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Sustainable crop productivity depends on monitoring farmland soil quality through physical and chemical soil properties. This work integrates crop identification, irrigation recommendations, and fertilizer analysis with data-driven machine learning models to build an interactive Soil Analysis System using real-time satellite and remote-sensor data.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/data-driven-soil-analysis-and-evaluation-for-smart-farming-using-machine-learning-approaches/291198/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/data-driven-soil-analysis-and-evaluation-for-smart-farming-using-machine-learning-approaches/291198.png","ImageObject",300,407,{"name":92,"@type":93},"Valentina","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-06","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",17,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why is soil quality monitoring important for smart farming?","Question",{"text":113,"@type":114},"Soil provides the nutrition base for crops, and its physical and chemical properties strongly influence agricultural productivity. Monitoring and evaluating soil quality supports sustainable and regular crop production.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What key functions does the proposed Soil Analysis System provide?",{"text":118,"@type":114},"The system combines crop identification with irrigation recommendations and fertilizer analysis, supported by data-driven machine learning models.",{"name":120,"@type":111,"acceptedAnswer":121},"What data sources are used to support the machine learning models?",{"text":122,"@type":114},"The approach uses real-time satellite data and remote sensor data to inform soil analysis and evaluation for smart farming decisions.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},291198,1789643684,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":26,"preview_page_count":4},13056703020460,"https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923","San Jose State University  \nSJSU ScholarWorks  \nFaculty Research, Scholarly, and Creative Activity  \n9-1-2023  \nData-Driven Soil Analysis and Evaluation for Smart Farming Using Machine Learning Approaches  \nYixin Huang Alumni  \nRishi Srivastava Alumni  \nChloe Ngo Alumni  \nJerry Gao  \nSan Jose State University, [jerry.gao@sjsu.edu](jerry.gao@sjsu.edu)  \nJane Wu  \nBRI  \nSee next page for additional authors  \nFollow this and additional works at: [https://scholarworks.sjsu.edu/faculty_rsca](https://scholarworks.sjsu.edu/faculty_rsca)  \nRecommended Citation  \nYixin Huang, Rishi Srivastava, Chloe Ngo, Jerry Gao, Jane Wu, and Sen Chiao. \"Data-Driven Soil Analysis and Evaluation for Smart Farming Using Machine Learning Approaches\" Agriculture (Switzerland) (2023) .  \n[https://doi.org/10.3390/agriculture13091777](https://doi.org/10.3390/agriculture13091777)  \nThis Article is brought to you for free and open access by SJSU ScholarWorks. It has been accepted for inclusion in Faculty Research, Scholarly, and Creative Activity by an authorized administrator of SJSU ScholarWorks. For more information, please contact [scholarworks@sjsu.edu](scholarworks@sjsu.edu).  \nAuthors  \nYixin Huang, Rishi Srivastava, Chloe Ngo, Jerry Gao, Jane Wu, and Sen Chiao  \nThis article is available at SJSU ScholarWorks: [https://scholarworks.sjsu.edu/faculty_rsca/4746](https://scholarworks.sjsu.edu/faculty_rsca/4746)  \n agriculture   \nArticle  \nData-Driven Soil Analysis and Evaluation for Smart Farming Using Machine Learning Approaches  \nYixin Huang 1, Rishi Srivastava 1, Chloe Ngo 1, Jerry Gao 2, *, Jane Wu 3 and Sen Chiao 4, *  \nCitation: Huang, Y.; Srivastava, R.; Ngo, C.; Gao, J.; Wu, J.; Chiao, S. Data-Driven Soil Analysis and Evaluation for Smart Farming Using Machine Learning Approaches. Agriculture 2023, 13, 1777. [https://](https://)[ ](https://)[doi.org/10.3390/agriculture13091777](doi.org/10.3390/agriculture13091777)  \nAcademic Editor: Francesco Marinello  \nReceived: 30 June 2023  \nRevised: 3 August 2023  \nAccepted: 24 August 2023  \nPublished: 7 September 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Applied Data Science Department, San Jose State University, San Jose, CA 95192, USA; [yixin.huang01@sjsu.edu](yixin.huang01@sjsu.edu) (Y.H.); [rishi.srivastava@sjsu.edu](rishi.srivastava@sjsu.edu) (R.S.); [chau.ngo@sjsu.edu](chau.ngo@sjsu.edu) (C.N.)  \n2 3iCloud, San Jose State University, San Jose, CA 95192, USA  \n3 3iCloud.Co and BRI Captial Inc., San Francisco, CA 94104, USA; [janewu@bri-us.com](janewu@bri-us.com)  \n4 NOAA Center for Atmospheric Sciences and Meteorology, Howard University, Washington, DC 20059, USA  \n* [Correspondence: jerry.gao@sjsu.edu](Correspondence: jerry.gao@sjsu.edu) (J.G.); [sen.chiao@howard.edu](sen.chiao@howard.edu) (S.C.)  \nAbstract: Food shortage issues affect more and more of the population globally as a consequence of the climate crisis, wars, and the COVID-19 pandemic. Increasing crop output has become one of the urgent priorities for many countries. To raise the productivity of the crop product, it is necessary to monitor and evaluate farmland soil quality by analyzing the physical and chemical properties of soil since the soil is the base to provide nutrition to the crop. As a result, soil analysis contributes greatly to maintaining the sustainability of soil in producing crops regularly. Recently, some agriculture researchers have started using machine learning approaches to conduct soil analysis, targeting the different soil analysis needs separately. The optimal method is to consider all those features (climate, soil chemicals, nutrition, and geolocations) based on the growing crops and production c","cbCaidmF5srQg4OP","https://ap.wps.com/l/cbCaidmF5srQg4OP","pdf",8865074,24,"English","# Introduction\n## Background and motivation\n## Smart agriculture and data sources\n## Purpose of the study\n# Methodology and system design\n## Machine learning approach\n## Soil Analysis System workflow\n# Results and discussion\n## Evaluation of the proposed approach\n## Implications for sustainable farming","[{\"question\":\"Why is soil quality monitoring important for smart farming?\",\"answer\":\"Soil provides the nutrition base for crops, and its physical and chemical properties strongly influence agricultural productivity. Monitoring and evaluating soil quality supports sustainable and regular crop production.\"},{\"question\":\"What key functions does the proposed Soil Analysis System provide?\",\"answer\":\"The system combines crop identification with irrigation recommendations and fertilizer analysis, supported by data-driven machine learning models.\"},{\"question\":\"What data sources are used to support the machine learning models?\",\"answer\":\"The approach uses real-time satellite data and remote sensor data to inform soil analysis and evaluation for smart farming decisions.\"}]","Data-Driven Soil Analysis and Evaluation for Smart Farming Using Machine Learning Approaches | PDF"]