Apple grading method based on neural network with ordered partitions and evidential ensemble learning

• 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. © 2022 The Authors. CAAI Transactions on Intelligence Technology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology and Chongqing University of Technology.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Nyckelord

apple grading

Demspter–Shafer theory

ensemble learning

ordinal classification

Fruits

Infrared devices

Learning systems

Dempster-Shafer theory

Demspter-Shafer theory

Ensemble models

Grading methods

Neural-networks

Performance

Uncertainty

Grading

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