AugmentedFruitVegML: Enhancing Postharvest Quality of Fruits and Vegetables for Improved Freshness Detection and Classification

ABSTRACT

Postharvest quality assessment of fruits and vegetables is pivotal for ensuring optimal freshness, reducing waste, and enhancing the economic value of agricultural produce. With the increasing application of machine learning (ML) in this domain, significant strides have been made in automated detection and classification systems. This paper aims to enhance the quality assessment of vegetables and fruits by proposing a novel model titled AugmentedFruitVegML. This proposed framework depicts a robust performance when utilized to numerous ML classifiers, including Support Vector Machines (SVM), Logistics Regression and evaluated across multiple metrics, including F1-score, accuracy, precision, accuracy, sensitivity, specificity, and ROC-AUC. The findings underscore the pertinence of enhancing fewer data and model selection in accomplishing optimal classification performance.  Our analysis indicates a strong suggestion that ML has the potential to substantially impact reduction to postharvest losses and improve supply chain efficiency. This study adds to the growing body of research on AI-enabled postharvest quality assessment, providing innovative perspectives into the methodological approaches that enhance reliability and accuracy.

References

• Yuan, Y., Chen, J., Polat, K., & Alhudhaif, A. (2024). An innovative approach to detecting the freshness of fruits and vegetables through the integration of convolutional neural networks and bidirectional long short-term memory network. Current Research in Food Science, 8, 100723.
• Tapia-Mendez, E., Cruz-Albarran, I. A., Tovar-Arriaga, S., & Morales-Hernandez, L. A. (2023). Deep learning-based method for classification and ripeness assessment of fruits and vegetables. Applied Sciences, 13(22), 12504.
• Mukhiddinov, M., Muminov, A., & Cho, J. (2022). Improved classification approach for fruits and vegetables freshness based on deep learning. Sensors, 22(21), 8192
• Wang, C., Liu, S., Wang, Y., Xiong, J., Zhang, Z., Zhao, B., … & He, P. (2022). Application of convolutional neural network-based detection methods in fresh fruit production: a comprehensive review. Frontiers in plant science, 13, 868745.
• Mukherjee, A., Sarkar, T., Chatterjee, K., Lahiri, D., Nag, M., Rebezov, M., … & Lorenzo, J. M. (2022). Development of artificial vision system for quality assessment of oyster mushrooms. Food Analytical Methods, 15(6), 1663-1676.
• Mohi-Alden, K., Omid, M., Firouz, M. S., & Nasiri, A. (2023). A machine vision-intelligent modelling based technique for in-line bell pepper sorting. Information Processing in Agriculture, 10(4), 491-503.
• Patil, P. U., Lande, S. B., Nagalkar, V. J., Nikam, S. B., & Wakchaure, G. C. (2021). Grading and sorting technique of dragon fruits using machine learning algorithms. Journal of Agriculture and Food Research, 4, 100118.
• Kheiralipour, K., & Pormah, A. (2017). Introducing new shape features for classification of cucumber fruit based on image processing technique and artificial neural networks. Journal of food process engineering, 40(6), e12558.
• Wieme, J., Mollazade, K., Malounas, I., Zude-Sasse, M., Zhao, M., Gowen, A., & Van Beek, J. (2022). Application of hyperspectral imaging systems and artificial intelligence for quality assessment of fruit, vegetables and mushrooms: A review. biosystems engineering, 222, 156-176.
• Ahmed, T., Wijewardane, N. K., Lu, Y., Jones, D. S., Kudenov, M., Williams, C., & Kamruzzaman, M. (2024). Advancing sweetpotato quality assessment with hyperspectral imaging and explainable artificial intelligence. Computers and Electronics in Agriculture, 220, 108855.
• Zhu, L., Spachos, P., Pensini, E., & Plataniotis, K. N. (2021). Deep learning and machine vision for food processing: A survey. Current Research in Food Science, 4, 233-249.
• Yeboah, S., Hong, S. J., Park, Y., Choi, J. H., & Eum, H. L. (2023). Postharvest Quality Improvement of Bell Pepper (Capsicum annuum L. cv Nagano) with Forced-Air Precooling and Modified Atmosphere Packaging. Foods, 12(21), 3961.
• Moon, T., Park, J., & Son, J. E. (2021). Prediction of the fruit development stage of sweet pepper (Capsicum annum var. annuum) by an ensemble model of convolutional and multilayer perceptron. Biosystems Engineering, 210, 171-180.
