[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126010-en":3,"doc-seo-126010-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126010,687207024643,"Oliver","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","WEED IDENTIFICATION USING IMAGE ANALYSIS AND MACHINE LEARNING - A Thesis","Increasing cases of herbicide-resistant weeds and the labor-intensive nature of traditional weed control methods necessitate more efficient and accurate solutions in agriculture. This thesis evaluates YOLO series algorithms (YOLOv5–YOLOv10) to detect glyphosate-resistant Palmer amaranth, identifying YOLOv9 as the top performer with the highest mean average precision (mAP). It also compares YOLOv8, YOLOv9, YOLOv10, and Faster R-CNN for multi-species weed detection, with YOLOv9 again proving most robust. Finally, it applies YOLOv9 to assess PRE/POST herbicide effectiveness in Enlist soybeans by linking drone-derived weed counts with visual control ratings.","WEED IDENTIFICATION USING IMAGE ANALYSIS AND MACHINE  \nLEARNING  \nA Thesis  \nPresented to the Faculty of the Graduate School  \nof Cornell University  \nIn Partial Fulfillment of the Requirements for the Degree of  \nMaster of Science  \nby  \nAkhilesh Sharma  \n© 2024 Akhilesh Sharma  \nABSTRACT  \nIncreasing cases of herbicide-resistant weeds and the labor-intensive nature of traditional weed control methods necessitate more efficient and accurate solutions in agriculture. This thesis addresses these challenges by evaluating YOLO series algorithms (YOLOv5 to YOLOv10) to determine the most effective model for detecting glyphosate-resistant Palmer amaranth, identifying YOLOv9 as the best performer with the highest mean average precision (mAP) . We then compared YOLOv8, YOLOv9, YOLOv10, and Faster R-CNN models for multi-species weed detection and found YOLOv9 to be the most robust model. Finally, the study investigated the use of YOLOv9 algorithm for evaluating the effectiveness of various PRE and POST herbicides for weed control in 2,4-D/glufosinate/glyphosate-resistant (Enlist) soybeans. automates weed control ratings in Enlist soybean fields by correlating weed counts from drone imagery with visual control ratings. Altogether, the findings ofthis research demonstrated the potential for significantly improving weed management practices through species-specific weed identification using YOLO based object detection models highlighting the transformative impact of advanced machine learning algorithms on developing sustainable and economic weed  \nmanagement practices.  \nBIOGRAPHICAL SKETCH  \nAkhilesh Sharma hails from Chandigarh, India, a city known for its modern architecture and rich cultural heritage. He completed his Bachelor of Technology in Electrical Engineering, where he developed a strong foundation in electrical systems, control engineering, and signal processing. Akhilesh's undergraduate studies sparked a keen interest in the practical applications of technology, leading him to explore the intersection of electrical engineering and agricultural innovation.  \nDriven by a passion for leveraging technology to solve real-world problems, Akhilesh decided to pursue a Masters degree in Soil and Crop Sciences at Cornell. His research focuses on weed identification using image analysis and machine learning, aiming to enhance weed management and optimize resource use.  \nAkhilesh's wants not only to advance agricultural practices but also to safeguard the environment. His work embodies the proverb, \"We do not inherit the  \nearth from our ancestors; we borrow it from our children,\" reflecting his dedication to sustainable innovation. Through his endeavors, Akhilesh strives to cultivate a future where technology and nature coexist harmoniously, benefiting both farmers and the  \nplanet.  \nTo my mother Kanta and my sister Priyanka, for their boundless love and support  \nACKNOWLEDGMENTS  \nComing from an engineering background into agriculture was a complete change forme. If not for the incredible people at Cornell University, I would not have been able to come this far. I am deeply thankful for their guidance and support throughout this  \njourney.  \nTo my professors and mentors, your wisdom and encouragement have been invaluable. To my colleagues and friends, your camaraderie and assistance have made this transition smoother and more enjoyable.  \nLastly, to my sister and my mother, for their boundless love and support, I am eternally grateful. Your unwavering belief in me has been my greatest source of  \nstrength.  \nTABLE OF CONTENTS  \nBiographical Sketch ................................................................................................iii  \nAcknowledgments .................................................................................................. v  \nList of Figures .........................................................................................................vii  \nList of Tables ...............................","cbCaicGxF3xK6oUk","https://ap.wps.com/l/cbCaicGxF3xK6oUk","pdf",5441037,2,1,112,"English","en",105,"# Detection of Glyphosate-Resistant Palmer Amaranth (Amaranthus palmeri) using YOLO Series Algorithms\n## 1.1 Abstract\n## 1.2 Introduction\n## 1.3 Materials and methods\n## 1.4 Results and discussion\n## 1.5 Conclusion\n## 1.6 References\n# Comparative performance of YOLOv8, YOLOv9, YOLOv10 and Faster R-CNN models for detection of multiple weed species\n## 2.1 Abstract\n## 2.2 Introduction\n## 2.3 Materials and methods\n## 2.4 Results and discussion\n## 2.5 Conclusion\n## 2.6 References","[{\"question\":\"Which YOLO model performed best for detecting glyphosate-resistant Palmer amaranth?\",\"answer\":\"YOLOv9 achieved the highest mean average precision (mAP) among the evaluated YOLO series algorithms (YOLOv5 to YOLOv10).\"},{\"question\":\"How did YOLOv9 compare with Faster R-CNN for multi-species weed detection?\",\"answer\":\"When comparing YOLOv8, YOLOv9, YOLOv10, and Faster R-CNN, the study found YOLOv9 to be the most robust model for detecting multiple weed species.\"},{\"question\":\"How was the YOLOv9 algorithm used to evaluate herbicide effectiveness in Enlist soybeans?\",\"answer\":\"The study used YOLOv9 to automate weed control ratings by correlating weed counts extracted from drone imagery with visual control ratings for PRE and POST herbicide treatments.\"}]","WEED IDENTIFICATION USING IMAGE ANALYSIS AND MACHINE LEARNING - A Thesis | PDF",1785902538,282,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"weed-identification-using-image-analysis-and-machine-learning-a-thesis","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/weed-identification-using-image-analysis-and-machine-learning-a-thesis/126010/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Which YOLO model performed best for detecting glyphosate-resistant Palmer amaranth?","Question",{"text":76,"@type":77},"YOLOv9 achieved the highest mean average precision (mAP) among the evaluated YOLO series algorithms (YOLOv5 to YOLOv10).","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How did YOLOv9 compare with Faster R-CNN for multi-species weed detection?",{"text":81,"@type":77},"When comparing YOLOv8, YOLOv9, YOLOv10, and Faster R-CNN, the study found YOLOv9 to be the most robust model for detecting multiple weed species.",{"name":83,"@type":74,"acceptedAnswer":84},"How was the YOLOv9 algorithm used to evaluate herbicide effectiveness in Enlist soybeans?",{"text":85,"@type":77},"The study used YOLOv9 to automate weed control ratings by correlating weed counts extracted from drone imagery with visual control ratings for PRE and POST herbicide treatments.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]