[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127372-en":3,"doc-seo-127372-105":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},127372,962085564549,"Genevieve","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Advanced Crop Yield Prediction Using Machine Learning and Deep Learning - A Comprehensive Review","Machine learning and deep learning methods have substantially advanced crop yield prediction, improving accuracy and reliability for agricultural decision-making. This review evaluates ML and DL algorithms for yield forecasting and examines how remote sensing data from satellites and drones provides high-resolution inputs. It surveys state-of-the-art approaches, frequently used features, data sources, and evaluation metrics, and compares ML versus DL performance. The review concludes that DL benefits most from large datasets and proposes future generalized models across crops and regions.","Advanced crop yield prediction using machine learning and deep learning: a comprehensive review  \nAyush Anand, Kavita Jhajharia  \nDepartment of Information Technology, Faculty of Engineering, Manipal University Jaipur, Rajasthan, India  \n\n| Article history:\u003Cbr>Received Aug 28, 2024 Revised Dec 28, 2024 Accepted Jan 22, 2025 | The advancement of machine learning (ML) and deep learning (DL) techniques has significantly improved crop yield prediction, making it more accurate and reliable. In this review, the implementation of ML and DL algorithms for crop yield prediction is thoroughly investigated, focusing on their crucial role in enhancing crop productivity. Along with ML and DL algorithms examine, the review analyses the use of remote sensing technologies, such as satellite and drone data, in providing high-resolution inputs essential for accurate yield predictions. The study identifies the state of art algorithms, most used features, data sources and evaluation metrics, providing a comparison of ML and DL. The findings indicate that DL models are more effective with large datasets, while ML models remain robust for smaller datasets. The future directions are proposed to develop the generalised models for different crops and regions. The review aims to assist researchers by summarising state of art techniques and identifying the present.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| --- | --- |\n| Keywords:\u003Cbr>Crop yield prediction Deep learning\u003Cbr>Machine learning\u003Cbr>Remote sensing Systematic literature review Vegetation indices |  |\n\nCorresponding Author:  \nKavita Jhajharia  \nDepartment of Information Technology, Faculty of Engineering, Manipal University Jaipur Rajasthan 303007, India  \n[Email: Kavita.jhajharia@jaipur.manipal.edu](Email: Kavita.jhajharia@jaipur.manipal.edu)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThe field of computer science is constantly advancing and ever-evolving, driven by the pursuit of even more sophisticated solutions to complex problems. Machine learning (ML) has emerged as a powerful paradigm within this domain, enabling computers to learn and adapt without explicit programming [1] . ML encompasses a diverse set of techniques, each with its unique strengths and applications [2] . Some common approaches include supervised learning, which involves training algorithms on labelled data to perform tasks like classification and regression. Unsupervised learning, on the other hand, focuses on uncovering hidden structures within unlabelled data, allowing for tasks like data clustering and dimensionality reduction. Additionally, reinforcement learning enables systems to learn through trial and error, interacting with an environment.  \nML is also making significant contribution in the agriculture industry, particularly in the area of crop yield prediction [3] . ML can help farmers and policymakers mitigate food insecurities. It is based on the concept of statistics and ML in which crop yield is predicted using historical data associated with the crops like climate, soil, and region. Modern tools such as satellites, drones and sensors are also used to obtain data and monitor crops. One of the key drivers of this progress is the integration of remote sensing technology [4] . Satellites and drones equipped with various sensors can gather data on factors like soil moisture, vegetation health, and weather patterns from a distance [5] . These models then identify intricate relationships between these diverse factors and historical crop yields, allowing for accurate predictions.  \nWith surge in demand of food with increasing population, ML in agriculture has propelled to the forefront of research aimed at advancing the sector. However, navigating the complexities of choosing suitable datasets, algorithms, and methodologies can be challenging for researchers as these vary greatly depending on the area of study and type of crop. This review paper addresses questions such as the most used ","cbCaic8qWvvJzf4H","https://ap.wps.com/l/cbCaic8qWvvJzf4H","pdf",554519,1,14,"English","en",105,"# Introduction\n## Machine learning in agriculture and yield prediction\n## Role of remote sensing and data sources\n## Research questions and review objectives\n## Related works and key findings","[{\"question\":\"How does machine learning contribute to crop yield prediction in agriculture?\",\"answer\":\"ML predicts crop yield using historical relationships among climate, soil, region, and other associated data. It supports farmers and policymakers by improving forecasting for food security planning.\"},{\"question\":\"What role does remote sensing play in improving yield predictions?\",\"answer\":\"Remote sensing technologies such as satellites and drones collect high-resolution information on soil moisture, vegetation health, and weather patterns. These inputs help models learn complex relationships with historical yields.\"},{\"question\":\"What does the review conclude about ML versus deep learning performance?\",\"answer\":\"Deep learning models are reported as more effective when large datasets are available, while traditional ML models remain robust for smaller datasets. The review also contrasts evaluation practices, features, and commonly used models.\"}]","Advanced Crop Yield Prediction Using Machine Learning and Deep Learning - A Comprehensive Review | PDF",1785938553,35,{"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},"advanced-crop-yield-prediction-using-machine-learning-and-deep-learning-a-comprehensive-review","",{"@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/advanced-crop-yield-prediction-using-machine-learning-and-deep-learning-a-comprehensive-review/127372/",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","2026-08-05",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},"How does machine learning contribute to crop yield prediction in agriculture?","Question",{"text":75,"@type":76},"ML predicts crop yield using historical relationships among climate, soil, region, and other associated data. It supports farmers and policymakers by improving forecasting for food security planning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What role does remote sensing play in improving yield predictions?",{"text":80,"@type":76},"Remote sensing technologies such as satellites and drones collect high-resolution information on soil moisture, vegetation health, and weather patterns. These inputs help models learn complex relationships with historical yields.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the review conclude about ML versus deep learning performance?",{"text":84,"@type":76},"Deep learning models are reported as more effective when large datasets are available, while traditional ML models remain robust for smaller datasets. 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