• Blasco, J., Munera, S., Aleixos, N., Cubero, S., & Molto, E. (2017). Machine vision-based measurement systems for fruit and vegetable quality control in postharvest. Measurement, modeling and automation in advanced food processing, 71-91.
• Patel, H., Prajapati, R., & Patel, M. (2019, April). Detection of quality in orange fruit image using SVM classifier. In 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)(pp. 74-78). IEEE.
• Varjão, J. O. R., Botelho, G. M., Almeida, T. D. S., Martins, G. A. D. S., & Silva, W. G. D. (2019). Citrus fruit quality classification using support vector machines. Int J Advanc Eng Res Sci, 6(7), 59-65.
• Arunima, P. L., Gopinath, P. P., Lekshmi, P. G., & Esakkimuthu, M. (2024). Digital assessment of post-harvest Nendran banana for faster grading: CNN-based ripeness classification model. Postharvest Biology and Technology, 214, 112972.
• Zhang, Y., Zhu, D., Ren, X., Shen, Y., Cao, X., Liu, H., & Li, J. (2022). Quality changes and shelf-life prediction model of postharvest apples using partial least squares and artificial neural network analysis. Food Chemistry, 394, 133526.
• Singh, A., Vaidya, G., Jagota, V., Darko, D. A., Agarwal, R. K., Debnath, S., & Potrich, E. (2022). Recent advancement in postharvest loss mitigation and quality management of fruits and vegetables using machine learning frameworks. Journal of Food Quality, 2022(1), 6447282.
• Hayat, A., Morgado-Dias, F., Choudhury, T., Singh, T. P., & Kotecha, K. (2024). FruitVision: A deep learning based automatic fruit grading system. Open Agriculture, 9(1), 20220276.
• Fu, Y., Nguyen, M., & Yan, W. Q. (2022). Grading methods for fruit freshness based on deep learning. SN Computer Science, 3(4), 264.
• Fadiji, T., Bokaba, T., Fawole, O. A., & Twinomurinzi, H. (2023). Artificial intelligence in postharvest agriculture: mapping a research agenda. Frontiers in Sustainable Food Systems, 7, 1226583.
• Pathmanaban, P., Gnanavel, B. K., Anandan, S. S., & Sathiyamurthy, S. (2023). Advancing post-harvest fruit handling through AI-based thermal imaging: applications, challenges, and future trends. Discover Food, 3(1), 27.
• 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.
• Mahmood, A., Singh, S. K., & Tiwari, A. K. (2022). Pre-trained deep learning-based classification of jujube fruits according to their maturity level. Neural Computing and Applications, 34(16), 13925-13935.
• Ashtiani, S. H. M., Javanmardi, S., Jahanbanifard, M., Martynenko, A., & Verbeek, F. J. (2021). Detection of mulberry ripeness stages using deep learning models. IEEE Access, 9, 100380-100394.
• Momeny, M., Jahanbakhshi, A., Jafarnezhad, K., & Zhang, Y. D. (2020). Accurate classification of cherry fruit using deep CNN based on hybrid pooling approach. Postharvest Biology and Technology, 166, 111204.
• Kanjilal, R., Saenz, J. E., & Uysal, I. (2025). Large-scale data-driven uniformity analysis and sensory prediction of commercial banana ripening process. Postharvest Biology and Technology, 219, 113203.
• Kumari, N., Dwivedi, R. K., Bhatt, A. K., & Belwal, R. (2022). Automated fruit grading using optimal feature selection and hybrid classification by self-adaptive chicken swarm optimization: grading of mango. Neural computing and applications, 34(2), 1285-1306.
• Alharbi, A. H., Alkhalaf, S., Asiri, Y., Abdel-Khalek, S., & Mansour, R. F. (2023). Automated fruit classification using enhanced tunicate swarm algorithm with fusion based deep learning. Computers and Electrical Engineering, 108, 108657.
• Ali, S. S. E., & Dildar, S. A. (2021). MLP-WOA Neural Network-Based Automated Grading of Fruits and Vegetable Quality Detection for Food Industry Using Artificial Intelligence Techniques (Computer Vision—Image Recognition). In Applications of Artificial Intelligence in Engineering: Proceedings of First Global Conference on Artificial Intelligence and Applications (GCAIA 2020)(pp. 539-561). Springer Singapore.
• Pathmanaban, P., Gnanavel, B. K., Anandan, S. S., & Sathiyamurthy, S. (2023). Advancing post-harvest fruit handling through AI-based thermal imaging: applications, challenges, and future trends. Discover Food, 3(1), 27.

Download all article in PDF

WSN 197 (2024) 182-199

ADVERTISEMENT