[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123047-en":3,"doc-seo-123047-105":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},123047,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Early Prediction of Frost Events in High Altitude Crops - Using Machine Learning Methods","Tropical highland agriculture in the Andes faces frequent frost risk at elevations around 2,500 m, where crops show high susceptibility to freezing conditions. This study proposes an early frost prediction model linking frost events with climate variables collected from thirteen meteorological stations within flower crops across nine municipalities in Cundinamarca. Temperature, relative humidity, dew point, photosynthetically active radiation, and precipitation serve as inputs, evaluated using precision, recall, true negative rate, accuracy, and F1 score. Results indicate low humidity, wind speed, and cloudiness with high thermal radiation as key preceding behavior, with the GBDT model achieving over 91% performance.","This is an open access article distributed under the terms of the Creative Commons Attribution License (CC-BY), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original work is properly cited.  \nAgricultural Engineering  \nEarly prediction of frost events in high altitude crops,  \nusing machine learning methods1  \nEvelin Calderón Caro2* , DaríoAntonio Castañeda Sánchez3 , John Willian Branch Bedoya2   \n10.1590/0034-737X2024710040  \nABSTRACT  \nIn the tropic, many crops are distributed in the highlands of provinces of the Andean regions at heights of 2,500 m asland constitute the areas with the highest susceptibility to the frost events occurrence. The study objective was to propose an early frost prediction model based on the relationships between frost events and climatic variables, modeled with machine learning methods. The climatic variables were obtained from thirteen meteorological stations located inside flower crops and distributed in nine municipalities of the Cundinamarca Department. The variables registered were temperature, relative humidity, dew point, photosynthetically active radiation, and precipitation, entered as explanatory variables of frost events. The metrics used for predictive performance evaluation of the five machine learning methods examined were precision, recall, true negative rate, accuracy, and F1 score. The variables’ climatic behavior of previous hours to a frost event are low humidity, wind speed and cloudiness, and high thermal radiation. The fourth of the five trained models performed well due to their classification evaluation metrics, greater than 91% . The cross-validation and statistical analysis demonstrated the higher accuracy of the GBDT model on frost events detection.  \nKeywords: forecast; artificial neural networks; gradient boosting; climatic variables.  \nINTRODUCTION  \nTemperature drives the latitudinal and elevational limits of plant species distribution across the earth. In some plants, freezing conditions define the lower temperature limit, suggested as the main driver of the species reduction, which increases with the latitude. Likewise, in tropical mountains, the upper elevational limit of many vascular plants is determined by freezing condition that expands in open areas such as tropical alpine ecosystems (e.g., Paramos) or transformed landscapes for agriculture. The tissues of the tropical plants, exposed to frost events (temperature \u003C 0°C), can be damaged due to cellular dehydration and membrane disintegration. At this temperature, the water in the extracellular spaces freezes to form ice crystals,  \ncausing cell death (Kochhar & Gujral, 2020; Rout, 2020) . Effects associated with this phenomenon include reduction in leaf size, wilting, chlorosis, necrosis, and poor reproductive development (Kochhar & Gujral, 2020) . In addition, photosynthetic rate, respiration, and protein synthesis rate can decrease due to temperature-dependent processes (Liet al., 2018; Kochhar & Gujral, 2020). Then, the magnitude of these effects varies according to plant species traits, plant age, and exposition time to these low temperatures.  \nFrost events have the highest probability of occurrence in paramos or transformed highland landscapes of the tropical alpine ecosystems. These events can affect the performance of many plants’ species, including multiple  \nSubmitted on May 26th, 2022 and accepted on July 29th, 2024.  \n1 This work is part of the first author’s Master Dissertation and it was funded by Soluciones Wiga and Growers Hub Trading Group Companies.  \n2 Universidad Nacional de Colombia, departamento de Ciencias de la Computación y de la Decisión, Grupo de Investigación y Desarrollo en Inteligencia Artificial, Medellín, Antioquia, Colombia, [evcalderonca@unal.edu.co](evcalderonca@unal.edu.co); [jwbranch@unal.edu.co](jwbranch@unal.edu.co)  \n3 Universidad Nacional de Colombia, departamento de Ciencias Agronómicas, Grupo de Investigación Fitotecnia","cbCaivgaC9vzYvJI","https://ap.wps.com/l/cbCaivgaC9vzYvJI","pdf",1168229,1,11,"English","en",105,"# Abstract\n# Introduction\n## Temperature limits and frost damage mechanisms\n## Frost occurrence in highland ecosystems and agricultural relevance\n## Climate variables, data sources, and study motivation","[{\"question\":\"What is the main objective of the study on frost events?\",\"answer\":\"To propose an early frost prediction model using relationships between frost events and climate variables, modeled with machine learning methods.\"},{\"question\":\"Which climate variables were used as explanatory inputs for predicting frost events?\",\"answer\":\"Temperature, relative humidity, dew point, photosynthetically active radiation, and precipitation were used as explanatory variables.\"},{\"question\":\"Which model performed best in detecting frost events?\",\"answer\":\"The fourth of the five trained models performed well with classification metrics above 91%, and cross-validation and statistical analysis showed the GBDT model had higher accuracy for frost event detection.\"}]","Early Prediction of Frost Events in High Altitude Crops - Using Machine Learning Methods | PDF",1785814382,28,{"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},"early-prediction-of-frost-events-in-high-altitude-crops-using-machine-learning-methods","",{"@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/early-prediction-of-frost-events-in-high-altitude-crops-using-machine-learning-methods/123047/",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","2026-08-04",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},"What is the main objective of the study on frost events?","Question",{"text":75,"@type":76},"To propose an early frost prediction model using relationships between frost events and climate variables, modeled with machine learning methods.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which climate variables were used as explanatory inputs for predicting frost events?",{"text":80,"@type":76},"Temperature, relative humidity, dew point, photosynthetically active radiation, and precipitation were used as explanatory variables.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performed best in detecting frost events?",{"text":84,"@type":76},"The fourth of the five trained models performed well with classification metrics above 91%, and cross-validation and statistical analysis showed the GBDT model had higher accuracy for frost event detection.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]