[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127543-en":3,"doc-seo-127543-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127543,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Apricot Stone Classification Using Image Analysis and Machine Learning","Apricot stones have high commercial and functional value, and their optimal processing depends on cultivar differences that can be difficult to manage when stones from multiple cultivars are mixed. The study evaluates two low-cost color imaging systems combined with supervised learning to classify apricot stone cultivars. RGB images are captured by flatbed scanner or digital camera, extracting 2,172 texture features across multiple color spaces, followed by feature selection and classifier comparison.","sustainability   \nArticle  \nApricot Stone Classification Using Image Analysis and Machine Learning  \nEwa Ropelewska 1, *, Ahmed M. Rady 2,3 and Nicholas J. Watson 2  \nCitation: Ropelewska, E.; Rady, A.M.; Watson, N.J. Apricot Stone Classification Using Image Analysis and Machine Learning. Sustainability 2023, 15, 9259. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/su15129259](10.3390/su15129259)  \nAcademic Editors: Michael  \nS. Carolan, Jacopo Bacenetti and Ada Margarida Correia Nunes Da Rocha  \nReceived: 14 February 2023  \nRevised: 27 April 2023  \nAccepted: 5 June 2023  \nPublished: 8 June 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Fruit and Vegetable Storage and Processing Department, The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100 Skierniewice, Poland  \n2 Food, Water, Waste Research Group, Faculty of Engineering, University of Nottingham, Nottingham NG7 2RD, UK  \n3 Food Quality and Sensory Science, Teagasc Food Research Centre, Ashtown, D15 KN3K Dublin, Ireland  \n* Correspondence: [ewa.ropelewska@inhort.pl](ewa.ropelewska@inhort.pl)  \nAbstract: Apricot stones have high commercial value and can be used for manufacturing functional foods, cosmetic products, active carbon, and biodiesel. The optimal processing of the stones is dependent on the cultivar and there is a need for methods to sort among different cultivars (which are often mixed in processing facilities) . This study investigates the effectiveness of two low-cost colour imaging systems coupled with supervised learning to develop classification models to determine the cultivar of different stones. Apricot stones of the cultivars `Bella', `Early Orange', `Harcot',`Skierniewicka Słodka', and `Taja' were used. The RGB images were acquired using a flatbed scanner or a digital camera; and 2172 image texture features were extracted within the R, G, B; L, a, b; X, Y, Z; U, and V colour coordinates. The most influential features were determined and resulted in 103 and 89 selected features for the digital camera and the flatbed scanner, respectively. Linear and nonlinear classifiers were applied including Linear Discriminant Analysis (LDA), Decision Trees (DT), k-Nearest Neighbour (kNN), Support Vector Machines (SVM), and Naive Bayes (NB) . The models resulting from the flatbed scanner and using selected features achieved an accuracy of 100% via either quadratic diagonal LDA or kNN classifiers. The models developed using images from the digital camera and all or selected features had an accuracy of up to 96.77% using the SVM classifier. This study presents novel and simple-to-implement at-line (flatbed scanner) and online (digital camera) methodologies for apricot stone sorting. The developed procedure combining colour imaging and machine learning may be used for the authentication of apricot stone cultivars and quality evaluation of apricot from sustainable production.  \nKeywords: apricot stone sorting; flatbed scanner; digital camera; classification models; machine learning  \n1. Introduction  \nApricot (Prunus armeniaca L.) is a stone fruit that is produced widely around the world. The world production of apricot in 2019 was 3,719,974 tonnes [1] . In 2020, the majority of apricot was produced in Asia (65.22%), Europe (20.59%), and Africa (12.16%) with approximately 55.31% produced in Turkey, Uzbekistan, Iran, Algeria, and Italy [1] . Apricots can be consumed fresh or can be processed into dried fruits, jam, marmalades, or fruit bars [2,3] . During fruit processing, apricot stones (i.e., kernels) are removed and utilised in the food, cosmetic, and biodiesel industries for a range of applications including active ca","cbCailQi5E2tTglK","https://ap.wps.com/l/cbCailQi5E2tTglK","pdf",4979727,1,14,"English","en",105,"# Introduction\n## Apricot stones: value and applications\n## Need for cultivar differentiation\n## Imaging and machine learning approach","[{\"question\":\"Why is apricot stone cultivar classification important?\",\"answer\":\"Apricot stones have high commercial value and enable production of functional foods, cosmetics, active carbon, and biodiesel. Processing outcomes depend on cultivar, so mixed cultivars need reliable sorting methods.\"},{\"question\":\"What imaging systems were used to collect data?\",\"answer\":\"The study uses two low-cost color imaging setups: a flatbed scanner for at-line sorting and a digital camera for online sorting.\"},{\"question\":\"Which machine learning methods achieved the best classification accuracy?\",\"answer\":\"For flatbed scanner images with selected features, accuracy reached 100% using quadratic diagonal LDA or kNN. For digital camera images, SVM achieved up to 96.77% accuracy using all or selected features.\"}]","Apricot Stone Classification Using Image Analysis and Machine Learning | PDF",1785939862,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"apricot-stone-classification-using-image-analysis-and-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/apricot-stone-classification-using-image-analysis-and-machine-learning/127543/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is apricot stone cultivar classification important?","Question",{"text":75,"@type":76},"Apricot stones have high commercial value and enable production of functional foods, cosmetics, active carbon, and biodiesel. Processing outcomes depend on cultivar, so mixed cultivars need reliable sorting methods.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What imaging systems were used to collect data?",{"text":80,"@type":76},"The study uses two low-cost color imaging setups: a flatbed scanner for at-line sorting and a digital camera for online sorting.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning methods achieved the best classification accuracy?",{"text":84,"@type":76},"For flatbed scanner images with selected features, accuracy reached 100% using quadratic diagonal LDA or kNN. For digital camera images, SVM achieved up to 96.77% accuracy using all or selected features.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]