{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T18:12:59Z","timestamp":1780683179200,"version":"3.54.1"},"reference-count":45,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2020,12,19]],"date-time":"2020-12-19T00:00:00Z","timestamp":1608336000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Pozna\u0144 University of Life Sciences","award":["506.752.03.00"],"award-info":[{"award-number":["506.752.03.00"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper endeavors to evaluate rapeseed samples obtained in the process of storage experiments with different humidity (12% and 16% seed moisture content) and temperature conditions (25 and 30 \u00b0C). The samples were characterized by different levels of contamination with filamentous fungi. In order to acquire graphic data, the analysis of the morphological structure of rapeseeds was carried out with the use of microscopy. The acquired database was prepared in order to build up training, validation, and test sets. The process of generating a neural model was based on Convolutional Neural Networks (CNN), Multi-Layer Perceptron Networks (MLPN), and Radial Basis Function Networks (RBFN). The classifiers that were compared were devised on the basis of the environments Tensorflow (deep learning) and Statistica (machine learning). As a result, it was possible to achieve the lowest classification error of 14% for the test set, 18% classification error for MLPN, and 21% classification error for RBFN, in the process of recognizing mold in rapeseed with the use of CNN.<\/jats:p>","DOI":"10.3390\/s20247305","type":"journal-article","created":{"date-parts":[[2020,12,21]],"date-time":"2020-12-21T01:01:08Z","timestamp":1608512468000},"page":"7305","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Application of Deep and Machine Learning Using Image Analysis to Detect Fungal Contamination of Rapeseed"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2535-8370","authenticated-orcid":false,"given":"Krzysztof","family":"Przyby\u0142","sequence":"first","affiliation":[{"name":"Food Sciences and Nutrition, Department of Food Technology of Plant Origin, Poznan University of Life Sciences, Wojska Polskiego 31, 60-624 Poznan, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6720-891X","authenticated-orcid":false,"given":"Jolanta","family":"Wawrzyniak","sequence":"additional","affiliation":[{"name":"Food Sciences and Nutrition, Department of Food Technology of Plant Origin, Poznan University of Life Sciences, Wojska Polskiego 31, 60-624 Poznan, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8244-2763","authenticated-orcid":false,"given":"Krzysztof","family":"Koszela","sequence":"additional","affiliation":[{"name":"Department of Biosystems Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-625 Poznan, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Franciszek","family":"Adamski","sequence":"additional","affiliation":[{"name":"Food Sciences and Nutrition, Department of Food Technology of Plant Origin, Poznan University of Life Sciences, Wojska Polskiego 31, 60-624 Poznan, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marzena","family":"Gawrysiak-Witulska","sequence":"additional","affiliation":[{"name":"Food Sciences and Nutrition, Department of Food Technology of Plant Origin, Poznan University of Life Sciences, Wojska Polskiego 31, 60-624 Poznan, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1111\/j.1745-4522.2009.01164.x","article-title":"Degradation of tocopherols during near-ambient rapeseed drying","volume":"16","author":"Siger","year":"2009","journal-title":"J. Food Lipids"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1016\/j.foodchem.2011.01.040","article-title":"Antioxidant capacity, total phenolics, glucosinolates and colour parameters of rapeseed cultivars","volume":"127","author":"Karlovits","year":"2011","journal-title":"Food Chem."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1515\/intag-2015-0078","article-title":"Degradation of tocopherols during rapeseed storage in simulated conditions of industrial silos","volume":"30","author":"Siger","year":"2016","journal-title":"Int. Agrophys."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.jspr.2019.07.011","article-title":"Dynamics of phytosterol degradation in a bulk of rapeseed stored under different temperature and humidity conditions","volume":"83","author":"Wawrzyniak","year":"2019","journal-title":"J. Stored Prod. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/S1360-1385(02)00002-X","article-title":"Vitamin E biosynthesis: Biochemistry meets cell biology","volume":"8","author":"Hofius","year":"2003","journal-title":"Trends Plant Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.indcrop.2009.02.011","article-title":"Comparison of some engineering properties of rapeseed cultivars","volume":"30","author":"Unal","year":"2009","journal-title":"Ind. Crops Prod."