[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121373-en":3,"doc-seo-121373-105":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":20,"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},121373,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Advanced classification techniques for weed and crop species recognition using machine learning algorithms - Key application in precision agriculture","An intelligent machine learning framework integrates image analysis with environmental data to deliver precision weed management through accurate multi-species classification. The approach uses efficient feature extraction for supervised learning, leveraging color, texture, and shape cues to train robust models with low computational complexity. Experiments across ten weed and crop species in diverse environments achieve 94.3% average classification accuracy. Results also indicate a 90% reduction in herbicide application versus traditional methods, with real-time inference under 1.5 seconds per frame for drone and autonomous deployment, supporting sustainable farming.","Advanced classification techniques for weed and crop species recognition using machine learning algorithms  \nSathya Rajendran, K.S. Thirunavukkarasu  \nDepartment of Computer Science, Vels Institute of Science, Technology and Advanced Studies, Chennai, India  \nArticle history:  \nReceived Sep 1, 2024 Revised Jan 26, 2025 Accepted Mar 28, 2025  \nKeywords:  \nClassification Convolutional neural network Machine learning Optimization  \nWeed and crop management  \nCorresponding Author:  \nThis study proposes an intelligent machine learning framework integrating image analysis and environmental data for precision weed management. The framework leverages efficient feature extraction techniques combined with supervised machine learning algorithms to accurately classify multiple species. Features such as color, texture, and shape characteristics are utilized for model training, enabling high-precision classification while maintaining low computational complexity. The experimental results demonstrate the robustness of the approach, achieving an average classification accuracy of 94.3% across ten weed and crop species in diverse agricultural environments. The system also achieved a 90% reduction in herbicide application compared to traditional methods, showcasing its potential for sustainable farming. Realtime testing confirmed the framework’s efficiency, processing images in under 1.5 seconds per frame, making it suitable for deployment in drones and autonomous farming equipment. These results underscore the practical and scalable nature of the proposed system in automating weed management and advancing sustainable agricultural practices.  \nThis is an open access article under the CC BY-SA license.  \nSathya Rajendran  \nDepartment of Computer Science, Vels Institute of Science, Technology and Advanced Studies  \nPV Vaithiyalingam Rd, Velan Nagar, Krishnapuram, Pallavaram, Chennai, Tamil Nadu 600117, India  \nEmail: [sathya.r0714@gmail.com](sathya.r0714@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nAgriculture plays a critical role in sustaining the world's population, providing food security, and supporting the livelihoods of millions. However, one of the primary challenges faced by farmers today is the accurate identification and classification of weeds and crops in agricultural fields. Weeds, which compete with crops for essential nutrients, water, and sunlight, can significantly reduce crop yields and quality if not managed properly. Effective weed control is a key component of precision agriculture, which seeks to optimize field-level management using advanced technologies to increase crop productivity and reduce environmental impact [1]–[5] . Unfortunately, traditional weed management techniques, such as manual inspection and broad-spectrum herbicide application, are often labor-intensive, time-consuming, and environmentally harmful. To address these challenges, automated weed and crop classification systems powered by machine learning technologies have emerged as a promising solution in modern precision agriculture. The accurate classification of weed and crop species in agricultural fields is crucial for effective weed management, which in turn can lead to improved crop yields, lower production costs, and reduced environmental degradation. However, weed and crop species classification in large-scale agricultural fields remains a difficult and complex task for several reasons. First, agricultural fields are often large, heterogeneous environments where weeds and crops coexist in varying densities and distributions. Therefore, there is a pressing need for an automated solution that can accurately classify weeds and crops in real-time  \nand at scale. In recent years, precision agriculture has emerged as an innovative approach to managing agricultural fields with high levels of precision and accuracy. The central idea behind precision agriculture is to use data-driven technologies to optimize crop production, reduce resource use, an","cbCaiq3eZHi5sTcT","https://ap.wps.com/l/cbCaiq3eZHi5sTcT","pdf",554006,1,10,"English","en",105,"# Abstract\n# Introduction\n## Motivation and challenges in weed and crop identification\n## Limitations of traditional weed management\n## Role of precision agriculture and automated classification\n## Objectives and structure of the study","[{\"question\":\"What framework does the study propose for weed management?\",\"answer\":\"It proposes an intelligent machine learning framework that integrates image analysis with environmental data to classify multiple weed and crop species for precision weed management.\"},{\"question\":\"Which features and algorithms are used for species classification?\",\"answer\":\"The framework uses color, texture, and shape characteristics for model training and employs supervised machine learning algorithms, including convolutional neural network-based approaches.\"},{\"question\":\"How effective and fast is the proposed system?\",\"answer\":\"Experiments report an average classification accuracy of 94.3% across ten weed and crop species. Real-time testing processes images in under 1.5 seconds per frame, supporting deployment in drones and autonomous farming equipment.\"}]","Advanced classification techniques for weed and crop species recognition using machine learning algorithms - Key application in precision agriculture | PDF",1785735317,25,{"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-classification-techniques-for-weed-and-crop-species-recognition-using-machine-learning-algorithms-key-application-in-precision-agriculture","",{"@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-classification-techniques-for-weed-and-crop-species-recognition-using-machine-learning-algorithms-key-application-in-precision-agriculture/121373/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What framework does the study propose for weed management?","Question",{"text":75,"@type":76},"It proposes an intelligent machine learning framework that integrates image analysis with environmental data to classify multiple weed and crop species for precision weed management.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which features and algorithms are used for species classification?",{"text":80,"@type":76},"The framework uses color, texture, and shape characteristics for model training and employs supervised machine learning algorithms, including convolutional neural network-based approaches.",{"name":82,"@type":73,"acceptedAnswer":83},"How effective and fast is the proposed system?",{"text":84,"@type":76},"Experiments report an average classification accuracy of 94.3% across ten weed and crop species. 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