[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123162-en":3,"doc-seo-123162-105":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},123162,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning-Based Smart Agricultural Practices To Assess Soil Fertility And Nutrient Dynamics - Research Report","This research utilises machine learning to support precision agriculture by forecasting soil characteristics and nutrient dynamics using environmental measurements. The dataset includes temperature, humidity, soil moisture, and N, P, K values to build models that produce fertilizer application recommendations. Model quality is evaluated with R-squared, Adjusted R-squared, MAE, and MSE, where the random forest model delivers superior accuracy. Results indicate different regressors vary in fit, and continuous environmental monitoring is essential for sustainable farming.","Machine Learning-Based Smart Agricultural Practices To Assess Soil Fertility And Nutrient Dynamics  \nSEEJPH Volume XXIV, S4, 2024; ISSN: 2197-5248; Posted:02-08-2024  \nMachine Learning-Based Smart Agricultural Practices To Assess Soil Fertility And  \nNutrient Dynamics  \nLakshmi Kalyani N1, Bhanu Prakash Kolla2*  \n1,2K.L. Deemed to be University, Green Fields, Vaddeswaram 522302, Guntur District, A.P, India  \n*Corresponding author: Kolla Bhanu Prakash: *Email: [drkbp@kluniversity.in](drkbp@kluniversity.in)  \n\n| KEYWORDS | ABSTRACT |\n| --- | --- |\n| soil nutrients; fertilizer | This research utilises machine learning (ML) to improve agricultural precision by forecasting soil |\n| prediction; precision | characteristics levels using environmental data. A dataset that comprised additional information on |\n| agriculture; artificial | temperature, humidity, soil moisture, nitrogen (N), phosphorous (P), and potassium (K) values was used |\n| intelligence; computer | to create models that proposed recommendations for adequate fertilizer application. The performance of |\n| vision; image | models was assessed utilizing R-squared, Adjusted R-squared, mean absolute error (MAE), and mean |\n| processing; smart | squared error (MSE). The random forest (RF) model was more accurate than others, showing the lowest |\n| agricultural systems | MSE for P (mg/kg) and competitive MAE for other characteristics. Gradient boosting models had higher errors and negative R-squared values, suggesting they didn ’t fit the data, even though the results were close in performance. Linear regression proved to be reliable with the lowest MAE for N (mg/kg) and K (mg/kg) and the most significant R-squared values for P (mg/kg), showing its persuasiveness inaccurately forecasting these characteristic levels despite its simplicity. The research leverages machine learning to precisely predict soil nutrients for smarter farming, with the random forest model providing superior accuracy over other techniques. These advancements highlight the importance of continuous innovation in environmental monitoring for sustainable agriculture. |\n\n1. Introduction  \nExamining crop nutrition data is more critical than ever to determine exact nutrient requirements and improve fertilizer application procedures[1] . The provided paper is about soil moisture prediction using remote sensing images and deep learning. It does not mention the use of machine learning for forecasting soil characteristics levels or the evaluation metrics mentioned in the query [2] . To determine the best fertilization strategies, timely and accurate evaluation and control of crops ’ nutritional condition are essential [3] . This reduces the environmental impact by increasing agricultural output, improving crop quality, and minimizing consumption of chemical fertilizers[4] . Determining the accurate amount of fertilizer required to cultivate plants that meet the specified quality measures is a crucial component of different control procedures[5] . Nitrogen (N), phosphorus (P), and potassium (K) are the three characteristics that are considered necessary for growth and productivity in plants. Many factors, including the location of tree farming, type of soil, agriculture methods, the age of the trees, the age and location of the leaves, and the precise combination of rootstock and scion used, maintain the necessity for these nutrients[6] . In general plant cultivation, leaves are an essential part of selecting nutrient insufficiencies and instructing the adequate application of fertilizer [7] . The authors investigated the effects of imbalance in training data on the performance of a random forest model (RF) and concluded that data should be balanced before modeling, in modeling soil texture classes using RF models through a digital soil mapping approach[8] . These structures are significant for storing minerals and carbohydrates and for photosynthesis, which is essential to the basic functioning of plants [9] . For ","cbCaidGxREYi12et","https://ap.wps.com/l/cbCaidGxREYi12et","pdf",1089569,1,16,"English","en",105,"# Introduction\n## Literature survey\n# System model\n## Experiment analysis\n# Conclusion","[{\"question\":\"What data inputs are used to predict soil nutrients in this study?\",\"answer\":\"The study uses environmental parameters including temperature, humidity, soil moisture, and nutrient values for nitrogen (N), phosphorus (P), and potassium (K). These inputs also support image-related and environmental feature integration for prediction.\"},{\"question\":\"Which model performs best for nutrient prediction and how is performance measured?\",\"answer\":\"The random forest model shows the highest accuracy, with the lowest MSE for P and comparatively strong MAE results. Performance is assessed using R-squared, Adjusted R-squared, MAE, and MSE.\"},{\"question\":\"How do the findings support precision and sustainable agriculture?\",\"answer\":\"Accurate NPK prediction enables recommendations for adequate fertilizer application, helping reduce unnecessary chemical fertilizer use. This improves crop health and quality while minimizing environmental impact.\"}]","Machine Learning-Based Smart Agricultural Practices To Assess Soil Fertility And Nutrient Dynamics - Research Report | PDF",1785814985,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-based-smart-agricultural-practices-to-assess-soil-fertility-and-nutrient-dynamics-research-report","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-smart-agricultural-practices-to-assess-soil-fertility-and-nutrient-dynamics-research-report/123162/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What data inputs are used to predict soil nutrients in this study?","Question",{"text":75,"@type":76},"The study uses environmental parameters including temperature, humidity, soil moisture, and nutrient values for nitrogen (N), phosphorus (P), and potassium (K). These inputs also support image-related and environmental feature integration for prediction.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which model performs best for nutrient prediction and how is performance measured?",{"text":80,"@type":76},"The random forest model shows the highest accuracy, with the lowest MSE for P and comparatively strong MAE results. Performance is assessed using R-squared, Adjusted R-squared, MAE, and MSE.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the findings support precision and sustainable agriculture?",{"text":84,"@type":76},"Accurate NPK prediction enables recommendations for adequate fertilizer application, helping reduce unnecessary chemical fertilizer use. 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