[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123193-en":3,"doc-seo-123193-105":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},123193,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Optimizing precision farming - enhancing machine learning efficiency with robust regression techniques in high-dimensional data","Smart precision farming leverages IoT, cloud computing, and big data to optimize agricultural productivity while lowering costs and supporting sustainability via digitalization and intelligent workflows. Key barriers include complex variable management, multicollinearity, outliers, model robustness, and limited small-to-medium sample sizes. Improving machine learning efficiency requires reducing retraining time and resolving modeling complexity, particularly for large high-dimensional datasets. A study with 435 drying parameters and 1,914 observations compares Ridge and Lasso with robust regression (S, M, MM, M-Hampel, M-Huber, M-Tukey and related variants), selecting models with the best MAPE, MSE, SSE and highest R2.","J. Nig. Soc. Phys. Sci. 7 (2025) 2314  \nJournal of the Nigerian Society of Physical Sciences  \nOptimizing precision farming: enhancing machine learning efficiency with robust regression techniques in high-dimensional  \ndata  \nNour Hamad Abu Afouna, Majid Khan Majahar Ali ∗  \nSchool of Mathematical Sciences, Universiti Sains Malaysia 11800 USM, Penang, Malaysia  \nAbstract  \nSmart precision farming leverages IoT, cloud computing, and big data to optimize agricultural productivity, lower costs, and promote sustainability through digitalization and intelligent methodologies. However, it faces challenges such as managing complex variables, addressing multicollinearity, handling outliers, ensuring model robustness, and enhancing accuracy, particularly with small to medium-sized datasets. To overcome these obstacles, reducing retraining time and resolving the complexity issue is essential for improving the machine learning algorithm’s performance, scalability, and efficiency, especially when dealing with large or high-dimensional datasets. In a recent study involving 435 drying parameters and 1,914 observations, two machine learning algorithms-Ridge and Lasso-were employed to analyze and compare the impact of two variable selection techniques, specifically the regularization methods Ridge and Lasso, before and after addressing heterogeneity in highly ranked variables (50, 100, 150, 200, 250, 300) . Additionally, robust regression methods such as S, M, MM, M-Hampel, M-Huber, M-Tukey, MM-bisquare, MM-Hampel, and MM-Huber were applied. The results demonstrated that the robust methods, when applied to Ridge and Lasso, achieved the highest efficiency, with the smallest values for MAPE, MSE, SSE, and the highest R2 values, both before and after accounting for heterogeneity. As a result of the study, the best models are the Ridge model with the MM bisquares before heterogeneity, the Ridge model with the MM method after heterogeneity, and the Lasso model with the MM method before heterogeneity and the Lasso model with MM Hampel after heterogeneity.  \nDOI:10.46481/jnsps.2025.2314  \nKeywords: Lasso, Ridge, M-estimation, MM-estimation, Robust Regression  \nArticle History :  \nReceived: 15 August 2024  \nReceived in revised form: 18 October 2024  \nAccepted for publication: 31 October 2024  \nAvailable online: 27 December 2024  \n© 2025 The Author(s) . Published by the Nigerian Society of Physical Sciences under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.  \nCommunicated by: B. J. Falaye  \n1. Introduction  \nPrecision farming is a crucial development in agricultural operations, completely altering the method by which humans  \n∗ Corresponding author: Tel.: +60 14-954 3405  \n[Email address:](Email address: majidkhanmajaharali@usm.my)[ majidkhanmajaharali@usm.my](Email address: majidkhanmajaharali@usm.my) (Majid Khan Majahar Ali )  \napproach harvests and resource efficiency. The current procedure employs advanced data analysis and technology to modify the techniques of agriculture to the specific requirements of certain fields and harvests. The application of mathematical models to simulate and predict agricultural results based on enormous amounts of data is critical to precision farming’s efficiency. Figure 1 shows how IoT systems work. They collect data such as moisture content, temperature, humidity, and solar  \nAfouna & Ali / J. Nig. Soc. Phys. Sci. 7 (2025) 2314 2  \nradiation, send it to the cloud, and process it. Farmers and users can then view the results on apps to optimize agricultural processes and increase production [1] . However, the accuracy and utility of these models are heavily dependent on the selection of significant variables and their ability to deal with data variances, such as outliers, which may influence results and restrict decision-making.  \nMachine learning (ML)","cbCaidJTBzAMpCqK","https://ap.wps.com/l/cbCaidJTBzAMpCqK","pdf",975849,1,25,"English","en",105,"# Introduction\n## Challenges in precision farming modeling\n## Variable selection using Ridge and Lasso\n## Impacts of weak or irrelevant models","[{\"question\":\"Why is variable selection critical in precision farming machine learning models?\",\"answer\":\"The accuracy and usefulness of precision farming models depend on selecting significant variables and handling data variation such as outliers, which can affect outcomes and decision-making.\"},{\"question\":\"What roles do Ridge and Lasso play in variable selection?\",\"answer\":\"Ridge regression (L2 regularization) stabilizes regression by penalizing coefficient size, helping with multicollinearity and overfitting. Lasso (L1 regularization) enables variable selection while reducing coefficients, which suits datasets with many features.\"},{\"question\":\"Which robust regression approaches are evaluated alongside Ridge and Lasso?\",\"answer\":\"The document applies robust regression methods including S, M, MM, M-Hampel, M-Huber, M-Tukey, and related MM-based variants (e.g., bisquare) in combination with Ridge and Lasso to improve efficiency and robustness.\"}]","Optimizing precision farming - enhancing machine learning efficiency with robust regression techniques in high-dimensional data | PDF",1785815139,63,{"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},"optimizing-precision-farming-enhancing-machine-learning-efficiency-with-robust-regression-techniques-in-high-dimensional-data","",{"@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/optimizing-precision-farming-enhancing-machine-learning-efficiency-with-robust-regression-techniques-in-high-dimensional-data/123193/",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],{"name":72,"@type":73,"acceptedAnswer":74},"Why is variable selection critical in precision farming machine learning models?","Question",{"text":75,"@type":76},"The accuracy and usefulness of precision farming models depend on selecting significant variables and handling data variation such as outliers, which can affect outcomes and decision-making.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What roles do Ridge and Lasso play in variable selection?",{"text":80,"@type":76},"Ridge regression (L2 regularization) stabilizes regression by penalizing coefficient size, helping with multicollinearity and overfitting. Lasso (L1 regularization) enables variable selection while reducing coefficients, which suits datasets with many features.",{"name":82,"@type":73,"acceptedAnswer":83},"Which robust regression approaches are evaluated alongside Ridge and Lasso?",{"text":84,"@type":76},"The document applies robust regression methods including S, M, MM, M-Hampel, M-Huber, M-Tukey, and related MM-based variants (e.g., bisquare) in combination with Ridge and Lasso to improve efficiency and robustness.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]