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"487","DOI":"10.5650\/jos.55.487","article-title":"Biodiesel: Source, Production, Composition, Properties and Its Benefits","volume":"55","author":"Bajpai","year":"2006","journal-title":"J. Oleo Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1880","DOI":"10.1016\/j.eswa.2014.10.003","article-title":"Discriminating rapeseed varieties using computer vision and machine learning","volume":"42","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"229","DOI":"10.3390\/en11092229","article-title":"Biodiesel by transesterification of rapeseed oil using ultrasound: A kinetic study of base-catalysed reactions","volume":"11","author":"Encinar","year":"2018","journal-title":"Energies"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Santaraite, M., Sendzikiene, E., Makareviciene, V., and Kazancev, K. (2020). Biodiesel production by lipase-catalyzed in situ transesterification of rapeseed oil containing a high free fatty acid content with ethanol in diesel fuel media. Energies, 13.","DOI":"10.3390\/en13102588"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Encinar, J.M., Nogales-Delgado, S., S\u00e1nchez, N., and Gonz\u00e1lez, J.F. (2020). Biolubricants from rapeseed and castor oil transesterification by using titanium isopropoxide as a catalyst: Production and characterization. Catalysts, 10.","DOI":"10.3390\/catal10040366"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1007\/s11746-011-1793-0","article-title":"Changes in Tocochromanol Content in Seeds of Brassica napus L. During Adverse Conditions of Storage","volume":"88","author":"Siger","year":"2011","journal-title":"J. Am. Oil Chem. Soc."},{"key":"ref_13","first-page":"261","article-title":"Influence of storage conditions on microbial quality of rapeseed cake and middlings","volume":"24","author":"Kasprzycka","year":"2010","journal-title":"Int. Agrophys."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.jspr.2014.03.009","article-title":"Experimental study and discrete element method modeling of temperature distributions in rapeseed stored in a model bin","volume":"59","author":"Rusinek","year":"2014","journal-title":"J. Stored Prod. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compag.2010.02.007","article-title":"A review of advanced techniques for detecting plant diseases","volume":"72","author":"Sankaran","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"38878","DOI":"10.1038\/srep38878","article-title":"Detection of Fungus Infection on Petals of Rapeseed (Brassica napus L.) Using NIR Hyperspectral Imaging","volume":"6","author":"Zhao","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1223","DOI":"10.1002\/aocs.12130","article-title":"Management Control Points Related to the Lag Phase of Fungal Growth in a Stored Rapeseed Ecosystem","volume":"95","author":"Wawrzyniak","year":"2018","journal-title":"J. Am. Oil Chem. Soc."},{"key":"ref_18","first-page":"65","article-title":"Relationships Between Fungal Contamination and Some Physicochemical Properties of Rapeseeds","volume":"34","author":"Janda","year":"2015","journal-title":"Ekologia"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.compag.2018.10.033","article-title":"Artificial neural networks and electron microscopy to evaluate the quality of fruit and vegetable spray-dried powders. Case study: Strawberry powder","volume":"155","author":"Koszela","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Koszela, K., Otrz\u0105sek, J., Zaborowicz, M., Boniecki, P., Mueller, W., Raba, B., Lewicki, A., and Przyby\u0142, K. (2014, January 5\u20136). Quality assessment of microwave-vacuum dried material with the use of computer image analysis and neural model. Proceedings of the International Society for Optical Engineering, Athens, Greece.","DOI":"10.1117\/12.2064274"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ma, H., Liu, Y., Ren, Y., Wang, D., Yu, L., and Yu, J. (2020). Improved CNN Classification Method for Groups of Buildings Damaged by Earthquake, Based on High Resolution Remote Sensing Images. Remote Sens., 12.","DOI":"10.3390\/rs12020260"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Przyby\u0142, K., Duda, A., Koszela, K., Stangierski, J., Polarczyk, M., and Gierz, \u0141. (2020). Classification of Dried Strawberry by the Analysis of the Acoustic Sound with Artificial Neural Networks. Sensors, 20.","DOI":"10.3390\/s20020499"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"135","DOI":"10.17221\/427\/2017-CJFS","article-title":"Computer vision and artificial neural network techniques for classification of damage in potatoes during the storage process","volume":"37","author":"Boniecki","year":"2019","journal-title":"Czech J. Food Sci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Boniecki, P., Zaborowicz, M., Pilarska, A., and Piekarska-Boniecka, H. (2020). Identification Process of Selected Graphic Features Apple Tree Pests by Neural Models Type MLP, RBF and DNN. Agriculture, 10.","DOI":"10.3390\/agriculture10060218"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Przyby\u0142, K., Gawa\u0142ek, J., and Koszela, K. (2020). Application of artificial neural network for the quality-based classification of spray-dried rhubarb juice powders. J. Food Sci. Technol.","DOI":"10.1007\/s13197-020-04537-9"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Nowakowski, K., Boniecki, P., Tomczak, R.J., Kujawa, S., and Raba, B. (2012, January 7\u20138). Identification of malting barley varieties using computer image analysis and artificial neural networks. Proceedings of the SPIE\u2014The International Society for Optical Engineering, Kuala Lumpur, Malaysia.","DOI":"10.1117\/12.954155"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kujawa, S., Mazurkiewicz, J., Mueller, W., Gierz, \u0141., Przyby\u0142, K., Wojcieszak, D., Zaborowicz, M., Koszela, K., and Boniecki, P. (2019, January 10\u201313). Identification of co-substrate composted with sewage sludge using convolutional neural networks. Proceedings of the Eleventh International Conference on Digital Image Processing, Guangzhou, China.","DOI":"10.1117\/12.2539800"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1038\/s41438-019-0151-5","article-title":"Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production","volume":"6","author":"Bauer","year":"2019","journal-title":"Hortic. Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"840","DOI":"10.18178\/ijmlc.2019.9.6.881","article-title":"CapsNet, CNN, FCN: Comparative Performance Evaluation for Image Classification","volume":"9","author":"Jiang","year":"2019","journal-title":"Int. J. Mach. Learn. Comput."},{"key":"ref_30","unstructured":"Das, S. (2017). CNN Architectures: LeNet, AlexNet, VGG, GoogLeNet, ResNet and more. Medium, Available online: https:\/\/medium.com\/analytics-vidhya\/cnns-architectures-lenet-alexnet-vgg-googlenet-resnet-and-more-666091488df5."},{"key":"ref_31","first-page":"231","article-title":"Convolution neural network for relation extraction","volume":"Volume 8347","author":"Liu","year":"2013","journal-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"165356","DOI":"10.1016\/j.ijleo.2020.165356","article-title":"Remote sensing image scene classification using CNN-MLP with data augmentation","volume":"221","author":"Shawky","year":"2020","journal-title":"Optik"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1002\/jsfa.5820","article-title":"Kinetics of mould growth in the stored barley ecosystem contaminated with Aspergillus westerdijkiae, Penicillium viridicatum and Fusarium poae at 23\u201330 \u00b0C","volume":"93","author":"Wawrzyniak","year":"2013","journal-title":"J. Sci. Food Agric."},{"key":"ref_34","unstructured":"ASABE (2007). ASABE Standards D245.5. Moisture Relationships of Plant-Based Agricultural Products, American Society of Agricultural Engineers."},{"key":"ref_35","first-page":"259","article-title":"Edge Detection Techniques for Image Segmentation","volume":"3","author":"Muthukrishnan","year":"2011","journal-title":"Int. J. Comput. Sci. Inf. Technol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"163768","DOI":"10.1016\/j.ijleo.2019.163768","article-title":"Multidirectional edge detection based on gradient ghost imaging","volume":"207","author":"Chen","year":"2020","journal-title":"Optik"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Przyby\u0142, K., Ryniecki, A., Niedba\u0142a, G., Mueller, W., Boniecki, P., Zaborowicz, M., Koszela, K., Kujawa, S., and Koz\u0142owski, R.J. (2016, January 20\u201322). Software supporting definition and extraction of the quality parameters of potatoes by using image analysis. Proceedings of the Eighth International Conference on Digital Image Processing, Chengdu, China.","DOI":"10.1117\/12.2244050"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man. Cybern."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"45","DOI":"10.5589\/m02-004","article-title":"An analysis of co-occurrence texture statistics as a function of grey level quantization","volume":"28","author":"Clausi","year":"2002","journal-title":"Can. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1109\/TPAMI.1986.4767760","article-title":"Sum and Difference Histograms for Texture Classification","volume":"PAMI-8","author":"Unser","year":"1986","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1109\/36.752194","article-title":"Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices","volume":"37","author":"Soh","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"393","DOI":"10.17221\/82\/2009-CJFS","article-title":"Eggshell crack detection based on acoustic impulse response and supervised pattern recognition","volume":"27","author":"Lin","year":"2009","journal-title":"Czech J. Food Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1006\/jaer.2000.0658","article-title":"AE\u2014Automation and Emerging Technologies: Co-occurrence Matrix Texture Features of Multi-spectral Images on Poultry Carcasses","volume":"78","author":"Park","year":"2001","journal-title":"J. Agric. Eng. Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3351","DOI":"10.1086\/316861","article-title":"Removing Radio Interference from Contaminated Astronomical Spectra Using an Independent Reference Signal and Closure Relations","volume":"120","author":"Briggs","year":"2000","journal-title":"Astron. J."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Nelli, F., and Nelli, F. (2018). Deep Learning with TensorFlow. Python Data Analytics, Apress.","DOI":"10.1007\/978-1-4842-3913-1"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/24\/7305\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:47:17Z","timestamp":1760179637000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/24\/7305"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,19]]},"references-count":45,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["s20247305"],"URL":"https:\/\/doi.org\/10.3390\/s20247305","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,19]]}}}