Title: An efficient fruit quality monitoring and classification using convolutional neural network and fuzzy system

Authors: K.D. Mohana Sundaram; T. Shankar; N. Sudhakar Reddy

Addresses: Annamalai University, Chidambaram, Tamil Nadu, India ' Department of Electronics and Communication Engineering, Annamalai University, Chidambaram, Tamil Nadu, India ' M.J.R. College of Engineering and Technology, Andhra Pradesh, India

Abstract: Fruit quality monitoring in agro industries is carried out by people who may deviate from their responsibility due to tiredness, illness, or personal reasons. So, an automatic quality assessment system is proposed based on convolutional neural network (CNN) and Mamdani fuzzy logic that estimate quality of a Persian Lemon. The proposed CNN was trained with the transfer learning method and the results obtained were compared with previous works. The proposed CNN achieved 94.79% accuracy in the validation process which is 13% higher than the existing architecture. The proposed fuzzy logic classified each lemon in three ranges based on rules customised for the estimation of fruit quality standards.

Keywords: fuzzy systems; transfer learning; convolutional neural network; CNN.

DOI: 10.1504/IJESMS.2024.135115

International Journal of Engineering Systems Modelling and Simulation, 2024 Vol.15 No.1, pp.20 - 26

Received: 13 Oct 2021
Accepted: 02 Feb 2022
Published online: 01 Dec 2023 *