{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T21:06:16Z","timestamp":1776978376535,"version":"3.51.4"},"reference-count":32,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:00:00Z","timestamp":1732665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Centre and the European Union\u2019s Horizon 2020 research","award":["2022\/45\/P\/NZ9\/03904"],"award-info":[{"award-number":["2022\/45\/P\/NZ9\/03904"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The emergence of new technologies focusing on \u201ccomputer vision\u201d has contributed significantly to the assessment of fruit quality. In this study, an innovative approach based on image analysis was used to assess the external quality of fresh and frozen \u2018Mejhoul\u2019 and \u2018Boufeggous\u2019 date palm cultivars stored for 6 months at \u221210 \u00b0C and \u221218 \u00b0C. Their quality was evaluated, in a non-destructive manner, based on texture features extracted from images acquired using a digital camera and flatbed scanner. The whole process of image processing was carried out using MATLAB R2024a and Q-MAZDA 23.10 software. Then, extracted features were used as inputs for pre-established algorithms\u2013groups within WEKA 3.9 software to classify frozen date fruit samples after 0, 2, 4, and 6 months of storage. Among 599 features, only 5 to 36 attributes were selected as powerful predictors to build desired classification models based on the \u201cFunctions-Logistic\u201d classifier. The general architecture exhibited clear differences in classification accuracy depending mainly on the frozen storage period and imaging device. Accordingly, confusion matrices showed high classification accuracy (CA), which could reach 0.84 at M0 for both cultivars at the two frozen storage temperatures. This CA indicated a remarkable decrease at M2 and M4 before re-increasing by M6, confirming slight changes in external quality before the end of storage. Moreover, the developed models on the basis of flatbed scanner use allowed us to obtain a high correctness rate that could attain 97.7% in comparison to the digital camera, which did not exceed 85.5%. In perspectives, physicochemical attributes can be added to developed models to establish correlation with image features and predict the behavior of date fruit under storage.<\/jats:p>","DOI":"10.3390\/s24237560","type":"journal-article","created":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T10:05:56Z","timestamp":1732701956000},"page":"7560","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Non-Destructive Monitoring of External Quality of Date Palm Fruit (Phoenix dactylifera L.) During Frozen Storage Using Digital Camera and Flatbed Scanner"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3310-3260","authenticated-orcid":false,"given":"Younes","family":"Noutfia","sequence":"first","affiliation":[{"name":"Fruit and Vegetable Storage and Processing Department, The National Institute of Horticultural Research, Konstytucji 3 Maja 1\/3, 96-100 Skierniewice, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8891-236X","authenticated-orcid":false,"given":"Ewa","family":"Ropelewska","sequence":"additional","affiliation":[{"name":"Fruit and Vegetable Storage and Processing Department, The National Institute of Horticultural Research, Konstytucji 3 Maja 1\/3, 96-100 Skierniewice, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6552-2051","authenticated-orcid":false,"given":"Zbigniew","family":"J\u00f3\u017awiak","sequence":"additional","affiliation":[{"name":"Fruit and Vegetable Storage and Processing Department, The National Institute of Horticultural Research, Konstytucji 3 Maja 1\/3, 96-100 Skierniewice, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1042-1098","authenticated-orcid":false,"given":"Krzysztof","family":"Rutkowski","sequence":"additional","affiliation":[{"name":"Fruit and Vegetable Storage and Processing Department, The National Institute of Horticultural Research, Konstytucji 3 Maja 1\/3, 96-100 Skierniewice, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.tifs.2023.03.003","article-title":"Postharvest quality, technologies, and strategies to reduce losses along the supply chain of banana: A review","volume":"134","author":"Pathare","year":"2023","journal-title":"Trends Food Sci. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1016\/j.tifs.2021.01.073","article-title":"Gas sensor technologies and mathematical modelling for quality sensing in fruit and vegetable cold chains: A review","volume":"110","author":"Wang","year":"2021","journal-title":"Trends Food Sci. