[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123259-en":3,"doc-seo-123259-105":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},123259,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Machine Learning Driven Agricultural Portal - Enhancing Crop Production and Decision-Making","Agricultural Portal is an innovative, machine learning–driven platform that supports farmers by delivering agricultural information, resources, and practical decision tools. The system provides weather forecasts, crop shopping, crop prediction, yield prediction, and crop stock and purchase history, while combining advanced predictive models to improve accuracy. Built on a scalable technology foundation, it targets farmers across different regions and farm sizes. The proposed portal aims to raise productivity, strengthen decision-making, and increase profitability by improving access to actionable data and insights.","International Journal of Innovative Research in Science  \nEngineering and Technology (IJIRSET)  \n(A Monthly, Peer Reviewed, Refereed, Scholarly Indexed, Open Access Journal)  \nImpact Factor: 8.699 Volume 14, Issue 4 , April 2025  \n|[www.ijirset.com](www.ijirset.com |A Monthly)[ |A Monthly](www.ijirset.com |A Monthly), Peer Reviewed & Refereed Journal| e-ISSN: 2319-8753| p-ISSN: 2347-6710|  \nVolume 14, Issue 4, April 2025  \n|DOI: 10.15680/IJIRSET.2025.1404432|  \nMachine Learning Driven Agricultural Portal Enhancing Crop Production and Decision-Making  \nG.Nivetha Sri, Sayeedha Firdouse Khan, Rotte Sachin, Shaik Asif, Sangem Ruthvik,  \nShaik Khasim Vali  \nAssistant Professor, Department ofCSE, Bharath Institute of Higher Education and Research, Chennai, India Assistant Professor, Department ofCSE, Bharath Institute of Higher Education and Research, Chennai, India Department ofCSE, Bharath Institute of Higher Education and Research, Chennai, India Department ofCSE, Bharath Institute of Higher Education and Research, Chennai, India Department ofCSE, Bharath Institute of Higher Education and Research, Chennai, India Department ofCSE, Bharath Institute of Higher Education and Research, Chennai, India  \nABSTRACT- The Agricultural Portal is an innovative platform designed to improve crop production by providing farmers with easy access to agricultural information, resources, and tools. The portal offers a wide range of features including weather forecasts, crop shopping, crop prediction, yield prediction, crop stock and purchase History. This technical paper outlines the development and implementation of the Agricultural Portal, highlighting its features and functionalities. The paper also explores the benefits of the portal for farmers, including increased productivity, improved decision-making, and enhanced profitability. Advanced machine learning models are integrated into the portal to improve its predictive capabilities. The portal is built on a robust technology platform that is scalable and adaptable to the needs of farmers of different sizes and geographies. The Agricultural Portal represents a significant step forward in the use of technology in agriculture. By providing farmers with easy access to information and resources, it has the potential to transform the way they farm and improve crop production across the globe by using different Machine Learning Algorithms. In the existing system There is only one feature but we are trying to add extra features and making into a single portal in the proposed system.  \nKEYWORDS-Machine Learning Algorithms, Agricultural portal, predicting systems  \nI. INTRODUCTION  \nAgriculture is vital to humanity, not only for food but also for employment and the economy. Crops or arable land are relatively ‘‘new,’’ even though people have been eating grains and plants for over 1,00,000 years. Around 11,000 years ago, during the Neolithic era, often referred to as the New Stone Age, people first actively managed the land and its vegetation. In India, agriculture provides a considerable portion of the country’s economic support and the majority of the country’s food needs. Due to India’s The associate editor coordinating the review of this manuscript and rapid population growth and important climatic changes, the demand chain and food supply must be maintained. Several scientific approaches have been included in agriculture to preserve the harmony between the supply and demand offood. The significant climatic variance makes it difficult for farmers to choose how to be more flexible and sustainable . inputs. food security. In India, Agriculture uses 70 percent of the water world wide .  \nThe integration of machine learning and data-driven approaches in agriculture has significantly enhanced crop yield prediction and price forecasting. Research in this domain has focused on leveraging supervised learning models, climate data, and secure cloud storage frameworks to improve agricultural decision-making. S","cbCairE3VfaaFHJU","https://ap.wps.com/l/cbCairE3VfaaFHJU","pdf",1421763,1,11,"English","en",105,"# Abstract\n# Introduction\n## Agriculture as a food and economic driver\n## Data-driven and machine learning for forecasting\n# Proposed Agricultural Portal\n## Portal features and functionalities","[{\"question\":\"What functions does the Agricultural Portal provide to farmers?\",\"answer\":\"The portal offers weather forecasts, crop shopping, crop prediction, yield prediction, and crop stock and purchase history. It centralizes information and decision tools in a single accessible platform.\"},{\"question\":\"How does machine learning improve crop production outcomes in this proposal?\",\"answer\":\"Advanced machine learning models are integrated to enhance predictive capabilities for crop and yield outcomes. This supports better agricultural planning and more informed decision-making.\"},{\"question\":\"Why is the portal designed to be scalable and suitable for different farm sizes and geographies?\",\"answer\":\"The paper emphasizes a robust technology platform that can scale and adapt to farmers across regions. 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