[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123760-en":3,"doc-seo-123760-105":30,"detail-sidebar-cat-0-en-105":95},{"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},123760,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Modelling of Ladyfinger Plantations Using Open Data Malaysia - A Comparative Study of Machine Learning Techniques","This study investigates how open data from Open Data Malaysia can support agricultural classification modeling focused on ladyfinger plantations. Climate variables are integrated with agricultural data to build predictive models for crop yield and sustainable agriculture decision-making. Four machine learning techniques—Naïve Bayes, SVM, KNN, and decision tree—are evaluated using multiple performance metrics. Results show Naïve Bayes provides the strongest predictive performance, while decision tree underperforms. Incorporating climate data improves classification, while the small dataset size limits accuracy and generalizability, motivating larger and more diverse datasets and future crop-comparative research.","Modelling of Ladyfinger Plantations Using Open Data Malaysia: A Comparative Study of Machine Learning Techniques  \nMohamad Farhan Mohamad Mohsin1,2*, Mohamad Ghozali Hassan2, Kamal Imran Mohd Sharif2, Mohd Azril Bin Ismail2, Syairah Aimi Shahron2, Noor Ashri Ja’afar2  \n1School of Computing,  \nUniversiti Utara Malaysia, Kedah, MALAYSIA  \n2School of Technology Management and Logistics, Universiti Utara Malaysia, Kedah, MALAYSIA  \n*Corresponding Author  \nDOI: [https://doi.org/10.30880/emait.2023.04.02.005](https://doi.org/10.30880/emait.2023.04.02.005)  \nReceived 10 October 2023; Accepted 13 December 2023; Available online 31 December 2023  \nAbstract: This study investigates the potential of using open data from Open Data Malaysia to develop classification models for agricultural practices specifically focusing on ladyfinger plantations. The integration of climate data with agricultural data is performed to build predictive models for crop yield prediction for sustainable agriculture. Four machine learning models, namely Naïve Bayes, SVM, KNN, and decision tree, are evaluated based on various performance metrics. The ladyfinger dataset was obtained from Open Data Malaysia containing both climate and agricultural data. was preprocessed and mined. The results indicate that the Naïve Bayes model achieves the highest performance making it the most suitable model for predicting ladyfinger yield. The decision tree model performed poorly and may not be suitable for this type of classification task. This study highlights two important findings. Firtstly, the inclusion of climate data significantly improved the classification performance of the models. Secondly the limited size of the ladyfinger dataset emphasizes the need for larger and more diverse datasets to enhance the accuracy and generalizability of predictive models in agriculture. Open data initiatives are important for providing researchers with data however larger and more diverse datasets are needed to improve model accuracy. Future research could investigate machine learning models for predicting crop yields in different crops with various climate and agricultural data combinations.  \nKeywords: Agriculture, classification, data mining, machine learning, prediction  \n1. Introduction  \nThe agriculture sector plays a crucial role in Malaysia's economy and there is increasing interest in utilizing data mining techniques to improve agricultural productivity [1],[2] . Open data has the potential to facilitate such efforts by providing researchers and practitioners with access to diverse datasets including from various organizations. Open Data Malaysia is a key platform for sharing open data across various sectors in Malaysia [3] . It provides a wide range of datasets including agriculture such data on crop production, land use, and weather patterns.  \nIn recent years, the Malaysian government has been actively promoting the use of open data in research and decision-making, launching the Malaysian Public Sector Open Data Portal in 2015, which later became integrated into the Open Data Malaysia platform [4] . As a result, the platform has recorded a significant number in users reaching a total of 1156339 in September2023 . Currently, there are more than 12,500 datasets (780 related to agriculture) and 403  \ndata providers available on the platform [5] . This is an indicator of the growing availability of open data in Malaysia including agriculture and climate research fields. The success story of this services can be seen in various practical application developed using the shared data such Mobile Trainer, My Transplant Diary, Kitar, OurAuthority, and more [6] .  \nIn agriculture, climate variables such as temperature, rainfall, and humidity have a significant impact on agricultural outcomes [7] . Integrating climate data with agricultural data can provide valuable insights into predicting crop yields and informing decision-making in the agriculture sector [8],[9]. Moreover, climate v","cbCaigQP1YIgAhir","https://ap.wps.com/l/cbCaigQP1YIgAhir","pdf",477545,1,9,"English","en",105,"# Introduction\n## Open data in Malaysia and agriculture\n## Climate variables and integration with agricultural data\n## Study aim and machine learning approach\n## Paper structure","[{\"question\":\"What open data source is used for the ladyfinger modeling study?\",\"answer\":\"The study uses datasets obtained from Open Data Malaysia, including both climate data and agricultural data related to ladyfinger plantations.\"},{\"question\":\"Which machine learning models are compared in the classification task?\",\"answer\":\"The compared models are Naïve Bayes, SVM, KNN, and decision tree, evaluated using several performance metrics.\"},{\"question\":\"What is the main finding about the best-performing model?\",\"answer\":\"Naïve Bayes achieves the highest performance and is identified as the most suitable model for predicting ladyfinger yield, whereas the decision tree model performs poorly.\"},{\"question\":\"How do climate data and dataset size affect model performance?\",\"answer\":\"Including climate data significantly improves classification performance. Limited ladyfinger dataset size reduces accuracy and generalizability, indicating a need for larger and more diverse datasets.\"}]","Modelling of Ladyfinger Plantations Using Open Data Malaysia - A Comparative Study of Machine Learning Techniques | PDF",1785818382,23,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":28},"modelling-of-ladyfinger-plantations-using-open-data-malaysia-a-comparative-study-of-machine-learning-techniques","",{"@graph":36,"@context":89},[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/modelling-of-ladyfinger-plantations-using-open-data-malaysia-a-comparative-study-of-machine-learning-techniques/123760/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What open data source is used for the ladyfinger modeling study?","Question",{"text":75,"@type":76},"The study uses datasets obtained from Open Data Malaysia, including both climate data and agricultural data related to ladyfinger plantations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are compared in the classification task?",{"text":80,"@type":76},"The compared models are Naïve Bayes, SVM, KNN, and decision tree, evaluated using several performance metrics.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the main finding about the best-performing model?",{"text":84,"@type":76},"Naïve Bayes achieves the highest performance and is identified as the most suitable model for predicting ladyfinger yield, whereas the decision tree model performs poorly.",{"name":86,"@type":73,"acceptedAnswer":87},"How do climate data and dataset size affect model performance?",{"text":88,"@type":76},"Including climate data significantly improves classification performance. 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