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"128178","DOI":"10.1016\/j.foodchem.2020.128178","article-title":"Oligosaccharide is a promising natural preservative for improving postharvest preservation of fruit: A review","volume":"341","author":"Bose","year":"2021","journal-title":"Food Chem."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Al-Dairi, M., Pathare, P.B., and Al-Yahyai, R. (2021). Effect of postharvest transport and storage on color and firmness quality of tomato. Horticulturae, 7.","DOI":"10.3390\/horticulturae7070163"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"110629","DOI":"10.1016\/j.foodres.2021.110629","article-title":"Edible coatings and films with incorporation of prebiotics\u2014A review","volume":"148","author":"Paulo","year":"2021","journal-title":"Food Res. Int."},{"key":"ref_6","first-page":"30","article-title":"Technologies post-r\u00e9colte pour la pr\u00e9servation de la qualit\u00e9 des dattes durant le stockage","volume":"134","author":"Misbah","year":"2022","journal-title":"Afr. Mediterr. Agric. J.-Al Awamia"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1156","DOI":"10.1007\/s11694-018-9729-0","article-title":"Effects of calcium chloride and salicylic acid postharvest treatments on the quality of Khalal Barhi dates at different ripening levels during cold storage","volume":"12","author":"Atia","year":"2018","journal-title":"J. Food Meas. Charact."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1515\/rtuect-2017-0008","article-title":"Low temperature refrigeration as an alternative anti-pest treatment of dates","volume":"20","author":"Lallouche","year":"2017","journal-title":"Environ. Clim. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4408","DOI":"10.1016\/j.sjbs.2021.04.035","article-title":"Commercial techniques for preserving date palm (Phoenix dactylifera) fruit quality and safety: A review","volume":"28","author":"Sarraf","year":"2021","journal-title":"Saudi J. Biol. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ahmed, A.R., Aleid, S.M., and Mohammed, M. (2023). Impact of Modified Atmosphere Packaging Conditions on Quality of Dates: Experimental Study and Predictive Analysis Using Artificial Neural Networks. Foods, 12.","DOI":"10.3390\/foods12203811"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"108774","DOI":"10.1016\/j.compag.2024.108774","article-title":"Dynamic visual servo control methods for continuous operation of a fruit harvesting robot working throughout an orchard","volume":"219","author":"Chen","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Noutfia, Y., and Ropelewska, E. (2024). What can artificial intelligence approaches bring to an improved and efficient harvesting and postharvest handling of date fruit (Phoenix dactylifera L.)? A review. Postharvest Biol. Technol., 213.","DOI":"10.1016\/j.postharvbio.2024.112926"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1016\/j.tifs.2021.12.021","article-title":"Emerging non-destructive imaging techniques for fruit damage detection: Image processing and analysis","volume":"120","author":"Mahanti","year":"2022","journal-title":"Trends Food Sci. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Mohammed, M., Munir, M., and Aljabr, A. (2022). Prediction of date fruit quality attributes during cold storage based on their electrical properties using artificial neural networks models. Foods, 11.","DOI":"10.3390\/foods11111666"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mohammed, M., Srinivasagan, R., Alzahrani, A., and Alqahtani, N.K. (2023). Machine-Learning-Based Spectroscopic Technique for Non-Destructive Estimation of Shelf Life and Quality of Fresh Fruits Packaged under Modified Atmospheres. Sustainability, 15.","DOI":"10.3390\/su151712871"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Srinivasagan, R., Mohammed, M., and Alzahrani, A. (2023). TinyML-sensor for shelf life estimation of fresh date fruits. Sensors, 23.","DOI":"10.3390\/s23167081"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Szczypi\u0144ski, P.M., Klepaczko, A., and Kocio\u0142ek, M. (2017, January 20\u201322). QMaZda\u2014Software tools for image analysis and pattern recognition. Proceedings of the 2017 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), Poznan, Poland.","DOI":"10.23919\/SPA.2017.8166867"},{"key":"ref_18","unstructured":"Jain, A.K. (1989). Fundamentals of Digital Image Processing, Prentice-Hall."