[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117106-en":3,"doc-seo-117106-105":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},117106,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","INTEGRATED CROP RECOMMENDATION SYSTEM - Harnessing Machine Learning","To meet the growing demand for food while reducing environmental impact, this study proposes an Integrated Crop Recommendation System that combines advanced machine learning with sustainable farming practices. The approach improves crop selection by accounting for soil quality, local climate, and habitat needs for pollinators to raise precision and effectiveness. It addresses limitations of many agricultural decision support systems that overlook links among soil health, weather, and biodiversity. The research includes literature review, methodology development, data analysis, and results discussion.","California State University, San Bernardino  \nCSUSB ScholarWorks  \n\n| Electronic Theses, Projects, and Dissertations | Office of Graduate Studies |\n| --- | --- |\n| 8-2024\u003Cbr>INTEGRATED CROP RECOMMENDATION SYSTEM: HARNESSING MACHINE LEARNING\u003Cbr>Arun Kumar Reddy Saddikuti\u003Cbr>Follow this and additional works at: [https://scholarworks.lib.csusb.edu/etd](https://scholarworks.lib.csusb.edu/etd)\u003Cbr> Part of the Business Commons |  |\n\nRecommended Citation  \nSaddikuti, Arun Kumar Reddy, \"INTEGRATED CROP RECOMMENDATION SYSTEM: HARNESSING MACHINE LEARNING\" (2024) . Electronic Theses, Projects, and Dissertations. 2018.  \n[https://scholarworks.lib.csusb.edu/etd/2018](https://scholarworks.lib.csusb.edu/etd/2018)  \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).  \nINTEGRATED CROP RECOMMENDATION SYSTEM: HARNESSING  \nMACHINE LEARNING  \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  \nInformation Systems and Technology  \nby  \nSaddikuti Arun Kumar Reddy August 2024  \nINTEGRATED CROP RECOMMENDATION SYSTEM: HARNESSING  \nMACHINE LEARNING  \nA Project Presented to the Faculty of  \nCalifornia State University, San Bernardino  \nby  \nSaddikuti Arun Kumar Reddy  \nAugust 2024  \nApproved by:  \nDr. Nima Molavi, Committee Member, Co-Chair Dr. Conrad Shayo, Committee Member, Co-Chair Dr. Conrad Shayo, Department Chair, Information and Decision Sciences  \n© 2024 Saddikuti Arun Kumar Reddy  \nABSTRACT  \nTo meet the increasing demand for food while also reducing impact this study introduces an innovative \"Integrated Crop Recommendation System\" that combines advanced machine learning with sustainable farming methods. The goal of this system is to transform how crops are chosen by considering factors like soil quality, local climate and habitats for pollinators thereby enhancing the precision and effectiveness of crop suggestions. In contrast to agricultural decision support systems that often neglect the interconnectedness of soil health, weather conditions and biodiversity, this new approach aims to improve food security and sustainability. The primary research focus is on optimizing practices that support pollinators in environments. The research aims to provide farmers with enhanced guidance and deeper insights into the relationships among soil quality, weather patterns and ecological sustainability offering a solution for modern farming practices. The study encompasses a literature review, methodology development, data analysis, and discussion of findings. Outlines research directions. Research Questions are: Q1) How can incorporating pollinator-related data into machine learning models enhance the accuracy and efficiency of agricultural decision support systems for optimal crop recommendations? Q2) What kind of effect does incorporating practices to support pollinators have on the overall strength and durability of crop recommendations produced by the integrated machine learning model?  \nThe Findings and Discussions for the questions are: Q1) In our research we. Evaluated a system for suggesting crops based on machine learning. This system considers factors such as soil quality, weather conditions and reliance on pollinators by studying data sets related to crop recommendations and pollination. Our analysis of the data showed connections like the relationship between rainfall and crop production. Additionally, our decision tree model performed better than the SVM model, in predicting crop yields. Q2) The research shows that including methods to support pollinators in crop recommendations based on machine learning can improve their performance and durability.","cbCaihUldaAiuf6N","https://ap.wps.com/l/cbCaihUldaAiuf6N","pdf",1257984,1,74,"English","en",105,"# Abstract\n## Research Questions\n## Findings and Discussion\n## Conclusions\n## Areas for Further Study\n# Acknowledgements\n# Dedication","[{\"question\":\"What problem does the Integrated Crop Recommendation System address?\",\"answer\":\"It targets the need to increase food production while lowering environmental impact by improving how crops are chosen.\"},{\"question\":\"Which factors are considered to improve crop recommendations?\",\"answer\":\"The system uses machine learning with soil quality, local climate, and pollinator habitat-related information.\"},{\"question\":\"How does supporting pollinators affect the recommendation performance?\",\"answer\":\"Including pollinator support practices improves the accuracy and durability/strength of crop recommendations and helps tailor conservation approaches.\"}]","INTEGRATED CROP RECOMMENDATION SYSTEM - 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