Weight and volume estimation of single and occluded tomatoes using machine vision

Abstract

The fundamental characteristics of agricultural products are appearance, size, and weight, which affect their market value, consumer preference, and choice. Thus, food and agricultural industries seek rapid, simple, and nondestructive approaches to assess real-time measurements at the post-harvest stage before packaging for the consumer market. While sorting and grading may be performed by humans, it is unreliable, time-consuming, complicated, subjective, onerous, expensive, and easily influenced by surroundings. Therefore, an astute sorting and grading method for tomato fruit is required. We evaluated two tomato configurations on a conveyor belt: single tomatoes (no occlusion) and multi-tomatoes (partially occluded). We used polygon approximation for concave and convex point extraction algorithms to segment the occluded tomatoes. We developed seven models for regression using single-tomato image features. The Bayesian regularization artificial neural network outranked all the trained models in weight estimation with a root-mean-square error (RMSE) of 1.468 g and R2 of 0.971. For volume estimation, the RBF SVM had the best performance with R2 of 0.982 and RMSE of 1.2683 cm3. It is feasible to implement a proposed system as a noninvasive in-line sorting technique for tomatoes.

Keywords

• tomato
• grading and sorting
• machine learning
• weight and volume
• occlusion
• polygon approximation