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2091","DOI":"10.1109\/29.45554","article-title":"Multifrequency channel decompositions of images and wavelet models","volume":"37","author":"Mallat","year":"1989","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Noutfia, Y., and Ropelewska, E. (2023). Innovative Models Built Based on Image Textures Using Traditional Machine Learning Algorithms for Distinguishing Different Varieties of Moroccan Date Palm Fruit (Phoenix dactylifera L.). Agriculture, 13.","DOI":"10.3390\/agriculture13010026"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ropelewska, E., Szwejda-Grzybowska, J., Wrzodak, A., and Mieszczakowska-Fr\u0105c, M. (2024). Non-Destructive Monitoring of Sweet Pepper Samples After Selected Periods of Lacto-Fermentation. Agriculture, 14.","DOI":"10.3390\/agriculture14111855"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Shahi, T.B., Sitaula, C., Neupane, A., and Guo, W. (2022). Fruit classification using attention-based MobileNetV2 for industrial applications. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0264586"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Koz\u0142owski, M., Szczypi\u0144ski, P.M., Reiner, J., Lampa, P., Mrzyg\u0142\u00f3d, M., Szturo, K., and Zapotoczny, P. (2024). Identifying defects and varieties of Malting Barley Kernels. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-73683-3"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.tifs.2021.09.014","article-title":"A concise review on food quality assessment using digital image processing","volume":"118","author":"Meenu","year":"2021","journal-title":"Trends Food Sci. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e11373","DOI":"10.1002\/aps3.11373","article-title":"Instance segmentation for the fine detection of crop and weed plants by precision agricultural robots","volume":"8","author":"Champ","year":"2020","journal-title":"Appl. Plant Sci."},{"key":"ref_26","first-page":"23","article-title":"Understanding how noise affects the accuracy of CNN image classification","volume":"5","author":"Lau","year":"2021","journal-title":"J. Appl. Technol. Innov."},{"key":"ref_27","first-page":"28","article-title":"A computer vision system for defect discrimination and grading in tomatoes using machine learning and image processing","volume":"2","author":"Ireri","year":"2019","journal-title":"Artif. Intell. Agric."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tsouvaltzis, P., Babellahi, F., Amodio, M.L., and Colelli, G. (2020). Early detection of eggplant fruit stored at chilling temperature using different non-destructive optical techniques and supervised classification algorithms. Postharvest Biol. Technol., 159.","DOI":"10.1016\/j.postharvbio.2019.111001"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ropelewska, E. (2022). Assessment of the influence of storage conditions and time on red currants (Ribes rubrum L.) using image processing and traditional machine learning. Agriculture, 12.","DOI":"10.3390\/agriculture12101730"},{"key":"ref_30","first-page":"9","article-title":"Cryogenic freezing of fresh date fruits for quality preservation during frozen storage","volume":"17","author":"Alhamdan","year":"2018","journal-title":"J. Saudi Soc. Agric. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e14938","DOI":"10.1111\/jfpp.14938","article-title":"Effect of freezing rate on the quality properties of Medjool dates at the tamr stage","volume":"44","author":"Mowafy","year":"2020","journal-title":"J. Food Process. Preserv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e14228","DOI":"10.1111\/jfpp.14228","article-title":"Assessment of physico-chemical and sensory properties of two date (Phoenix dactylifera L.) cultivars under commercial cold storage conditions","volume":"43","author":"Noutfia","year":"2019","journal-title":"J. Food Process. Preserv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/23\/7560\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:40:12Z","timestamp":1760114412000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/23\/7560"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,27]]},"references-count":32,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["s24237560"],"URL":"https:\/\/doi.org\/10.3390\/s24237560","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,27]]}}}