[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122445-en":3,"doc-seo-122445-105":29,"detail-sidebar-cat-0-en-105":89},{"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":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},122445,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Using Machine Learning to classify low-growing forage plants of Megathyrsus maximus (Syn. Panicum maximum) - Research findings","Low-growing forage plants of Megathyrsus maximus (syn. Panicum maximum) are a key alternative for meat production pastures spanning over 160 million hectares. This study trained and validated machine learning classifiers to identify the most accurate model for distinguishing cultivars and genotypes using dry mass production plus pasture structural and morphogenic variables. Logistic Regression and Random Forest were evaluated with Percentage Correct (PC), Kappa coefficient, and confusion matrices. Random Forest achieved the best performance (68.95% PC, Kappa 0.53) and the most correct instances, while Logistic Regression showed lower accuracy (53.37% PC, Kappa 0.30).","August 12th to 16th,  \nUsing Machine Learning to classify low-growing forage plants of Megathyrsus  \nmaximus (Syn. Panicum maximum)  \nNéstor Eduardo V. Frontado 1*, Gelson dos S. Difante 1 , Alexandre R. Araújo2 , Denise B. Montagner2 , Hitalo Rodrigues da Silva 1 , Larissa P. R. Teodoro3  \n1 Postgraduate Program in Animal Science, Faculty of Veterinary Medicine and Zootechny, Campo Grande/MS; Federal University of Mato Grosso do Sul;  \n2 Researcher, Campo Grande/MS; Brazilian Agricultural Research CorporationEmbrapa Beef Cattle.3 Postgraduate Program in Agronomy, MS; Chapadão do Sul  \nCampus. Federal University of Mato Grosso do Sul.  \n*Doctoral student ~~ ~~ [nestor.villamizar.frontado@gmail.com](nestor.villamizar.frontado@gmail.com)  \nForage plants of the species Megathyrsus maximus (Syn. Panicum maximum) are an important alternative for the more than 160 million hectares devoted to meat production on pasture. Train and validate machine learning algorithms to identify the most accurate model in classifying cultivars and genotypes of this species. The objective was to evaluate the performance of two classification models low-growing M. maximus forages using dry mass production data and pasture structural and morphogenic variables as inputs. The machine learning models tested were Logistic Regression (REGL) and Random Forest (RF) and two metrics were used to assess accuracy, Percentage Correct (PC) and Kappa coefficient. The data was also subjected to confusion matrix analysis. The data was collected in a greenhouse at Embrapa Beef Cattle from September 2021 to February 2022. The soil used was typical Dystrophic Red, collected in the Cerrado in the 20-40 cm layer. The experimental design was in randomized blocks in a 3x2x5 factorial scheme, with three forage plants (BRS Tamani, PM422, and PM408), two doses of phosphorus (P), 19 mg dm-3 and 116 mg dm-3 ; five doses of dolomitic limestone 0, 326, 653, 1306 and 2612 mg dm-3 . The best performance for classifying cultivars and genotypes of M. maximus was in the RF model, with an accuracy of 68.95%(PC) and 0.53 (Kappa), and the lowest in the REGL model, with an accuracy of 53.37%(PC) and 0.30 (Kappa) . For the confusion matrix, the RF model showed the highest number of correct instances (307) and the lowest number of incorrect instances (143) . The REGL model had the fewest correct instances (236) and the most incorrect instances (214) . The use of machine learning with dry mass production, and structural and morphogenic data inputs is viable for classifying M. maximus cultivars and genotypes, and the Random Forest model is an alternative for discriminating low-growing Megathyrsus maximus forage plants.  \nKeywords: Computational intelligence, Machine learning, Panicum maximum, morphogenesis.  \nAcknowledgments: UFMS, Embrapa Beef Cattle, CNPq, UNIPASTO, CAPES, Fundect.","cbCaiihgF7Yi4dTX","https://ap.wps.com/l/cbCaiihgF7Yi4dTX","pdf",258324,1,"English","en",105,"# Study objective\n## Models and evaluation metrics\n## Data collection and experimental design\n## Results and implications","[{\"question\":\"What was the objective of using machine learning in this study?\",\"answer\":\"To evaluate and compare the performance of two classification models for distinguishing low-growing Megathyrsus maximus cultivars and genotypes using dry mass, structural, and morphogenic variables.\"},{\"question\":\"Which machine learning models were tested, and how were they evaluated?\",\"answer\":\"Logistic Regression and Random Forest were tested using Percentage Correct (PC) and the Kappa coefficient, with additional confusion matrix analysis.\"},{\"question\":\"Which model performed best and how did it compare with the other model?\",\"answer\":\"Random Forest performed best, reaching 68.95% PC and Kappa 0.53, whereas Logistic Regression had 53.37% PC and Kappa 0.30, with fewer correct instances and more incorrect ones.\"}]","Using Machine Learning to classify low-growing forage plants of Megathyrsus maximus (Syn. Panicum maximum) - Research findings | PDF",1785810666,3,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":84,"head_meta":86,"extra_data":88,"updated_unix":27},"using-machine-learning-to-classify-low-growing-forage-plants-of-megathyrsus-maximus-syn-panicum-maximum-research-findings","",{"@graph":35,"@context":83},[36,52,66],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,49],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":28},"https://docshare.wps.com/document/research-report/",{"item":50,"name":13,"@type":42,"position":51},"https://docshare.wps.com/document/using-machine-learning-to-classify-low-growing-forage-plants-of-megathyrsus-maximus-syn-panicum-maximum-research-findings/122445/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":22,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69,75,79],{"name":70,"@type":71,"acceptedAnswer":72},"What was the objective of using machine learning in this study?","Question",{"text":73,"@type":74},"To evaluate and compare the performance of two classification models for distinguishing low-growing Megathyrsus maximus cultivars and genotypes using dry mass, structural, and morphogenic variables.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"Which machine learning models were tested, and how were they evaluated?",{"text":78,"@type":74},"Logistic Regression and Random Forest were tested using Percentage Correct (PC) and the Kappa coefficient, with additional confusion matrix analysis.",{"name":80,"@type":71,"acceptedAnswer":81},"Which model performed best and how did it compare with the other model?",{"text":82,"@type":74},"Random Forest performed best, reaching 68.95% PC and Kappa 0.53, whereas Logistic Regression had 53.37% PC and Kappa 0.30, with fewer correct instances and more incorrect ones.","https://schema.org",{"og:url":50,"og:type":85,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":87,"canonical":50},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":90},[91,95,99,103,108,113,118,121,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":92,"show_sort_weight":93,"slug":94},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":96,"show_sort_weight":97,"slug":98},"Literature",80,"literature",{"id":51,"doc_module":4,"doc_module_name":45,"category_name":100,"show_sort_weight":101,"slug":102},"Exam",70,"exam",{"id":104,"doc_module":4,"doc_module_name":45,"category_name":105,"show_sort_weight":106,"slug":107},5,"Comic",60,"comic",{"id":109,"doc_module":4,"doc_module_name":45,"category_name":110,"show_sort_weight":111,"slug":112},6,"Technology",50,"technology",{"id":114,"doc_module":4,"doc_module_name":45,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":119,"slug":120},30,"research-report",{"id":122,"doc_module":4,"doc_module_name":45,"category_name":123,"show_sort_weight":124,"slug":125},9,"Religion & Spirituality",20,"religion-spirituality",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":124,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":104,"slug":136},19,"General","general"]