Abstract
Date fruit, a vital agricultural product in the Middle East area, is harvested annually in millions of metric tons and is renowned for its abundant nutrients. With computer vision and machine learning techniques, automatic date fruit classification enables farmers and supermarkets to differentiate between various varieties and qualities of date fruits within their inventory. Date fruits have unique physical characteristics, such as shape, size, color, texture, and skin type that are important in determining their variety and quality. These characteristics can vary significantly depending on the cultivar, growing conditions, and ripening stage of the date fruits. This paper presents a novel date fruit type classification and grading system achieved through the feature-level fusion of deep learning features and wavelet scattering features. Wavelet scattering features are extracted at varying levels of decomposition; enabling reliable extraction of information from diverse channels. To extract deep features this study utilizes pre-trained architectures, including Alexnet, Googlenet, Resnet, and MobileNetV2. The proposed methodology has been experimentally evaluated with the Date Fruit in Controlled Environment dataset, which has nine classes, and has yielded an accuracy of 95.9% for date species classification. Various date fruit species from the TU-DG dataset were graded, and for Ajwa species, the accuracy is 97.8%, for Mabroom, 92.6% accuracy, and for Sukkary, 99.5% accuracy.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Aiadi O, Kherfi ML (2017) A new method for automatic date fruit classification. Int J Comput vis Robot 7:692–711. https://doi.org/10.1504/IJCVR.2017.087751
Albarrak K, Gulzar Y, Hamid Y et al (2022) A deep learning-based model for date fruit classification. Sustain. https://doi.org/10.3390/su14106339
Almomen M, Al-Saeed M, Ahmad HF (2023) Date fruit classification based on surface quality using convolutional neural network models. Appl Sci 13(13):7821
Alresheedi KM, Aladhadh S, Khan RU, Qamar AM (2022) Dates fruit recognition: from classical fusion to deep learning. Comput Syst Sci Eng 40:151–166. https://doi.org/10.32604/CSSE.2022.017931
Alsirhani A, Siddiqi MH, Mostafa AM, Ezz M, Mahmoud AA (2023) A novel classification model of date fruit dataset using deep transfer learning. Electron 12(3):665
Altaheri H, Alsulaiman M, Muhammad G (2019) Date fruit classification for robotic harvesting in a natural environment using deep learning. IEEE Access 7:117115–117133. https://doi.org/10.1109/ACCESS.2019.2936536
Alzu’Bi R, Anushya A, Hamed E et al (2018) Dates fruits classification using SVM. AIP Conf Proc 1952. https://doi.org/10.1063/1.5032040
Arivazhagan S (2010) Fruit recognition using color and texture features. J Emerg Trends Comput Inf Sci 1(2):1–5
Arivazhagan S, Shebiah RN, Ananthi S, Vishnu Varthini S (2013) Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agric Eng Int CIGR J 15(1):211–217
Fadel M (2007) Date Fruits Classification Using Probabilistic Neural Networks. Agric Eng Int CIGR J IX:
Faisal M, Albogamy F, Elgibreen H, Algabri M, Alqershi FA (2020) Deep Learning and Computer Vision for Estimating Date Fruits Type, Maturity Level, and Weight. IEEE Access 2020, 8, 206770–206782. Available online: https://www.Mewa.Gov.Sa/En/MediaCenter/News/Pages/News201220.Aspx
Haidar A, Dong H, Mavridis N (2012) Image-based date fruit classification. Int Congr Ultra Mod Telecommun Control Syst Work. https://doi.org/10.1109/ICUMT.2012.6459693
Hakami A, Arif M (2019) Automatic inspection of the external quality of the date fruit. Procedia Comput Sci 163:70–77. https://doi.org/10.1016/j.procs.2019.12.088
Hassanien AE, Chang KC, Mincong T (Eds) (2021) Advanced Machine Learning Technologies and Applications. Advances in Intelligent Systems and Computing. Springer
He K et al (2016) Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition
Hsiao S, Mattox S, Park T et al (2009) Data enhancement for date fruit classification using DCGAN. ISECURE J 13(3):39–48. https://doi.org/10.22042/ISECURE.2021.13.3.0
Ibrahim DM, Elshennawy NM (2022) Improving date fruit classification using CycleGAN-generated dataset. C Comput Model Eng Sci. https://doi.org/10.32604/cmes.2022.016419
Khriji L, Ammari AC, Awadalla M (2020) Hardware/software co-design of a vision system for automatic classification of date fruits. Int J Embed Real-Time Commun Syst 11:21–40. https://doi.org/10.4018/IJERTCS.2020100102
Koirala A, Walsh KB, Wang Z, McCarthy C (2019) Deep learning for real-time fruit detection and orchard fruit load estimation: benchmarking of ‘MangoYOLO.’ Precision Agric 20(6):1107–1135. https://doi.org/10.1007/s11119-019-09642-0
Koklu M, Kursun R, Taspinar YS, Cinar I (2021) Classification of date fruits into genetic varieties using image analysis. Math Probl Eng. https://doi.org/10.1155/2021/4793293
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In NIPS, p 1106–1114
Kurtulmus F, Lee WS, Vardar A (2014) Immature peach detection in colour images acquired in natural illumination conditions using statistical classifiers and neural network. Precision Agric 15(1):57–79. https://doi.org/10.1007/s11119-013-9323-8
Mallat S (2010) Recursive interferometric representation. In: Proceedings of the European Signal Processing Conference, Aalborg, Denmark, p 716–720
Mallat S (2012) Group invariant scattering. Commun Pure Appl Math 65(10):1331–1398
Muhammad G (2015) Date fruits classification using texture descriptors and shape-size features. Eng Appl Artif Intell 37:361–367. https://doi.org/10.1016/j.engappai.2014.10.001
Nasiri A, Taheri-Garavand A, Zhang YD (2019) Image-based deep learning automated sorting of date fruit. Postharvest Biol Technol 153:133–141. https://doi.org/10.1016/j.postharvbio.2019.04.003
Oyallon E, Mallat S, Sifre L (2013) Generic deep networks with wavelet scattering. http://arxiv.org/abs/1312.5940. Accessed 29 Nov 2023
Pérez-Pérez DB, Salomón-Torres R, García-Vázquez JP (2021) Dataset for localization and classification of Medjool dates in digital images. Data Br. https://doi.org/10.1016/j.dib.2021.107116
Raissouli H, Aljabri AA, Aljudaibi SM, Haron F, Alharbi G (2020) Date grading using machine learning techniques on a novel dataset. Int J Adv Comput Sci Appl 11(8):758–765. https://doi.org/10.14569/IJACSA.2020.0110893
Russel NS, Selvaraj A (2022) Leaf species and disease classification using multiscale parallel deep CNN architecture. Neural Comput Appl 34(21):19217–19237. https://doi.org/10.1007/s00521-022-07521-w
Safran M, Alrajhi W, Alfarhood S (2023) DPXception: a lightweight CNN for image-based date palm species classification. Front Plant Sci 14:1–14
Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC MobileNetV2: Inverted Residuals and Linear Bottlenecks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, p 4510–4520
Simonyan K, Zisserman A (2014) Deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Szegedy C et al (2015) Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition
Zhang J, Zhang B, Jiang X (2000) Analysis of feature extraction methods based on wavelet transform. Signal Process 16:157–162
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare not to have any conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Russel, N.S., Selvaraj, A. Wavelet scattering transform and deep features for automated classification and grading of dates fruit. J Ambient Intell Human Comput (2024). https://doi.org/10.1007/s12652-024-04786-y
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12652-024-04786-y