Dry fruit image classification using stacking ensemble model
Files
(Published version)
Date
2025
Authors
Islam, M.
Islam, M.
Suny, A.
Al Rafi, A.
Chowdhury, A.
Islam, M.M.
Masum, S.
Ali, M.S.
Jabid, T.
Rasel, M.M.K.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
Journal of Agriculture and Food Research, 2025; 21(101850):1-12
Statement of Responsibility
Conference Name
Abstract
Precise and efficient classification of dry fruit images is critical for enhancing quality control, efficiency, and safety in the agricultural and food industries. This study presents a CNN-based classification model developed to analyze a diverse dataset of dry fruit images. The dataset comprises 11,520 high-resolution images representing four varieties (raisins, cashews, almonds, and figs), each divided into three groups, resulting in twelve distinct classes. The proposed multi-step methodology covers data collection, preprocessing, augmentation, and model training. A wide range of image conditions, including variations in fruit types, shapes, colors, and lighting, facilitated comprehensive experimentation and validation using performance indicators such as accuracy. A stacking ensemble, integrating predictions from multiple models (e.g., VGG16, VGG19, ResNet50, MobileNetV1, Mo- bileNetV2, SqueezeNet, and ShuffleNet), achieved a test accuracy of 98.32 %, surpassing individual base model performances (MobileNetV2: 90.33 %, Mo- bileNetV1: 93.68 %, SqueezeNet: 96.98 %, and ShuffleNetV2: 97.79 %). These findings underscore the model’s potential for real-time applications in qual-ity control, automated processing, nutritional research, and other domains, while also delineating the key components required for accurate classification.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
Copyright 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)