[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126123-en":3,"doc-seo-126123-105":31,"detail-sidebar-cat-0-en-105":93},{"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},126123,5909887254083,"Miles","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","A Machine Learning Based Framework for a Stage-Wise Classification of Date Palm White Scale Disease - Research paper","Date palm production underpins oasis agriculture through economic value and nutritional benefits, yet white scale disease threatens trees as infestations advance and can ultimately lead to death. Timely, accurate detection of infected leaves and infestation stage is essential for deciding whether chemical control is warranted, reducing yield losses while protecting fruit quality. A framework is proposed using GLCM texture features and HSV color-moment features from leaf images, with multiclass ML models trained and evaluated on separate feature sets. Results show strong performance, with SVM achieving the highest accuracy on combined descriptors.","BIG DATA MINING AND ANALYTICS  \nISSN 2096-0654 01/10 pp263 –272  \nVolume 6, Number 3, September 2023  \nDOI: 10.26599/BDMA.2022.9020022  \nA Machine Learning Based Framework for a Stage-Wise Classification  \nof Date Palm White Scale Disease  \nAbdelaaziz Hessane, Ahmed El Youssefi, Yousef Farhaoui􀀃 , Badraddine Aghoutane, and Fatima Amounas  \nAbstract: Date palm production is critical to oasis agriculture, owing to its economic importance and nutritional advantages. Numerous diseases endanger this precious tree, putting a strain on the economy and environment.  \nWhite scale Parlatoria blanchardi is a damaging bug that degrades the quality of dates. When an infestation reaches a specific degree, it might result in the tree's death. To counter this threat, precise detection of infected leaves and its infestation degree is important to decide if chemical treatment is necessary. This decision is crucial for farmers who wish to minimize yield losses while preserving production quality. For this purpose, we propose a feature extraction and machine learning (ML) technique based framework for classifying the stages of infestation by white scale disease (WSD) in date palm trees by investigating their leaflets images. 80 gray level co-occurrence matrix (GLCM) texture features and 9 hue, saturation, and value (HSV) color moments features are extracted from both grayscale and color images of the used dataset. To classify the WSD into its four classes (healthy, low infestation degree, medium infestation degree, and high infestation degree), two types of ML algorithms were tested; classical machine learning methods, namely, support vector machine (SVM) and k-nearest neighbors (KNN), and ensemble learning methods such as random forest (RF) and light gradient boosting machine (LightGBM) . The ML models were trained and evaluated using two datasets: the first is composed of the extracted GLCM features only, and the second combines GLCM and HSV descriptors. The results indicate that SVM classifier outperformed on combined GLCMand HSV features with an accuracy of 98.29% . The proposed framework could be beneficial to the oasis agricultural community in terms of early detection of date palm white scale disease (DPWSD) and assisting in the adoption of preventive measures to protect both date palm trees and crop yield.  \nKey words: precision agriculture; machine learning; ensemble learning; feature extraction; date palm; diseases  \n1 Introduction  \nThe date palm is a crop with significant socioeconomic and ecological importance in Morocco[1] . The latter has been prioritized by Morocco's Green Plan (MGP)  \nthrough promoting and helping farmers in growing these trees, which account for around 60% of agricultural revenue in oasis and employ over two million people[2] . Plant disease detection and classification  \n􀀏 Abdelaaziz Hessane, Ahmed El Youssefi, and Yousef Farhaoui are with the STI Laboratory, IDMS, Faculty of Sciences and Techniques, Moulay Ismail University of Meknes, Errachidia 52000, Morocco. E-mail: [a.hessane@edu.umi.ac.ma](a.hessane@edu.umi.ac.ma); ah.elyoussefi@[edu.umi.ac.ma](edu.umi.ac.ma); [y.farhaoui@fste.umi.ac.ma](y.farhaoui@fste.umi.ac.ma).  \n􀀏 Badraddine Aghoutane is with the IA Laboratory, Department of Computer Science, Faculty of Sciences, Moulay Ismail University of Meknes, Meknes 50070, Morocco. E-mail: [b.aghoutane@umi.ac.ma](b.aghoutane@umi.ac.ma).  \n􀀏 Fatima Amounas is with RO.AL&I Group, Computer Sciences Department, Faculty of Sciences and Techniques, Moulay Ismail University of Meknes, Errachidia 52000, Morocco. E-mail: [f](f amounas@yahoo.fr)[ ](f amounas@yahoo.fr)[amounas@yahoo.fr](f amounas@yahoo.fr).  \n* To whom correspondence should be addressed.  \nManuscript received: 2022-06-18; accepted: 2022-07-06  \n The author(s) 2023 . The articles published in this open access journal are distributed under the terms of the  \nCreative Commons Attribution 4.0 International License ([http://creativecommons.org/licenses/by/4.0/](http://cr","cbCaihjggEBIctzX","https://ap.wps.com/l/cbCaihjggEBIctzX","pdf",1887335,6,1,10,"English","en",105,"# Introduction\n## Problem background and motivation\n## Study aim and proposed approach","[{\"question\":\"What disease is targeted and why is stage-wise classification important?\",\"answer\":\"The framework targets date palm white scale disease caused by Parlatoria blanchardi. Stage-wise classification helps farmers decide whether intervention such as chemical treatment is needed to avoid yield loss and quality degradation.\"},{\"question\":\"Which image features are extracted for machine learning?\",\"answer\":\"The method extracts 80 gray-level co-occurrence matrix (GLCM) texture features and 9 HSV color-moment features, computed from grayscale and color leaf images.\"},{\"question\":\"Which machine learning models were evaluated and what was the best result?\",\"answer\":\"Classical models include SVM and KNN, while ensemble models include random forest (RF) and LightGBM. The SVM classifier achieved the best accuracy of 98.29% using combined GLCM and HSV features.\"}]","A Machine Learning Based Framework for a Stage-Wise Classification of Date Palm White Scale Disease - Research paper | PDF",1785903281,25,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"a-machine-learning-based-framework-for-a-stage-wise-classification-of-date-palm-white-scale-disease-research-paper","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/a-machine-learning-based-framework-for-a-stage-wise-classification-of-date-palm-white-scale-disease-research-paper/126123/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-25","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What disease is targeted and why is stage-wise classification important?","Question",{"text":77,"@type":78},"The framework targets date palm white scale disease caused by Parlatoria blanchardi. Stage-wise classification helps farmers decide whether intervention such as chemical treatment is needed to avoid yield loss and quality degradation.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which image features are extracted for machine learning?",{"text":82,"@type":78},"The method extracts 80 gray-level co-occurrence matrix (GLCM) texture features and 9 HSV color-moment features, computed from grayscale and color leaf images.",{"name":84,"@type":75,"acceptedAnswer":85},"Which machine learning models were evaluated and what was the best result?",{"text":86,"@type":78},"Classical models include SVM and KNN, while ensemble models include random forest (RF) and LightGBM. 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