[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128780-en":3,"doc-seo-128780-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128780,1099523882367,"Hazel","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Enhancing Sustainable Aquaculture: Applications of Artificial Intelligence in Fish Farming - Master Thesis","Environmental condition prediction is central to modern aquaculture because it supports risk reduction for disease outbreaks. This master thesis develops deep learning approaches for forecasting key variables in fish farms using advanced time-series techniques. The work refines both dataset construction and network architectures across multiple phases, from initial exploratory modeling to LSTM-based multi-day prediction, then spatial-temporal matrix forecasting and finally localized iterative autoregression.","Master Candidate  \nRiccardo Benetti  \nStudent ID 2097142  \nAcademic Year 2024/2025  \nMaster Thesis in Computer Engineering  \nEnhancing Sustainable Aquaculture: Applications of Arti􀀜cial Intelligence in Fish Farming  \nSupervisor  \nProf. Loris Nanni University of Padova  \nTo my family y mi amor  \nAbstract  \nThe prediction of environmental conditions plays a crucial role in modern aquaculture, particularly in assessing and mitigating the risk of disease outbreaks. This thesis explores the development of deep learning models for forecasting key environmental variables in 􀀜sh farms, leveraging advanced time-series prediction techniques. The study is structured into multiple phases, progressively re􀀜ning both the dataset and the network architectures to enhance prediction accuracy.  \nInitially, the project focused on understanding the dataset, its features, and their interactions. Early experiments tested di􀀛erent architectures and preprocessing methods, establishing a foundation for subsequent improvements. In the second phase, the introduction of Long Short-Term Memory (LSTM) networks allowed for better time-series modeling, enabling the prediction of multi-day sequences. In the third phase, the complexity of the prediction task was signi􀀜cantly increased by attempting to predict entire spatial matrices of environmental variables. The 􀀜nal phase of development returned to a more localized and precise prediction approach. Utilizing an improved dataset from CNR, the model leveragedan autoregression mechanism to predict futurevalues iteratively.  \nThe 􀀜ndings of this research demonstrate that while deep learning methods􀀖particularly LSTM-based architectures􀀖are e􀀛ective for environmental time-series forecasting, the complexity of the dataset and prediction target must be carefully considered. Future work will focus on integrating real farm mortality data and developing a classi􀀜cation model capable of providing early warnings for potential disease outbreaks. This advancement would represent a crucial step toward more predictive and preventive aquaculture management, helping farmers mitigate risks and improve overall 􀀜sh health.  \nContents  \nList of Figures vii  \nList of Tables ix  \n1 Introduction 1  \n1.1 Motivations ............................... 1  \n1.2 How can AI Help Fish Farming .................... 3  \n1.2.1 AI for Fish Health Monitoring and Disease Detection ... 3  \n1.2.2 Optimizing Feeding Strategies with AI ........... 3  \n1.2.3 Water Quality Management and Environmental Monitoring 4  \n1.2.4 AI for Sustainable Resource Management and Farm Automation ............................. 4  \n1.3 Scope and Goals of the Project ..................... 5  \n2 Literature Review 7  \n2.1 Selected Literature ........................... 8  \n2.2 Review Conclusions .......................... 10  \n3 Location and Dataset 13  \n3.1 Site’s Location .............................. 13  \n3.2 Dataset .................................. 15  \n3.2.1 Initial Dataset: Data from Dipartimento diScienze Mediche Veterinarie ............................ 16  \n3.2.2 Final Dataset: Data from CNR-IAS .............. 17  \n3.3 Feature Correlation Matrix ....................... 20  \n4 Models 23  \n4.1 Overview ................................. 23  \nCONTENTS  \n4.2 First Phase: Exploring Data Potential and Initial Model Development ................................... 24  \n4.3 Second Phase: Enhancing Time-Series Prediction with LSTM Networks ................................... 26  \n4.4 Third phase: Spatial-Temporal Prediction with Combined Convolutional and LSTM Architecture .................. 28  \n4.5 Fourth phase: Localized Prediction with Targeted Feature Selection 30  \n5 Results 35  \n5.1 Overview ................................. 35  \n5.2 results phase 1 placeholder ...................... 36  \n5.3 results phase 2 placeholder ...................... 36  \n5.4 results phase 3 placeholder ...................... 37  \n5.5 results phase 4 placeholder ...................... 38  \n6 Co","cbCaim15z9pZfhiX","https://ap.wps.com/l/cbCaim15z9pZfhiX","pdf",1466568,4,1,61,"English","en",105,"# Introduction\n## Motivations\n## How can AI Help Fish Farming\n## Scope and Goals of the Project\n# Literature Review\n## Selected Literature\n## Review Conclusions\n# Location and Dataset\n## Site’s Location\n## Dataset\n## Feature Correlation Matrix\n# Models\n## Overview\n## First Phase: Exploring Data Potential and Initial Model Development\n## Second Phase: Enhancing Time-Series Prediction with LSTM Networks\n## Third Phase: Spatial-Temporal Prediction with Combined Convolutional and LSTM Architecture\n## Fourth Phase: Localized Prediction with Targeted Feature Selection\n# Results\n## Overview\n## Results Phase 1 placeholder\n## Results Phase 2 placeholder\n## Results Phase 3 placeholder\n## Results Phase 4 placeholder\n# Conclusions and Future Work\n## Summary of Findings\n## Future Work\n## Final Remarks","[{\"question\":\"What problem does the thesis address in fish farming?\",\"answer\":\"It addresses the need to predict environmental conditions to assess and mitigate the risk of disease outbreaks in aquaculture.\"},{\"question\":\"How is the modeling approach developed across the thesis phases?\",\"answer\":\"The study progresses from dataset exploration and initial architectures to LSTM-based time-series forecasting, then increases complexity with spatial-temporal matrix prediction, and finally returns to localized iterative prediction using autoregression.\"},{\"question\":\"What future work is planned to improve practical disease prevention?\",\"answer\":\"Future work will integrate real farm mortality data and develop a classification model for early warnings of potential disease outbreaks, enabling more predictive and preventive management.\"}]","Enhancing Sustainable Aquaculture: Applications of Artificial Intelligence in Fish Farming - Master Thesis | PDF",1786003361,154,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"enhancing-sustainable-aquaculture-applications-of-artificial-intelligence-in-fish-farming-master-thesis","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/enhancing-sustainable-aquaculture-applications-of-artificial-intelligence-in-fish-farming-master-thesis/128780/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the thesis address in fish farming?","Question",{"text":76,"@type":77},"It addresses the need to predict environmental conditions to assess and mitigate the risk of disease outbreaks in aquaculture.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is the modeling approach developed across the thesis phases?",{"text":81,"@type":77},"The study progresses from dataset exploration and initial architectures to LSTM-based time-series forecasting, then increases complexity with spatial-temporal matrix prediction, and finally returns to localized iterative prediction using autoregression.",{"name":83,"@type":74,"acceptedAnswer":84},"What future work is planned to improve practical disease prevention?",{"text":85,"@type":77},"Future work will integrate real farm mortality data and develop a classification model for early warnings of potential disease outbreaks, enabling more predictive and preventive management.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]