Prediction of Fruit Maturity, Quality, and Its Life Using Deep Learning Algorithms

Aherwadi, Nagnath; Mittal, Usha; Singla, Jimmy; Jhanjhi, N. Z.; Yassine, Abdulsalam; Hossain, M. Shamim

doi:10.3390/electronics11244100

Cited by 171Smart Agriculture and AIAdvanced Neural Network ApplicationsPostharvest Quality and Shelf Life Management

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Prediction of Fruit Maturity, Quality, and Its Life Using Deep Learning Algorithms

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Aherwadi, Nagnath, Mittal, Usha, Singla, Jimmy, Jhanjhi, N. Z., Yassine, Abdulsalam, Hossain, M. Shamim·2022·Electronics

DOI 10.3390/electronics11244100

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Abstract

Fruit that has reached maturity is ready to be harvested. The prediction of fruit maturity and quality is important not only for farmers or the food industry but also for small retail stores and supermarkets where fruits are sold and purchased. Fruit maturity classification is the process by which fruits are classified according to their maturity in their life cycle. Nowadays, deep learning (DL) has been applied in many applications of smart agriculture such as water and soil management, crop planting, crop disease detection, weed removal, crop distribution, strong fruit counting, crop harvesting, and production forecasting. This study aims to find the best deep learning algorithms which can be used for the prediction of fruit maturity and quality for the shelf life of fruit. In this study, two datasets of banana fruit are used, where we create the first dataset, and the second dataset is taken from Kaggle, named Fruit 360. Our dataset contains 2100 images in 3 categories: ripe, unripe, and over-ripe, each of 700 images. An image augmentation technique is used to maximize the dataset size to 18,900. Convolutional neural networks (CNN) and AlexNet techniques are used for building the model for both datasets. The original dataset achieved an accuracy of 98.25% for the CNN model and 81.75% for the AlexNet model, while the augmented dataset achieved an accuracy of 99.36% for the CNN model and 99.44% for the AlexNet model. The Fruit 360 dataset achieved an accuracy of 81.96% for CNN and 81.75% for the AlexNet model. We concluded that for all three datasets of banana images, the proposed CNN model is the best suitable DL algorithm for bananas’ fruit maturity classification and quality detection.

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By analyzing individual health data, dietary habits, and nutritional needs, AI can design 3D-printed foods that cater to specific health conditions such as diabetes, hypertension, or malnutrition.
“By analyzing individual health data, dietary habits, and nutritional needs, AI can design 3D-printed foods that cater to specific health conditions, such as diabetes, hypertension, or malnutrition [152].”
From Towards the development of foods 3D printer: Trends and technologies for foods printing (2024) · cites this paper as [152]

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Authors: Aherwadi, N., Mittal, U., Singla, J., Jhanjhi, N. Z., Yassine, A., & Hossain, M. S.
Affiliations: Lovely Professional University (IN)
Taylor's University (MY)
Lakehead University (CA)
King Saud University (SA)
Corresponding author: Jhanjhi, N. Z.
ORCID: Aherwadi, Nagnath
Mittal, Usha
Singla, Jimmy
Jhanjhi, N. Z.
Yassine, Abdulsalam
Hossain, M. Shamim
Venue: Electronics
Publisher: MDPI AG
Published: 2022-12-09
Volume: 11
Issue: 24
Pages: 4100
ISSN: 2079-9292 (ISSN-L)
DOI: 10.3390/electronics11244100
OpenAlex: W4311974999
Cited by: 171
References: 21
Citations by year: 2026: 35 · 2025: 80 · 2024: 37 · 2023: 19
FWCI: 18.73
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Electronics (publishedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: King Saud University (RSP-2021/32)
Topics: Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Life Sciences › Agricultural and Biological Sciences › Plant Science › Postharvest Quality and Shelf Life Management
Keywords: banana ripeness classification, fruit quality detection, shelf life prediction, convolutional neural networks, AlexNet, image augmentation, smart agriculture

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Prediction of Fruit Maturity, Quality, and Its Life Using Deep Learning Algorithms

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