Title: Lemon fruit classification by transfer learning technique: experimental investigation of convolutional neural network

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

Addresses: Department of ECE, Annamalai University, Chidambaram, Tamil Nadu, India ' Department of ECE, Government College of Engineering, Srirangam, Trichy, Tamil Nadu, India ' Department of ECE, M.J.R. College of Engineering and Technology, Piler, Andhra Pradesh, India

Abstract: Before exporting fruits, quality control is extremely important in the fruit industries. The most crucial step in the quality assessment process is to classify the fruit as fresh or spoiled. Convolutional neural network (CNN) is the most recent technology used for classification. Henceforth, in this work, the performance of eight widely used CNNs, namely AlexNet, DenseNet, GoogleNet, Inceptionv-3, MobileNetv-2, ResNet-18, SqueezNet, and VGGNet-19, was evaluated and compared for fruit classification, utilising the Lemon fruit dataset. To classify the lemon fruits into three categories of good-quality, medium-quality, and poor-quality, 1,000 fully connected layers in each CNN were substituted with three fully connected layers. For comparison, all of the CNNs were trained using the Transfer Learning technique with learning rates of 0.1, 0.01, and 0.001. The VGG Net-19 architecture was found to have a validation accuracy of 92.6% for a learning rate of 0.001.

Keywords: convolutional neural network; CNN; fruit classification; fully connected layers; transfer learning.

DOI: 10.1504/IJIDS.2025.150096

International Journal of Information and Decision Sciences, 2025 Vol.17 No.4, pp.401 - 409

Received: 24 Oct 2022
Accepted: 06 May 2023
Published online: 01 Dec 2025 *

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