[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122850-en":3,"doc-seo-122850-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},122850,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",7,"Healthcare","Dates Fruit Disease Recognition using Machine Learning","Date fruit farming contributes substantially to the economies of major producing countries such as Saudi Arabia, Morocco, and Tunisia, yet date trees are vulnerable to disease and quality losses. Frequent field inspection is difficult due to large plantations, and even human-based observation is error-prone and raises processing costs. The work proposes an integrated machine-learning and computer-vision approach for automatic early detection using L*a*b color, statistical, and DWT texture features. A dataset of 871 images across four classes is classified with standard models (RF, MLP, NB, FDT), achieving the best average accuracy by combining all feature types.","Dates Fruit Disease Recognition using Machine  \nLearning  \nGhazanfar Latif Computer Science Department Prince Mohammad Bin Fahd University Khobar, Saudi [Arabiaglatif@pmu.edu.sa](Arabiaglatif@pmu.edu.sa)  \nJaafar Alghazo Computer Engineering Department  \nVirginia Military Institute Lexington, VA, U.S.A[alghazojm@vmi.edu](alghazojm@vmi.edu)  \nGhassen Ben Brahim line 2: Computer Science Department  \nline 3: Prince Mohammad Bin Fahd University Khobar, Saudi Arabia [gbrahim@pmu.edu.sa](gbrahim@pmu.edu.sa)  \nKhalid Alnujaidi Computer Science Department Prince Mohammad Bin Fahd University Khobar, Saudi Arabia [202002530@pmu.edu.sa](202002530@pmu.edu.sa)  \nAbstract—Many countries such as Saudi Arabia, Morocco and Tunisia are among the top exporters and consumers of palm date fruits. Date fruit production plays a major role in the economies of the date fruit exporting countries. Date fruits are susceptible to disease just like any fruit and early detection and intervention can end up saving the produce. However, with the vast farming lands, it is nearly impossible for farmers to observe date trees on a frequent basis for early disease detection. In addition, even with human observation the process is prone to human error and increases the date fruit cost. With the recent advances in computer vision, machine learning, drone technology, and other technologies; an integrated solution can be proposed for the automatic detection of date fruit disease. In this paper, a hybrid features based method with the standard classifiers is proposed based on the extraction of L*a*b color features, statistical features, and Discrete Wavelet Transform (DWT) texture features for the early detection and classification of date fruit disease. Adataset was developed for this work consisting of 871 images divided into the following classes; Healthy date, Initial stage of disease, Malnourished date, and Parasite infected. The extracted features were input to common classifiers such as the Random Forest (RF), Multilayer Perceptron (MLP), Naïve Bayes (NB), and Fuzzy Decision Trees (FDT). The highest average accuracy was achieved when combining the L*a*b, Statistical, and DWT Features.  \nKeywords—Date Fruit Diseases Detection, Date Fruit Classification, Date Malnourished, Hybrid Features, Image Processing, AI in Agriculture.  \nI. INTRODUCTION  \nThroughout the last couple of decades, many advancements have been made in computer vision methods. This has led to a drastic increase in the performance of machine learning processes and the creation of better Artificial Intelligence (AI) solutions. Artificial intelligence has become a crucial technology in our societies, it has been directly involved in the increase of global quality of life. We are now seeing the use of AI and image processing techniques increasingly being implemented in industrial processing, medical imaging, agriculture, and so many more places [1] .  \nSaudi Arabia is one of the leading countries in the production and consumption of palm dates. Date fruits play significant roles both economically and culturally within the country. The date palm (Phoenix dactylifera) is a native plant to the Middle East and North African (MENA) parts of the world. It holds a special place for the people of the MENA region since ancient times. This is mostly due to its special properties of being one of the very few plant species thatcan grow in the hot desert climate and provide such a highly nutritious fruit [2] . The Kingdom is the leading country inthe world in regard to average per capita consumption [3]. In the Qassim region of the country, during cultivation season a huge date festival is held, where people from all neighboring regions visit and indulge in the best quality dates. NicknamingQassim as the date city. It is appearing that date fruit holds cultural significance.  \nThe date fruit plays a big role in the economy of the country as well. The kingdom of Saudi Arabia is home to an estimated 30 million date ","cbCaieNAfYJeEtv4","https://ap.wps.com/l/cbCaieNAfYJeEtv4","pdf",385971,1,"English","en",105,"# Abstract\n# Keywords\n# Introduction","[{\"question\":\"Why is early detection of date fruit disease important?\",\"answer\":\"Early detection enables timely intervention that can prevent produce loss and reduce downstream processing costs caused by disease and quality degradation.\"},{\"question\":\"What features are used for detecting and classifying date fruit diseases?\",\"answer\":\"The approach extracts L*a*b color features, statistical features, and Discrete Wavelet Transform (DWT) texture features to represent disease-related patterns.\"},{\"question\":\"How are the images classified and which combination improves accuracy the most?\",\"answer\":\"Extracted features are input to classifiers including Random Forest, Multilayer Perceptron, Naïve Bayes, and Fuzzy Decision Trees. The highest average accuracy is achieved when combining L*a*b, statistical, and DWT features.\"}]","Dates Fruit Disease Recognition using Machine Learning | PDF",1785813276,18,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"dates-fruit-disease-recognition-using-machine-learning","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/healthcare/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/dates-fruit-disease-recognition-using-machine-learning/122850/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is early detection of date fruit disease important?","Question",{"text":74,"@type":75},"Early detection enables timely intervention that can prevent produce loss and reduce downstream processing costs caused by disease and quality degradation.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What features are used for detecting and classifying date fruit diseases?",{"text":79,"@type":75},"The approach extracts L*a*b color features, statistical features, and Discrete Wavelet Transform (DWT) texture features to represent disease-related patterns.",{"name":81,"@type":72,"acceptedAnswer":82},"How are the images classified and which combination improves accuracy the most?",{"text":83,"@type":75},"Extracted features are input to classifiers including Random Forest, Multilayer Perceptron, Naïve Bayes, and Fuzzy Decision Trees. 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