[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122269-en":3,"doc-seo-122269-105":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},122269,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Plant Identification Using Machine Learning under Real-World Conditions - Bachelor’s Thesis","A bachelor’s thesis develops a practical prototype application for identifying plant species under real-world conditions using machine learning. It evaluates how well trained models detect species across varying conditions, how to fine-tune models after initial training and deployment, and what accuracy versus computational-efficiency trade-offs arise when deploying on mobile devices. Images are curated into 21 species classes and the model is trained with YOLO in a Google Colab workflow, then deployed via Gradio on Hugging Face Spaces, with performance analyzed through confusion matrices and loss curves.","Plant Identification Using Machine Learning under Real-World Conditions  \nBachelor’s Thesis  \nDegree Programme in Computer Applications  \nSpring 2025  \nMatt Enrico D. Flores  \nDP Degree Programme in Computer Applications  \nAuthor Matt Enrico D. Flores Year 2025  \nSubject Plant Identification Using Machine Learning under Real-World Conditions Supervisors Mazhar Mohsin  \nThis thesis aimed to research and develop a practical prototype application that can identify different types of plant life under different conditions using machine learning while enhancing the researcher’s experience in the field. The research questions investigated by the thesis were: How effective are trained machine learning models at detecting different species of plants under different conditions , how can we efficiently fine-tune models for better species recognition after the initial training and deployment , and what are any trade-offs regarding accuracy and computational efficiency when deploying the identification models on mobile devices. The dataset used to develop the created model was provided by the researchers at HAMK Lepaa.  \nThe thesis approached the research problems using practical methods. The dataset provided from HAMK Lepaa was first combed through to separate images with good quality and bad quality , then they were sorted again by their corresponding species into 21 unique classes. This new dataset was then used for training a model using the You Only Look Once (YOLO) algorithm in a Google Colab environment. After multiple training sessions, the model is incorporated into a simple user interface created using Gradio and deployed into Hugging Face Spaces. The model and application’s performance are analysed using different confusion matrices and loss curve diagrams automatically generated during training.  \nThe thesis was successful in the creation of a working prototype that utilizes a trained model to make predictions. The model itself had difficulty predicting classes that were either visually similar to other classes or belonged to the same family. The deployed prototype also had slower processing speeds due to the limitations of Hugging Face Spaces. Based on the analysis, it is recommended that a larger dataset of higher quality be used to avoid issues of misclassifications among different classes andoverfitting during training. Alternative platforms and methods for deployment should also be considered to ensure that processing speeds are fast enough to remain practical for use.  \nKeywords Artificial Intelligence, Machine learning, Convolutional Neural Networks (CNN), You Look Only Once (YOLO)  \nPages 30 pages and appendices 1 page  \nGlossary  \nArtificial Intelligence The capability for computers to perform tasks usually associated with human intelligence  \nMachine Learning Branch of artificial intelligence that mimics the way humans learn to improve  \nwhen performing tasks  \nConvolutional Neural Networks (CNN) Specialized type of neural network used for complex recognition tasks  \nYou Look Only Once (YOLO) Open-source object detection algorithm that prioritizes speed  \nTable of Contents  \n1 Introduction ..................................................................................................................................... 1  \n2 Convolutional Neural Networks (CNN) in Deep Learning ................................................................ 2  \n2.1 Deep Learning for Computer Vision ....................................................................................... 2  \n2.2 CNN Fundamentals................................................................................................................ 5  \n2.3 You Only Look Once (YOLO) ................................................................................................. 8  \n3 Plant Identification using Machine Learning Models ...................................................................... 10  \n4 Methodology ........................................","cbCainEymdjc5R1g","https://ap.wps.com/l/cbCainEymdjc5R1g","pdf",1469504,1,36,"English","en",105,"# Introduction\n# Convolutional Neural Networks (CNN) in Deep Learning\n## Deep Learning for Computer Vision\n## CNN Fundamentals\n## You Only Look Once (YOLO)\n# Plant Identification using Machine Learning Models\n# Methodology\n## Dataset Preparation\n## Data Preprocessing using Roboflow\n## Training Environment and Architecture\n## User interface deployment using Gradio\n# Practical Part\n## Initial Training\n## Second Training\n## Third Training\n## Deployment on Hugging Face Spaces\n# Results\n## Model Performance\n## Implementation\n## Recommendations\n# Summary","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"To research and build a working prototype that identifies different plant types under varying real-world conditions using machine learning while improving the field research experience.\"},{\"question\":\"How was the training dataset prepared?\",\"answer\":\"The provided images were filtered by quality, then regrouped into 21 unique species classes before being used for model training.\"},{\"question\":\"How was the model trained and deployed?\",\"answer\":\"The model was trained using the YOLO algorithm in Google Colab and then integrated into a Gradio interface, deployed on Hugging Face Spaces for user access.\"}]","Plant Identification Using Machine Learning under Real-World Conditions - Bachelor’s Thesis | PDF",1785809754,91,{"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},"plant-identification-using-machine-learning-under-real-world-conditions-bachelors-thesis","",{"@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/plant-identification-using-machine-learning-under-real-world-conditions-bachelors-thesis/122269/",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},"What is the main goal of the thesis?","Question",{"text":75,"@type":76},"To research and build a working prototype that identifies different plant types under varying real-world conditions using machine learning while improving the field research experience.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the training dataset prepared?",{"text":80,"@type":76},"The provided images were filtered by quality, then regrouped into 21 unique species classes before being used for model training.",{"name":82,"@type":73,"acceptedAnswer":83},"How was the model trained and deployed?",{"text":84,"@type":76},"The model was trained using the YOLO algorithm in Google Colab and then integrated into a Gradio interface, deployed on Hugging Face Spaces for user access.","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"]