Apple grading method based on neural network with ordered partitions and evidential ensemble learning
Liyao Ma, Peng Wei, Xinhua Qu, Shuhui Bi, Yuan Zhou, Tao Shen
Abstract
Abstract In order to improve the performance of the automatic apple grading and sorting system, in this paper, an ensemble model of ordinal classification based on neural network with ordered partitions and Dempster–Shafer theory is proposed. As a non‐destructive grading method, apples are graded into three grades based on the Soluble Solids Content value, with features extracted from the preprocessed near‐infrared spectrum of apple serving as model inputs. Considering the uncertainty in grading labels, mass generation approach and evidential encoding scheme for ordinal label are proposed, with uncertainty handled within the framework of Dempster–Shafer theory. Constructing neural network with ordered partitions as the base learner, the learning procedure of the Bagging‐based ensemble model is detailed. Experiments on Yantai Red Fuji apples demonstrate the satisfactory grading performances of proposed evidential ensemble model for ordinal classification.