Infield Apple Detection and Grading Based on Multi-Feature Fusion

Guangrui Hu,Jun Chen,Zening Gao,Adilet Sugirbay,Shuo Zhang,Jian Zhao,Jianguo Zhou,Hongling Jin,Enyu Zhang

doi:10.3390/horticulturae7090276

Abstract

A field-based apple detection and grading device was developed and used to detect and grade apples in the field using a deep learning framework. Four features were selected for apple grading, namely, size, color, shape, and surface defects, and detection algorithms were designed to discriminate between the four features using machine vision and other methods. Then, the four apple features were fused, and a support vector machine (SVM) was used for infield apple grading into three grades: first-grade fruit, second-grade fruit, and other-grade fruit. The results showed that for a single index, the accuracy of detecting the apple size, the fruit shape, the color, and the surface defects, were 99.04%, 97.71%, 98%, and 95.85%. The grading accuracies for the first-grade fruit, second-grade fruit, other-grade fruit, and the average grading accuracy based on multiple features were 94.55%, 95.71%, 100%, and 95.49%, respectively. The field experiment showed that the average grading accuracy was 94.12% when the feeding interval of the apples was less than 1.5 s and the walking speed did not exceed 0.5 m/s, meeting the accuracy requirements of field-based apple grading.

Highlights

Fruit grading is crucial for fruit marketing and is directly related to the effects of fruit packaging, transportation, storage, and sales [1,2,3,4]
The results indicate that this method can accurately extract the fruit size
The size, red area ratio, roundness, shape index, and surface defects were extracted from the top view and side view images of the apples, and the apples were classified into three grades using the support vector machine (SVM)

Summary

Introduction

Fruit grading is crucial for fruit marketing and is directly related to the effects of fruit packaging, transportation, storage, and sales [1,2,3,4]. When people buy apples in the market, they typically evaluate the quality of apples based on exterior features related to fruit appearance, such as size, color, shape, and surface defects [6,9,10,11]. Grading apples according to their appearance is an important indicator to improve the market value of apples. Apple field grading is essential to improve the economic value of the fruit and significantly affects the industrial chain of apple production [1]. The downstream production enterprises can adopt targeted storage, processing, grading, and other processes according to the field grading results of the apples to increase the competitiveness of enterprises. The environment in apple field grading is more complex than in industrial grading and is affected by vibration and other factors [16]

Methods

Results

Conclusion