Citation: Chen, K.; Li, W.; An, J.; Bu,
T. Greengage Grading Method Based
on Dynamic Feature and Ensemble
Networks. Electronics 2022, 11, 1832.
https://doi.org/10.3390/
electronics11121832
Academic Editor:
Pedro Latorre-Carmona
Received: 15 May 2022
Accepted: 7 June 2022
Published: 9 June 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
Greengage Grading Method Based on Dynamic Feature and
Ensemble Networks
Keqiong Chen
1,
* , Weitao Li
2
, Jiaxi An
1
and Tianrui Bu
1
1
School of Advanced Manufacturing Engineering, Hefei University, Hefei 230601, China;
anjx@hfuu.edu.cn (J.A.); butr@hfuu.edu.cn (T.B.)
2
School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China;
wtli@hfut.edu.cn
* Correspondence: chenkq@hfuu.edu.cn; Tel.: +86-137-3922-0668
Abstract:
To overcome the deficiencies of the traditional open-loop cognition method, which lacks
evaluation of the cognitive results, a novel cognitive method for greengage grading based on dynamic
feature and ensemble networks is explored in this paper. First, a greengage grading architecture with
an adaptive feedback mechanism based on error adjustment is constructed to imitate the human
cognitive mechanism. Secondly, a dynamic representation model for convolutional feature space
construction of a greengage image is established based on the entropy constraint indicators, and
the bagging classification network for greengage grading is built based on stochastic configuration
networks (SCNs) to realize a hierarchical representation of the greengage features and enhance the
generalization of the classifier. Thirdly, an entropy-based error model of the cognitive results for
greengage grading is constructed to describe the optimal cognitive problem from an information
perspective, and then the criteria and mechanism for feature level and feature efficiency regulation
are given out within the constraint of cognitive error entropy. Finally, numerous experiments are
performed on the collected greengage images. The experimental results demonstrate the effectiveness
and superiority of our method, especially for the classification of similar samples, compared with the
existing open-loop algorithms.
Keywords:
greengage grading; dynamic feature; entropic constraint; feedback regulation; deep
ensemble learning
1. Introduction
The realization of the automatic classification of fruit grades has become an essen-
tial precondition for the modernization of the fruit industry [
1
]. Greengage is a kind of
pharmaceutical and food resource with multiple healthcare functions which is favored by
the masses. At present, the existing automatic classification mostly involves screening of
the particle size and weight. The sorting of its quality often relies on manual screening,
which is not only labor-intensive but also susceptible to subjective factors such as operator
experience, so its cognitive effect is hard to evaluate satisfactorily. Therefore, development
of a fast and accurate machine grading method becomes an urgent need to promote the fruit
industry [
2
–
4
]. A fast classification method for fruit grading based on multiple kernel sup-
port vector machines (kSVM) is proposed in [
2
]. A fuzzy cluster-based image segmentation
method is proposed in [
3
], and the extracted features are introduced into the deep neural
network to achieve apple grading. In [
4
], a carrot surface defect detection method based
on the fusion of computer vision and deep learning was proposed to achieve real-time
carrot quality grading. Various levels of the feature space and various perspectives within
the same feature level represent discriminative attention. However, with uncertain image
inputs and indeterminate grade outputs, the traditional machine fruit grading methods
with the open-loop method lack updated data structures of the feature space and classified
Electronics 2022, 11, 1832. https://doi.org/10.3390/electronics11121832 https://www.mdpi.com/journal/electronics
