[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119292-en":3,"doc-seo-119292-105":30,"detail-sidebar-cat-0-en-105":99},{"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":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},119292,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Leaf it to the AI-Visualizing Foliage with Machine Learning","This project creates a tool to aid in the research and monitoring of the threatened prickly Fig. This website displays current and predictive locations of foliage within a region. To get the data of the predictive location, an additional program was developed that uses a machine learning model to provide coordinates of potential areas where more plants may be. The computer program provides a visualization of potential patterns for the foliage of the plant. From the machine learning model results, through the data and provides patterns for researchers. The website provides visualizations by comparing the output of similar locations. The dataset used is of the Acerifolius data. The plant is native to Australia. This dataset contains the height, crown width, health rating, and diameter at breast height. This information was used as reference for the machine learning model. The missing data needed was the latitude and longitude. These two attributes were gained through combining another dataset to develop patterns. The coordinates set the plant dataset within France and not Australia. This methodology is not realistic, the idea was to train the model and see if it was working as intended. The data was split with 80% in a training set and 20% within a training set. Machine learning implementation: Random Forest Regression was used to analyze the datasets and accurately predict the coordinates for the visualizations. The program trains the model with the previously collected data. Ultimately, the dataset is run through to train for latitude then for longitude the models cannot predict both coordinates together. Once it is trained, validation is provided through the printing of mean absolute error. The lower the error, the better accuracy. The program is fitted with the test dataset and combined the predictions for both coordinates to be exported as a spreadsheet. This spreadsheet allows the visualization program to analyze it as a dataset and display it as maps to visualize patterns of foliage. Visualization implementation: Python, HTML, and CSS were used to embed the heat map and marker maps with the original dataset onto the web pages. This heat map marker map and CSS allows researchers to analyze the placements of the Acerifolius.","Digital Commons at St. Mary's University  \n\n| Research Showcase Posters-2025 | Annual Showcase-2025 |\n| --- | --- |\n| Spring 2025\u003Cbr>Leaf it to the AI: Visualizing Foliage with Machine Learning\u003Cbr>Chelsy Tinacba St. Mary 's University\u003Cbr>George Sikazwe\u003Cbr>University of Incarnate Word\u003Cbr>Michael Frye\u003Cbr>University of Incarnate Word\u003Cbr>Follow this and additional works at: [https://commons.stmarytx.edu/rscpos25](https://commons.stmarytx.edu/rscpos25)\u003Cbr> Part of the Environmental Education Commons, Environmental Health and Protection Commons, and the Environmental Monitoring Commons |  |\n\nRecommended Citation  \nTinacba, Chelsy; Sikazwe, George; and Frye, Michael, \"Leaf it to the AI: Visualizing Foliage with Machine Learning\" (2025) . Research Showcase Posters-2025. 22.  \n[https://commons.stmarytx.edu/rscpos25/22](https://commons.stmarytx.edu/rscpos25/22)  \nThis Book is brought to you for free and open access by the Annual Showcase-2025 at Digital Commons at St. Mary's University. It has been accepted for inclusion in Research Showcase Posters-2025 by an authorized administrator of Digital Commons at St. Mary's University. For more information, please contact[sfowler@stmarytx.edu](sfowler@stmarytx.edu), [egoode@stmarytx.edu](egoode@stmarytx.edu).","cbCail9pFdMRerva","https://ap.wps.com/l/cbCail9pFdMRerva","pdf",911593,1,2,"English","en",105,"# Abstract\n# Visualization Implementation\n# Machine Learning Implementation\n# Acerifolius Data\n# Acknowledgements\n# Results","[{\"question\":\"What is the primary goal of this project?\",\"answer\":\"The project aims to develop a tool to assist in the research and monitoring of the threatened prickly Fig by displaying current and predictive foliage locations using machine learning.\"},{\"question\":\"What dataset was used for the machine learning model?\",\"answer\":\"The dataset used comprises of the Acerifolius plant, native to Australia, including attributes such as height, crown width, health rating, and diameter at breast height. Latitude and longitude were derived from a separate dataset.\"},{\"question\":\"What machine learning algorithm was employed for the visualization predictions?\",\"answer\":\"Random Forest Regression was utilized to analyze the datasets and accurately predict the coordinates for the foliage visualizations. This algorithm was chosen for its ability to analyze the data and provide pattern predictions.\"},{\"question\":\"How was the visualization of the data implemented?\",\"answer\":\"Python, HTML, and CSS were used to create interactive heat maps and marker maps on web pages, embedding the original dataset and allowing researchers to analyze the foliage placements.\"},{\"question\":\"What metric was used to evaluate the accuracy of the machine learning model?\",\"answer\":\"The accuracy of the machine learning model was assessed using the Mean Absolute Error, with a lower error indicating better accuracy in predicting coordinates.\"}]","Leaf it to the AI-Visualizing Foliage with Machine Learning | PDF",1785723554,5,{"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":94,"head_meta":96,"extra_data":98,"updated_unix":28},"leaf-it-to-the-ai-visualizing-foliage-with-machine-learning","",{"@graph":36,"@context":93},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":21},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/leaf-it-to-the-ai-visualizing-foliage-with-machine-learning/119292/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85,89],{"name":72,"@type":73,"acceptedAnswer":74},"What is the primary goal of this project?","Question",{"text":75,"@type":76},"The project aims to develop a tool to assist in the research and monitoring of the threatened prickly Fig by displaying current and predictive foliage locations using machine learning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset was used for the machine learning model?",{"text":80,"@type":76},"The dataset used comprises of the Acerifolius plant, native to Australia, including attributes such as height, crown width, health rating, and diameter at breast height. Latitude and longitude were derived from a separate dataset.",{"name":82,"@type":73,"acceptedAnswer":83},"What machine learning algorithm was employed for the visualization predictions?",{"text":84,"@type":76},"Random Forest Regression was utilized to analyze the datasets and accurately predict the coordinates for the foliage visualizations. This algorithm was chosen for its ability to analyze the data and provide pattern predictions.",{"name":86,"@type":73,"acceptedAnswer":87},"How was the visualization of the data implemented?",{"text":88,"@type":76},"Python, HTML, and CSS were used to create interactive heat maps and marker maps on web pages, embedding the original dataset and allowing researchers to analyze the foliage placements.",{"name":90,"@type":73,"acceptedAnswer":91},"What metric was used to evaluate the accuracy of the machine learning model?",{"text":92,"@type":76},"The accuracy of the machine learning model was assessed using the Mean Absolute Error, with a lower error indicating better accuracy in predicting coordinates.","https://schema.org",{"og:url":51,"og:type":95,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":97,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":100},[101,105,109,113,117,122,127,130,135,138,142],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Story & Novel",90,"story-novel",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":110,"show_sort_weight":111,"slug":112},"Exam",70,"exam",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":114,"show_sort_weight":115,"slug":116},"Comic",60,"comic",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":119,"show_sort_weight":120,"slug":121},6,"Technology",50,"technology",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":128,"slug":129},30,"research-report",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":133,"slug":134},9,"Religion & Spirituality",20,"religion-spirituality",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":133,"slug":137},"World Cup","world-cup",{"id":139,"doc_module":4,"doc_module_name":46,"category_name":140,"show_sort_weight":139,"slug":141},10,"Lifestyle","lifestyle",{"id":143,"doc_module":4,"doc_module_name":46,"category_name":144,"show_sort_weight":29,"slug":145},19,"General","general"]