Design of a computational application for the evaluation of the quality of export lemon
The automation of food processes increases the quality, productivity, and economy of the companies. This study aimed at the development of a Computer Vision System (CVS) to classify lemons in real-time according to their diameter. The CVS consisted of a software, coded in Java programming language, which covers the acquisition, pre-processing, segmentation, description, recognition, and interpretation of the images. The classification criteria were in accord with the CODEX STAN 213 standard for lime-lemon. The CVS reached a 0% error in the classification of the diameter of the lemons and required 0.33 seconds as processing time to detect and classify each lemon. The CVS showed high performance in the automatic classification of lemons according to their diameter. This CVS could avoid the disadvantages of manual classification.
Aredo, V.; Velásquez L.; Carranza-Cabrera, J.; Siche, R. 2019. Predicting of the Quality Attributes of Orange Fruit Using Hyperspectral Images. Journal of food quality and hazards control, 6: 82-92.
Borji, A. 2018. Negative results in computer vision: A perspective. Image and Vision Computing 69: 1-8.
Cubero, S.; Lee, W.S.; Aleixos, N.; Albert, F.; Blasco, J. 2016. Automated systems based on machine vision for inspecting citrus fruits from the field to postharvest a review. Food and Bioprocess Technology 9(10): 1623-1639.
Du, C.J.; Sun, D.W. 2006. Learning techniques used in computer vision for food quality evaluation: a review. Journal of food engineering 72(1): 39-55.
FAO - Food and Agriculture Organization of the United Nations. 2005. Codex Alimentarius. Standard for limes. 57-61.
Pira, E.; Rafe, V.; Nikanjam, A. 2018. Searching for violation of safety and liveness properties using knowledge discovery in complex systems specified through graph transformations. Information and Software Technology 97: 110-134.
Hadimani, L.; Mittal, N. 2019. Development of a computer vision system to estimate the colour indices of Kinnow mandarins. Journal of food science and technology 56(4): 2305-2311.
Magwaza, L.S.; Opara, U.L.; Nieuwoudt, H.; Cronje, P.J.; Saeys, W.; Nicolaï, B. 2012. NIR spectroscopy applications for internal and external quality analysis of citrus fruit a review. Food and Bioprocess Technology 5(2): 425-444.
Rauf, H.T.; Saleem, B.A.; Lali, M.I.; Khan, M.A.; Sharif, M.; Bukhari, S.A.C. 2019. A citrus fruits and leaves dataset for detection and classification of citrus diseases through machine learning. Data in brief 26: 104340.
Narsaiah, K.; Jha, S.N. 2012. Nondestructive methods for quality evaluation of livestock products. Journal of food science and technology 49(3): 342-348.
Zhang, B.; Huang, W.; Li, J.; Zhao, C.; Fan, S.; Wu, J.; Liu, C. 2014. Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review. Food Research International 62: 326-343.
Salazar-Campos, O.; Salazar-Campos, J.; Menacho, D.; Morales D.; Aredo, V. 2019. Improvement of the classification of green asparagus using a Computer Vision System. Brazilian Journal of Food Technology 22: e2018140.
Siche, R.; Vejarano, R.; Aredo, V.; Velasquez, L.; Saldaña, E.; Quevedo, R. 2016. Evaluation of food quality and safety with hyperspectral imaging (HSI). Food Engineering Reviews 8(3): 306-322.
Yan, H.; Barbosa-Cánovas, G.V. 1997. Size characterization of selected food powders by five particle size distribution functions. Food science and technology international 3(5): 361-369.
Tasteatlas. 2020. Citrus Fruit. Recovered of https://www.tasteatlas.com/citrus-fruits.