The week's pick
Random Articles
Reseach Article
Date Fruit Classification with Machine Learning and Explainable Artificial Intelligence
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 50 |
| Year of Publication: 2023 |
| Authors: Md. Sahidullah, Nasim Mahmud Nayan, Md. Samin Morshed, Mohammad Mobarak Hossain, Muhammad Usama Islam |
10.5120/ijca2023922617
|
Md. Sahidullah, Nasim Mahmud Nayan, Md. Samin Morshed, Mohammad Mobarak Hossain, Muhammad Usama Islam . Date Fruit Classification with Machine Learning and Explainable Artificial Intelligence. International Journal of Computer Applications. 184, 50 ( Mar 2023), 1-5. DOI=10.5120/ijca2023922617
Abstract
Fruit research now has reached a new dimension thanks to machine learning, which produces actionable insights for further exploration by practitioners in the agricultural domain. In order to automatically categorize the edibility of date fruit, we investigated various types of date fruits and used explainable artificial intelligence (XAI) techniques combined with machine learning-based methods to effectively classify and explain the classification task. Our result shows that with a formidable accuracy ranging within the 90-92 percentile for seven methods, including boosting, bagging, support vector machine (SVM), k-nearest neighbor (KNN), and MLPs, the machine learning methods combined with Local Interpretable Model-Agnostic Explanations (LIME) based XAI provides better actionable insights which can be utilized by domain experts and stakeholders to produce and supply quality fruits particularly date fruits thus contributing in broader perspectives with respect to this dynamically evolving domain. The implementation of the investigation and experiment is available in Github.
References
- Shadi Alzu’bi, Omar Badarneh, Bilal Hawashin, Mahmoud Al-Ayyoub, Nouh Alhindawi, and Yaser Jararweh. Multilabel emotion classification for arabic tweets. In 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS), pages 499–504. IEEE, 2019.
- Reem Alzu’bi, A Anushya, Ebtisam Hamed, Eng Abdelnour Al Sha’ar, and BS Angela Vincy. Dates fruits classification using svm. In AIP Conference Proceedings, volume 1952, page 020078. AIP Publishing LLC, 2018.
- Winda Astuti, Satrio Dewanto, Khristian Edi Nugroho Soebandrija, and Sofyan Tan. Automatic fruit classification using support vector machines: a comparison with artificial neural network. In IOP Conference Series: Earth and Environmental Science, volume 195, page 012047. IOP Publishing, 2018.
- Santi Kumari Behera, Amiya Kumar Rath, Abhijeet Mahapatra, and Prabira Kumar Sethy. Identification, classification & grading of fruits using machine learning & computer intelligence: a review. Journal of Ambient Intelligence and Humanized Computing, pages 1–11, 2020.
- Davide Chicco and Giuseppe Jurman. The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation. BMC genomics, 21(1):1–13, 2020.
- Sumaira Ghazal, Waqar S Qureshi, Umar S Khan, Javaid Iqbal, Nasir Rashid, and Mohsin I Tiwana. Analysis of visual features and classifiers for fruit classification problem. Computers and Electronics in Agriculture, 187:106267, 2021.
- Harmandeep Singh Gill and Baljit Singh Khehra. Hybrid classifier model for fruit classification. Multimedia Tools and Applications, 80(18):27495–27530, 2021.
- Jasmeen Gill, Parvinder Singh Sandhu, Tejwant Singh, et al. A review of automatic fruit classification using soft computing techniques. In Int. Conf. Comput. Syst. Electron. Eng, pages 91–98, 2014.
- David Gunning, Mark Stefik, Jaesik Choi, Timothy Miller, Simone Stumpf, and Guang-Zhong Yang. Xai—explainable artificial intelligence. Science robotics, 4(37):eaay7120, 2019.
- Anuj Gupta, Mr Rishi Kushwaha, and Mr Kailash Patidar. A survey: Fruit classification using machine learning. International Journal of Engineering Technology and Applied Science, 5(7), 2019.
- DNVSLS Indira, Jyothi Goddu, Baisani Indraja, Vijaya Madhavi Lakshmi Challa, and Bezawada Manasa. A review on fruit recognition and feature evaluation using cnn. Materials Today: Proceedings, 2021.
- Muhammad Usama Islam and Beenish Moalla Chaudhry. A framework to enhance user experience of older adults with speech-based intelligent personal assistants. IEEE Access, 11:16683–16699, 2023.
- Muhammad Usama Islam, Mozaharul Mottalib, Mehedi Hassan, Zubair Ibne Alam, SM Zobaed, Fazle Rabby, et al. The past, present, and prospective future of xai: A comprehensive review. Explainable Artificial Intelligence for Cyber Security, pages 1–29, 2022.
- Md Karim, Md Foysal, Sunanda Das, et al. Stock price prediction using bi-lstm and gru-based hybrid deep learning approach. In Proceedings of Third Doctoral Symposium on Computational Intelligence, pages 701–711. Springer, 2023.
- Murat Koklu, Ramazan Kursun, Yavuz Selim Taspinar, and Ilkay Cinar. Classification of date fruits into genetic varieties using image analysis. Mathematical Problems in Engineering, 2021, 2021.
- Sotiris B Kotsiantis, Ioannis Zaharakis, P Pintelas, et al. Supervised machine learning: A review of classification techniques. Emerging artificial intelligence applications in computer engineering, 160(1):3–24, 2007.
- R Shantha Selva Kumari and V Gomathy. Fruit classification using statistical features in svm classifier. In 2018 4th International Conference on Electrical Energy Systems (ICEES), pages 526–529. IEEE, 2018.
- Vishal Meshram, Kailas Patil, Vidula Meshram, Dinesh Hanchate, and SD Ramkteke. Machine learning in agriculture domain: A state-of-art survey. Artificial Intelligence in the Life Sciences, 1:100010, 2021.
- Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. ” why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144, 2016.
- Sklearn Preprocessing StandardScaler—Scikit-Learn. 0.24. 2 documentation.
- MVijayalakshmi and V Joseph Peter. Cnn based approach for identifying banana species from fruits. International Journal of Information Technology, 13(1):27–32, 2021.
- NiWayan SuryaWardhani, Masithoh Yessi Rochayani, Atiek Iriany, Agus Dwi Sulistyono, and Prayudi Lestantyo. Crossvalidation metrics for evaluating classification performance on imbalanced data. In 2019 international conference on computer, control, informatics and its applications (IC3INA), pages 14–18. IEEE, 2019.
Index Terms
Keywords