[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121018-en":3,"doc-seo-121018-105":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},121018,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Developing Machine Learning Models for Selection of Management Zones","Developing Machine Learning Models for Selection of Management Zones evaluates how machine-learning approaches can support improved soil management decisions by identifying and selecting management zones. The thesis frames the problem through soil testing, representative sampling, soil heterogeneity, topography, and the role of management zones, then details data collection, preprocessing, and the creation of management zones. Model development focuses on classification, data splitting, normalization, and hyperparameter tuning, followed by evaluation using defined metrics and a structured workflow.","South Dakota State University  \nOpen PRAIRIE: Open Public Research Access Institutional Repository and Information Exchange  \nElectronic Theses and Dissertations  \n2024  \nDeveloping Machine Learning Models for Selection of Management Zones  \nSravanthi Bachina  \nFollow this and additional works at: [https://openprairie.sdstate.edu/etd2](https://openprairie.sdstate.edu/etd2)  \n Part of the Bioresource and Agricultural Engineering Commons, and the Plant Sciences Commons  \nDEVELOPING MACHINE LEARNING MODELS FOR SELECTION OF  \nMANAGEMENT ZONES  \nBY  \nSRAVANTHI BACHINA  \nA thesis submitted in partial fulfillment of the requirements for the  \nMaster of Science  \nMajor in Plant Science  \nSouth Dakota State University  \n11  \nTHESIS ACCEPTANCE PAGESravanthi Bachina  \nThis thesis is approved as a creditable and independent investigation by a candidate for the master's degree and is acceptable for meeting the thesis requirements for this degree. Acceptance of this does not imply that the conclusions reached by the candidate are necessarily the conclusions ofthe major department.  \nKristopher Osterloh Advisor  \nDavid Wright Department Head  \nNicole Lounsbery, PhD Director, Graduate School  \nDate  \nDate  \nDate  \nThis dissertation is dedicated to the corner stones of my life: my Mom (Anu Radha Bachina) and Dad (Hanumantha Rao Bachina) whose unwavering support and guidance have shaped my journey; my husband Srinadh Kodali who had been my constant source of strength and encouragement; and to my loving son Sriansh Kodali who inspires me todo great things every day. Their love and sacrifices have been the wind beneath my wings, allowing me to soar to new academic heights.  \nACKNOWLEDGEMENTS  \nI extend my deepest gratitude to Dr. Kristopher Osterloh for his unwavering support, insightful guidance, and invaluable feedback throughout the journey of this thesis. His dedication and encouragement have been instrumental in shaping my research endeavors.  \nI am sincerely thankful to the committee members Dr. Jiyul Chang and Dr. Maitiniyazi Maimaitijiang for their expertise, constructive criticism, and commitment to excellence. Their collective wisdom has significantly enriched the quality of this work.  \nI am deeply grateful to NRCS (Natural Resources Conservation Service) and SDSU (South Dakota State University) for their generous funding and support, which made this research possible. Their financial assistance not only facilitated the execution of this study but also underscored their commitment to advancing scientific inquiry and promoting academic excellence.  \nI extend my heartfelt appreciation to both NRCS and SDSU for their investment in my academic pursuits, which have undoubtedly contributed to the development of knowledge in this field.  \nTABLE OF CONTENTS  \nABBREVIATIONS ......................................................................................................... VII  \nLIST OF FIGURES .......................................................................................................... IX  \nLIST OF TABLES ............................................................................................................. X  \nABSTRACT...................................................................................................................... XI  \nCHAPTER 1: INTRODUCTION AND LITERATURE REVIEW ................................... 1  \n1.1 Introduction ............................................................................................................... 1  \n1.2 Soil testing ................................................................................................................ 2  \n1.3 Representative samples ............................................................................................. 3  \n1.4 Sampling techniques and challenges........................................................................ 4  \n1.5. Soil heterogeneity ...........................................................................","cbCaifGXELZu7Ts8","https://ap.wps.com/l/cbCaifGXELZu7Ts8","pdf",7197680,1,87,"English","en",105,"# Chapter 1: Introduction and Literature Review\n## 1.1 Introduction\n## 1.2 Soil testing\n## 1.3 Representative samples\n## 1.4 Sampling techniques and challenges\n## 1.5 Soil heterogeneity\n## 1.6 Topography and its relationship with dynamic soil properties\n## 1.7 Management zones\n## 1.8 Machine learning in soil science\n## 1.9 Review of recent models in soil science\n# Chapter 2: Materials and Methods\n## 2.1 Data Collection and Preprocessing\n## 2.1.1 Study Sites\n## 2.1.2 Data Collection\n## 2.1.3 Creation of management zones\n## 2.1.4 Data Preprocessing\n## 2.2 Model development\n## 2.2.1 Classification of management zones\n## 2.2.2 Data Splitting\n## 2.2.4 Data Normalization and Hyperparameter tuning\n## 2.3 Evaluation metrics\n## 2.4 Workflow\n# Chapter 3: Results and Discussion","[{\"question\":\"What is the purpose of the thesis?\",\"answer\":\"To develop machine-learning models that support the selection of management zones by classifying zone types based on soil and site data.\"},{\"question\":\"How does the thesis prepare the data for modeling?\",\"answer\":\"It describes data collection, preprocessing steps, and the creation of management zones, followed by normalization and hyperparameter tuning.\"},{\"question\":\"What kinds of evaluation are used to assess the models?\",\"answer\":\"The thesis uses evaluation metrics and compares model performance within a defined workflow after training and data splitting.\"}]","Developing Machine Learning Models for Selection of Management Zones | 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