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Article

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

1
Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, India
2
School of Computer Science (SCS), Taylor’s University, Subang Jaya 47500, Malaysia
3
Department of Software Engineering, Faculty of Engineering, Lakehead University, 955 Oliver Rd, Thunder Bay, ON P7B 5E1, Canada
4
Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
*
Author to whom correspondence should be addressed.
Electronics 2022, 11(24), 4100; https://doi.org/10.3390/electronics11244100
Submission received: 2 October 2022 / Revised: 3 December 2022 / Accepted: 5 December 2022 / Published: 9 December 2022

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.
Keywords: deep learning; convolutional neural network (CNN); AlexNet; image augmentation; maturity classification; quality detection; agriculture management deep learning; convolutional neural network (CNN); AlexNet; image augmentation; maturity classification; quality detection; agriculture management

Share and Cite

MDPI and ACS Style

Aherwadi, N.; Mittal, U.; Singla, J.; Jhanjhi, N.Z.; Yassine, A.; Hossain, M.S. Prediction of Fruit Maturity, Quality, and Its Life Using Deep Learning Algorithms. Electronics 2022, 11, 4100. https://doi.org/10.3390/electronics11244100

AMA Style

Aherwadi N, Mittal U, Singla J, Jhanjhi NZ, Yassine A, Hossain MS. Prediction of Fruit Maturity, Quality, and Its Life Using Deep Learning Algorithms. Electronics. 2022; 11(24):4100. https://doi.org/10.3390/electronics11244100

Chicago/Turabian Style

Aherwadi, Nagnath, Usha Mittal, Jimmy Singla, N. Z. Jhanjhi, Abdulsalam Yassine, and M. Shamim Hossain. 2022. "Prediction of Fruit Maturity, Quality, and Its Life Using Deep Learning Algorithms" Electronics 11, no. 24: 4100. https://doi.org/10.3390/electronics11244100

APA Style

Aherwadi, N., Mittal, U., Singla, J., Jhanjhi, N. Z., Yassine, A., & Hossain, M. S. (2022). Prediction of Fruit Maturity, Quality, and Its Life Using Deep Learning Algorithms. Electronics, 11(24), 4100. https://doi.org/10.3390/electronics11244100

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