{"id":198895,"date":"2026-02-20T12:28:20","date_gmt":"2026-02-20T11:28:20","guid":{"rendered":"https:\/\/helloproject.es\/vigilancia\/un-algoritmo-de-ia-identifica-especies-arboreas-en-la-amazonia\/"},"modified":"2026-02-20T12:28:20","modified_gmt":"2026-02-20T11:28:20","slug":"un-algoritmo-de-ia-identifica-especies-arboreas-en-la-amazonia","status":"publish","type":"post","link":"https:\/\/helloproject.es\/vigilancia\/un-algoritmo-de-ia-identifica-especies-arboreas-en-la-amazonia\/","title":{"rendered":"Un algoritmo de IA identifica especies arb\u00f3reas en la Amazonia"},"content":{"rendered":"

El monitoreo de la\u00a0fauna silvestre en los bosques tropicales es fundamental para conocer el estado de los ecosistemas y establecer planes de conservaci\u00f3n.<\/p>\n

Un equipo de investigaci\u00f3n del Museo Nacional de Ciencias Naturales (MNCN-CSIC) ha desarrollado\u00a0TropiCam-AI, el primer algoritmo de inteligencia artificial que permite identificar, a partir de im\u00e1genes de c\u00e1maras trampa, las especies animales que habitan en el dosel de los bosques h\u00famedos neotropicales.<\/p>\n

Ofrece nuevas posibilidades para la investigaci\u00f3n y conservaci\u00f3n de\u00a0especies dif\u00edciles de observar\u00a0mediante m\u00e9todos tradicionales<\/p>\n

En concreto, han desarrollado esta herramienta \u2013que alcanza un\u00a095 % de precisi\u00f3n\u2013\u00a0para 84 grupos de mam\u00edferos y aves neotropicales.<\/p>\n

Esta tecnolog\u00eda de c\u00f3digo abierto ofrece numerosas posibilidades para la investigaci\u00f3n y conservaci\u00f3n de especies dif\u00edciles de observar mediante m\u00e9todos tradicionales.<\/p>\n

\u201cCon TropiCam-AI podemos procesar r\u00e1pidamente cientos de miles de im\u00e1genes de fauna arb\u00f3rea en los bosques tropicales con gran precisi\u00f3n, y transformar la manera en la que estudiamos estos ecosistemas\u201d, se\u00f1ala el\u00a0autor principal del estudio,\u00a0Andrea Zampetti,<\/p>\n

<\/a><\/p>\n

Fauna neotropical<\/h2>\n

Esta herramienta es capaz de reconocer\u00a084 taxones\u00a0(63 especies, 13 g\u00e9neros, 5 familias y 3 \u00f3rdenes) de mam\u00edferos y aves, incluyendo todos los g\u00e9neros de monos del continente americano.<\/p>\n

Para desarrollar TropiCam-AI, el equipo entren\u00f3 el algoritmo con m\u00e1s de 180.000 im\u00e1genes de c\u00e1maras trampa procedentes de Brasil, Per\u00fa, Costa Rica y la Guayana Francesa, adem\u00e1s de 54.000 im\u00e1genes de ciencia ciudadana obtenidas en la plataforma iNaturalist.<\/p>\n

Esta herramienta es capaz de reconocer\u00a084 taxones\u00a0de mam\u00edferos y aves,\u00a0incluyendo todos los g\u00e9neros de\u00a0monos\u00a0del continente americano<\/p>\n

Posteriormente, para mejorar la precisi\u00f3n, se implement\u00f3 una\u00a0estrategia de clasificaci\u00f3n jer\u00e1rquica\u00a0que permite asignar especies dif\u00edciles de clasificar a niveles taxon\u00f3micos superiores (g\u00e9nero, familia o orden), lo que incrementa la precisi\u00f3n y posibilita que el modelo funcione \u00a0en lugares y especies no contemplados durante el entrenamiento.<\/p>\n

Por ejemplo, una nueva especie de mono ara\u00f1a podr\u00eda ser reconocida como perteneciente a un tax\u00f3n (Ateles), incluso si el algoritmo no fue entrenado con ella.<\/p>\n

<\/a><\/p>\n

Dise\u00f1ada para investigadores<\/h2>\n

Aunque los algoritmos de inteligencia artificial para el reconocimiento de especies se han vuelto cada vez m\u00e1s comunes en los \u00faltimos a\u00f1os, las herramientas disponibles se centran en especies terrestres de ambientes templados.<\/p>\n

TropiCam-AI est\u00e1 disponible en la plataforma\u00a0AddaxAI, una interfaz interactiva accesible para los investigadores independientemente de sus conocimientos t\u00e9cnicos, con el prop\u00f3sito de incrementar la accesibilidad a profesionales que trabajan en el Neotr\u00f3pico.<\/p>\n

Actualmente, las herramientas disponibles se centran en especies terrestres de ambientes templados<\/p>\n

Su f\u00e1cil acceso permitir\u00e1 realizar inventarios r\u00e1pidos de especies arb\u00f3reas, lo que facilita el desarrollo de estudios de ecolog\u00eda del comportamiento, ecolog\u00eda de comunidades y conservaci\u00f3n en uno de los ecosistemas m\u00e1s diversos y menos explorados del planeta: el neotr\u00f3pico.<\/p>\n

\u201cLa herramienta que hemos desarrollado no solo acelera el procesamiento de datos, sino que\u00a0democratiza el acceso a la inteligencia artificial para quienes realizan su trabajo en estos ecosistemas\u201d, concluye la investigadora del MNCN\u00a0Ana Ben\u00edtez-L\u00f3pez.<\/p>\n

Referencia:\u00a0<\/p>\n

Zampetti, A. et al.\u00a0Introducing TropiCam-AI: A taxonomically flexible automated classifier of\u00a0Neotropical arboreal mammals and birds from camera-trap data. Methods in Ecology and Evolution<\/a>. 2026<\/p>","protected":false},"excerpt":{"rendered":"

El monitoreo de la\u00a0fauna silvestre en los bosques tropicales es fundamental para conocer el estado de los ecosistemas y establecer planes de conservaci\u00f3n. Un equipo de investigaci\u00f3n del Museo Nacional de Ciencias Naturales (MNCN-CSIC) ha desarrollado\u00a0TropiCam-AI, el primer algoritmo de inteligencia artificial que permite identificar, a partir de im\u00e1genes de c\u00e1maras trampa, las especies animales […]<\/p>\n","protected":false},"author":5,"featured_media":0,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[5],"tags":[],"class_list":["post-198895","post","type-post","status-publish","format-standard","hentry","category-noticias"],"yoast_head":"\nUn algoritmo de IA identifica especies arb\u00f3reas en la Amazonia - bip4ex<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/helloproject.es\/vigilancia\/un-algoritmo-de-ia-identifica-especies-arboreas-en-la-amazonia\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Un algoritmo de IA identifica especies arb\u00f3reas en la Amazonia\" \/>\n<meta property=\"og:description\" content=\"El monitoreo de la\u00a0fauna silvestre en los bosques tropicales es fundamental para conocer el estado de los ecosistemas y establecer planes de conservaci\u00f3n. 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