[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118957-en":3,"doc-seo-118957-105":30,"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":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},118957,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Recommendation System using Machine Learning for Fertilizer Prediction","A machine-learning project develops a fertilizer recommendation model to improve agricultural productivity by predicting the optimal fertilizer for specific crop needs. The approach uses soil color, soil pH, rainfall, temperature, and crop type as input features, producing tailored guidance for farmers. Three algorithms—Support Vector Machines, Artificial Neural Networks, and XG-Boost—are implemented to perform the prediction task. Experiments and evaluations compare their effectiveness in selecting the best fertilizer to maximize crop yield, supporting sustainable farming and food security.","California State University, San Bernardino  \nCSUSB ScholarWorks  \n\n| Electronic Theses, Projects, and Dissertations | Office of Graduate Studies |\n| --- | --- |\n| 5-2024\u003Cbr>Recommendation System using machine learning for fertilizer prediction\u003Cbr>Durga Rajesh Bommireddy\u003Cbr>Follow this and additional works at: [https://scholarworks.lib.csusb.edu/etd](https://scholarworks.lib.csusb.edu/etd)\u003Cbr> Part of the Computer Engineering Commons |  |\n\nRecommended Citation  \nBommireddy, Durga Rajesh, \"Recommendation System using machine learning for fertilizer prediction\"(2024) . Electronic Theses, Projects, and Dissertations. 1943.  \n[https://scholarworks.lib.csusb.edu/etd/1943](https://scholarworks.lib.csusb.edu/etd/1943)  \nThis Project is brought to you for free and open access by the Office of Graduate Studies at CSUSB ScholarWorks. It has been accepted for inclusion in Electronic Theses, Projects, and Dissertations by an authorized administrator of CSUSB ScholarWorks. For more information, please contact [scholarworks@csusb.edu](scholarworks@csusb.edu).  \nRECOMMENDATION SYSTEM USING MACHINE LEARNING  \nFOR FERTILIZER PREDICTION  \nA Project Presented to the Faculty of  \nCalifornia State University, San Bernardino  \nIn Partial Fulfillment of the Requirements for the Degree Master of Science in Computer Science  \nby Rajesh Bommireddy  \nMay 2024  \nRECOMMENDATION SYSTEM USING MACHINE LEARNING  \nFOR FERTILIZER PREDICTION  \nA Project Presented to the Faculty of  \nCalifornia State University, San Bernardino  \nby  \nRajesh Bommireddy  \nMay 2024  \nApproved by:  \nDr. Qingquan Sun , Advisor, School of Computer Science  \nDr. Bilal Khan , Committee Member  \nDr. Yan Zhang , Committee Member  \n© 2024 Rajesh Bommireddy  \nABSTRACT  \nThis project presents the development of a sophisticated machine-learning model aimed at enhancing agricultural productivity by predicting the optimal fertilizer suited to specific crop requirements. Leveraging a diverse set of features including soil color, pH levels, rainfall, temperature, and crop type, our model offers tailored recommendations to farmers. Three powerful algorithms, Support vector machines (SVM), Artificial neural networks (ANN), and XG-Boost, were implemented to facilitate the prediction process. Through comprehensive experimentation and evaluation, we assessed the performance of each algorithm in accurately predicting the best fertilizer for maximizing crop yield. The project not only contributes to the advancement of machine learning techniques in agriculture but also holds significant implications for sustainable farming practices and food security.  \nACKNOWLEDGEMENTS  \nFirstly, I wish to express my gratitude to my esteemed advisor, Dr. Qingquan Sun, and respected committee members, Dr. Bilal Khan, and Dr. Yan Zhang. They have provided guidance, knowledge, and wonderful support. Their belief in me makes me strive to achieve my goal in the timeline. Their valuable input , mentorship, and guidance greatly influenced the course and excellence of this endeavor.  \nI express my gratitude to the teachers and staff at California State University, San Bernardino, whose availability of crucial resources and a supportive academic atmosphere were vital in ensuring the successful completion of this project. Their unwavering commitment to academic achievement has served as a consistent source of motivation.  \nDEDICATION  \nI would like to dedicate my master's research to my advisor, Dr. Qingquan Sun , with the deepest thanks and respect. His guidance and motivation helped me to complete my project successfully. Every time I met him, he always motivated me to show my work should look unique to others. I truly believe I gained immense knowledge under his guidance and support.  \nTABLE OF CONTENTS  \nABSTRACT .......................................................................................................... iii  \nACKNOWLEDGEMENTS .....................................................................................i","cbCais38fllnBKGi","https://ap.wps.com/l/cbCais38fllnBKGi","pdf",941588,1,49,"English","en",105,"# Chapter One: Introduction\n## Problem Statement\n# Chapter Two: Literature Survey\n# Chapter Three: System Requirements\n## Frameworks\n## Python\n## Flask\n## Pandas\n## NumPy\n## Scikit-learn\n## Matplotlib\n## Hardware requirements\n## Software requirements\n# Chapter Four: System Design\n## UML Diagrams\n## Goals\n# Chapter Five: Implementation\n## Modules\n## Data collection","[{\"question\":\"What problem does the project address in agriculture?\",\"answer\":\"The project targets enhancing agricultural productivity by predicting the most suitable fertilizer for specific crop requirements.\"},{\"question\":\"Which input features are used for the fertilizer recommendation model?\",\"answer\":\"The model leverages soil color, soil pH, rainfall, temperature, and crop type to drive its recommendations.\"},{\"question\":\"Which machine-learning algorithms are implemented and compared?\",\"answer\":\"The project implements Support Vector Machines (SVM), Artificial Neural Networks (ANN), and XG-Boost, then evaluates their prediction performance.\"}]","Recommendation System using Machine Learning for Fertilizer Prediction | 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problem does the project address in agriculture?","Question",{"text":76,"@type":77},"The project targets enhancing agricultural productivity by predicting the most suitable fertilizer for specific crop requirements.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which input features are used for the fertilizer recommendation model?",{"text":81,"@type":77},"The model leverages soil color, soil pH, rainfall, temperature, and crop type to drive its recommendations.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine-learning algorithms are implemented and compared?",{"text":85,"@type":77},"The project implements Support Vector Machines (SVM), Artificial Neural Networks (ANN), and XG-Boost, then evaluates their prediction 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