[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117459-en":3,"doc-seo-117459-105":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},117459,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Assessment of Macadamia Nutrients using Hyperspectral Data and Machine Learning","Growing demand for high-quality macadamia products requires efficient nutrient analysis to preserve product quality and nutritional integrity. Compared with destructive, costly, and slow laboratory methods, hyperspectral imaging (HSI) enables non-destructive and rapid assessment. This work evaluates Vis/NIR models for predicting nutrient concentrations in macadamia kernels, studies how test/train data proportioning impacts accuracy, and examines smoothing-based removal of problematic spectra. Machine learning models including ANN, PLSR, KNN regression, and random forest regression are combined with Relief, MDI, and Boruta feature selection; MDI shows promising results with fewer features for nutrients such as Mn and Na.","Assessment of Macadamia Nutrients Using Hyperspectral Data and Machine Learning  \nAuthor  \nKhan , WQ , Farrar, M , Awrangjeb , M , Bai , SH , Trueman , SJ , Wallace , H , Richards , TE , Arshid , W  \nPublished 2024  \nConference Title  \n2024 International Conference on Digital Image Computing: Techniques and Applications (DICTA)  \nVersion  \nAccepted Manuscript (AM)  \nDOI  \n10. 1109/DICTA63115 .2024.00083  \nRights statement  \nThis work is covered by copyright. You must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a specified licence , refer to the licence for details of permitted re-use. If you believe that this work infringes copyright please make a copyright takedown request using the form at [https://www.griffith.edu.au/copyright-matters](https://www.griffith.edu.au/copyright-matters).  \nDownloaded from  \n[https://hdl.handle.net/10072/435949](https://hdl.handle.net/10072/435949)  \nGriffith Research Online  \n[https://research-repository.griffith.edu.au](https://research-repository.griffith.edu.au)  \nAssessment of Macadamia Nutrients using Hyperspectral Data and Machine Learning  \nWafa Qaiser Khan 1 , Michael Farrar2 , Mohammad Awrangjeb 1 , Shahla Hosseini Bai2 , Stephen J. Trueman2 , Helen Wallace3 , Tarran E. Richards4 , Waqas Arshid 1  \n1 School of Information and Communication Technology, Griffith University Nathan Campus, Australia  \nwafaqaiser.khan@griffithuni.edu.au, m.awrangjeb@griffith.edu.au, waqas.arshid@griffithuni.edu.au  \n2Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University Nathan Campus, Australia m.farrar@griffith.edu.au, s.hosseini-bai@griffith.edu.au, s.trueman@griffith.edu.au  \n3 School of Biology & Environmental Science, Queensland University of Technology, Australia  \n[helen.wallace@qut.edu.au](helen.wallace@qut.edu.au)  \n4Corteva Agriscience, Australia  \n[tarran.richards@corteva.com](tarran.richards@corteva.com)  \nAbstract—With the increasing demand for high-quality macadamia products, there is a pressing need for efficient and accurate nutrient analysis methods to maintain product quality and nutritional integrity. Compared to existing nutrient analysis methods which are destructive, expensive, and slow, hyperspectral imaging (HSI) offers a promising solution providing non-destructive and rapid analysis capabilities. This research focuses on three primary research questions. First, we assess the predictive capabilities of visible to near-infrared (Vis/NIR) for determining nutrient concentrations in macadamia kernels. Second, we examine how data proportioning, particularly the test/train split, affects model prediction accuracy, demonstrating improved accuracy with optimised data proportioning. Third, we investigate the effectiveness of smoothing functions in eliminating problematic spectra and enhancing model accuracy, finding no success in removing light-polluted samples. We utilise various machine learning algorithms, including artificial neural network (ANN), partial least squares regression (PLSR), k nearest neighbors (KNN) regressor, and random forest regressor. Additionally, we explored three feature selection algorithms: Relief, mean decrease impurity (MDI), and Boruta, to refine our predictive models and enhance their accuracy. The research showed that MDI achieved acceptable results for certain nutrients (e.g., Mn, Na) with fewer features compared to Relief, indicating a potential advantage in feature efficiency.  \nIndex Terms—hyperspectral imaging, machine learning, macadamia integrifolia, nutrients  \nI. INTRODUCTION  \nMacadamia trees, part of the Proteaceae family and native to northeastern Australia, include species like M. integrifolia, M. tetraphylla, and their hybrids [1] . These trees support the world’s seventh-largest tree-nut industry, significantly contributing to the annual production of 5.3 million tons of tree nuts [","cbCaiq64xWJ999V4","https://ap.wps.com/l/cbCaiq64xWJ999V4","pdf",869356,1,9,"English","en",105,"# Introduction\n# Literature Review\n# Abstract and Index Terms","[{\"question\":\"Why hyperspectral imaging is used for macadamia nutrient assessment?\",\"answer\":\"HSI provides non-destructive, rapid nutrient analysis, offering an alternative to destructive, expensive, and slow traditional methods.\"},{\"question\":\"What variables were studied to improve model prediction accuracy?\",\"answer\":\"The research examines the predictive capability of Vis/NIR spectra, how test/train split proportioning affects accuracy, and whether smoothing functions can remove problematic spectra.\"},{\"question\":\"Which machine learning and feature selection methods were evaluated?\",\"answer\":\"Models included ANN, PLSR, KNN regressor, and random forest regressor. Feature selection methods included Relief, mean decrease impurity (MDI), and Boruta.\"}]","Assessment of Macadamia Nutrients using Hyperspectral Data and Machine Learning | PDF",1785675953,23,{"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},"assessment-of-macadamia-nutrients-using-hyperspectral-data-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/assessment-of-macadamia-nutrients-using-hyperspectral-data-and-machine-learning/117459/",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","2026-08-02",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 hyperspectral imaging is used for macadamia nutrient assessment?","Question",{"text":75,"@type":76},"HSI provides non-destructive, rapid nutrient analysis, offering an alternative to destructive, expensive, and slow traditional methods.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What variables were studied to improve model prediction accuracy?",{"text":80,"@type":76},"The research examines the predictive capability of Vis/NIR spectra, how test/train split proportioning affects accuracy, and whether smoothing functions can remove problematic spectra.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning and feature selection methods were evaluated?",{"text":84,"@type":76},"Models included ANN, PLSR, KNN regressor, and random forest regressor. 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