[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126630-en":3,"doc-seo-126630-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126630,549768064622,"Anda","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Detection of Hollow Heart Disorder in Watermelons Using Vibrational Test and Machine Learning","Internal voids in watermelons negatively affect production costs and reduce consumer confidence. Because invasive inspection is impractical and quality screening is often subjective, a non-destructive approach is required. This study proposes detecting hollow heart disorder in seedless watermelons using vibrational parameters from impact hammer tests combined with machine-learning classifiers. After statistical selection, the first vibrational peak frequency and fruit density are used as predictors, and adding a firmness estimator improves accuracy.","Journal of Agriculture and Food Research 14 (2023) 100779  \nContents lists available at ScienceDirect  \nJournal of Agriculture and Food Research  \njournal [homepage:](homepage: www.sciencedirect.com/journal/journal-of-agriculture-and-food-research)[ www.sciencedirect.com/journal/journal-of-agriculture-and-food-research](homepage: www.sciencedirect.com/journal/journal-of-agriculture-and-food-research)  \n| Detection of hollow heart disorder in watermelons using vibrational test and machine learning |  |  |  |\n| --- | --- | --- | --- |\n| F.J. Sim´on-Portillo, D. Abell´an-L´opez *, M. Fabra-Rodriguez, R. Peral-Orts, M. S´anchez-Lozano Department of Mechanical and Energy Engineering, Miguel Hernandez University of Elche, Avda. de la Universidad, Elche, 03202, Spain |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Watermelon\u003Cbr>Non-destructive testing\u003Cbr>Vibrational method\u003Cbr>Hollow detection\u003Cbr>Classifier algorithms\u003Cbr>Machine learning |  | The presence of internal voids in watermelons has an impact on the costs of producers and on consumer confidence. Various studies have shown that the vibrational parameters of the fruit are related to maturity, quality and the existence of internal defects. A method for the detection of internal voids in seedless watermelons based on vibrational parameters obtained in impact hammer tests and machine learning is presented. After a statistical study of the test results, the frequency of the first peak of the vibrational response and the density of the watermelon are selected as predictors to be used in the classification algorithms. The accuracy of detecting hollow watermelons increases if firmness estimator is introduced as a predictor. Probabilities of success above 89% in the detection of internal voids have been achieved using different classification algorithm. |  |\n\n1. Introduction  \nThe presence of voids in watermelons affects internal texture and taste. These defective watermelons are returned and not charged for, resulting in costs to the producer, complaints and loss of customer confidence. The voids inside the watermelon, a defect known as hollow heart, in some cases reach 50% of the internal volume of the watermelon and can be caused by irregular growth between the centre and the outside. This can occur when atmospheric conditions during growth alternate from wet to dry or when there are large temperature changes [1]. Overwatering or excessive nitrogen fertilisation can also cause this type of defect [2,3].  \nThe marketing of hollow watermelons entails an additional cost for producers. Watermelons cannot be inspected invasively so multiple research projects have been carried out in order to find a non-destructive inspection method [4,5]. Some of these methods are based on vibrational and/or acoustic techniques. On the packing line, or even in the field, there tend to be trained experts responsible for the initial screening of watermelons with internal defects. This method consists of manually tapping each watermelon so that an expert operator can classify it based on the sound produced. Although deeper sounds are associated with the existence of voids, the method is still subjective and imprecise, as well as involving a high cost in terms of time and personnel.  \nAutomating a method for detecting internal defects based on vibrational techniques presents certain difficulties. Firstly, the frequency and  \nmagnitude with which an object responds to vibrational excitation depends on its elasticity, density, size and shape. These properties are highly variable within the same batch of watermelons and depend on factors such as maturation time, water level, rind thickness, etc. Secondly, watermelons of the same variety grown in the same area and season may present internal voids of very different geometries and sizes [1]. Fig. 1 shows various watermelons used in this study with different types and degrees of internal voids. How these voids affect the vibrational cha","cbCainuPxZbjWlro","https://ap.wps.com/l/cbCainuPxZbjWlro","pdf",6749329,3,1,10,"English","en",105,"# Introduction\n## Background and causes of hollow heart\n## Limitations of manual acoustic inspection\n## Challenges in automating vibrational-based detection\n## Prior work on firmness and natural frequencies","[{\"question\":\"What problem does the study address in watermelons?\",\"answer\":\"The study addresses hollow heart disorder, where internal voids alter texture and taste and lead to producer costs and loss of customer confidence.\"},{\"question\":\"How does the proposed method detect hollow heart disorder?\",\"answer\":\"It uses vibrational parameters obtained from impact hammer tests and feeds selected predictors into machine-learning classification algorithms.\"},{\"question\":\"Which predictors improve detection accuracy?\",\"answer\":\"The first peak frequency and watermelon density are selected as predictors, and incorporating a firmness estimator further increases detection performance to success probabilities above 89%.\"}]","Detection of Hollow Heart Disorder in Watermelons Using Vibrational Test and Machine Learning | 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problem does the study address in watermelons?","Question",{"text":76,"@type":77},"The study addresses hollow heart disorder, where internal voids alter texture and taste and lead to producer costs and loss of customer confidence.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed method detect hollow heart disorder?",{"text":81,"@type":77},"It uses vibrational parameters obtained from impact hammer tests and feeds selected predictors into machine-learning classification algorithms.",{"name":83,"@type":74,"acceptedAnswer":84},"Which predictors improve detection accuracy?",{"text":85,"@type":77},"The first peak frequency and watermelon density are selected as predictors, and incorporating a firmness estimator further increases detection performance to success probabilities above 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