Abstract
Grading and classification of fruits and vegetables has a major role in storage and supply chain. Manual grading is difficult and time-consuming. Therefore, this study employs computer vision (CV) and machine learning algorithms to detect the freshness quality of tomatoes during storage. The freshness quality is classified into 10 grades, where grade 1 is fresh and grade 10 denotes rotten. Image prepossessing and handcrafted feature extraction combined with a shallow artificial neural network (ANN) is employed for the task. Results from the proposed ANN outperform several state-of-the-art methods, including imaging-based deep neural networks. We construct a large volume dataset containing –70 days, day-by-day degradation for the task. We hope that the dataset will attract researchers and add a valuable contribution to the community.
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Das, S., Mondal, P., Quraishi, M.I., Kar, S., Sekh, A.A. (2022). Freshness Quality Detection of Tomatoes Using Computer Vision. In: Sk, A.A., Turki, T., Ghosh, T.K., Joardar, S., Barman, S. (eds) Artificial Intelligence. ISAI 2022. Communications in Computer and Information Science, vol 1695. Springer, Cham. https://doi.org/10.1007/978-3-031-22485-0_22
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