis of specific parameters. Sizing parameter depends on fruit and machine characteristics.Models for predicting mass of orange from its dimensions and projected areas were identified. Models were divided into three classifications: 1- Single and multiple variable regression of orange dimensions (1st classification). 2- Single and multiple variable regression of projected areas (2nd classification). 3- Estimation of orange shape; ellipsoid or spheroid based on volume (3rd classification). Ten Iranian varieties of oranges were selected for the study. 3rd classification models had the highest performance followed by 2nd and 1st classifications respectively, with R2close to unity. The 2nd classification models need electronic systems with cameras for projection whereas, 1st classification models are used in the simple mechanical systems, except multiple variable ones, of and 3rd classification models need more complex mechanical systems. Among the systems that sorted oranges based on one dimension (Model 2), system that applies intermediate diameter suited better with nonlinear relationship as: M = 0.07b2 – 2.95 b + 39.15 with R2= 0.97.

Modeling of Orange Mass Based on Dimensions Keywords:

Dimensions , Mass models , Orange , Sorting , Sizing