Design of a tomato classifier based on machine vision.

Li Liu,Zhengkun Li,Yongjie Cui,Yufei Lan,Yinggang Shi,Zhihan Lv

doi:10.1371/journal.pone.0219803

Abstract

This paper attempts to design an automated, efficient and intelligent tomato grading method that facilitates the graded selling of the fruit. Based on machine vision, the color images of tomatoes with different morphologies were studied, and the color, shape and size were selected as the key features. On this basis, an automated grading classifier was created based on the surface features of tomatoes, and a grading platform was set up to verify the effect of the classifier. Specifically, the Hue value distributions of tomatoes with different maturities were investigated, and the Hue value ranges were determined for mature, semi-mature and immature tomatoes, producing the color classifier. Next, the first-order Fourier descriptor (1D- FD) was adopted to describe the radius sequence of tomato contour, and an equation was established to compute the irregularity of tomato contour, creating the shape classifier. After that, a linear regression equation was constructed to reflect the relationship between the transverse diameters of actual tomatoes and tomato images, and a classifier between large, medium and small tomatoes was produced based on the transverse diameter. Finally, a comprehensive tomato classifier was built based on the color, shape and size diameters. The experimental results show that the mean grading accuracy of the proposed method was 90.7%. This means our method can achieve automated real-time grading of tomatoes.

Highlights

Grading is a key measure to commercialize agricultural products, and improve the economic efficiency of agriculture [1]
Pavithra V. et al extracted the texture, color and shape features of tomato surface, and realized the quality grading of mature tomatoes using the support vector machine (SVM) classifier based on K-nearest neighbors’ algorithm [5]
If the transverse diameter is greater than 7 cm, the tomato is allocated to the large (L) category; if it falls between 5 cm and 7 cm, the tomato belongs to the medium (M) category; if it is smaller than 5 cm, the tomato is added to the small (S) category

Summary

Introduction

Grading is a key measure to commercialize agricultural products, and improve the economic efficiency of agriculture [1]. The automated tomato grading has been explored by various experts and scholars, using important indices like color, texture, size, shape and surface texture [3]. Many of them developed automated tomato grading algorithms based on computer vision [4]. Pavithra V. et al extracted the texture, color and shape features of tomato surface, and realized the quality grading of mature tomatoes using the support vector machine (SVM) classifier based on K-nearest neighbors’ algorithm [5]. Peng Wang et al recognized the color features of concentric circles with equal area on tomato surface, and created a maturity grading

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Discussion

Conclusion