{"id":206,"date":"2020-01-29T15:50:00","date_gmt":"2020-01-29T14:50:00","guid":{"rendered":"https:\/\/geopard.tech\/?p=206"},"modified":"2023-05-18T22:11:51","modified_gmt":"2023-05-18T20:11:51","slug":"deteccion-de-nubes-y-sombras-en-la-agricultura","status":"publish","type":"post","link":"https:\/\/geopard.tech\/es\/blog\/cloud-and-shadow-detection-in-agriculture\/","title":{"rendered":"Detecci\u00f3n de Nubes y Sombras en Agricultura"},"content":{"rendered":"
La detecci\u00f3n de nubes y sombras es una de las tareas m\u00e1s importantes en las soluciones anal\u00edticas de teledetecci\u00f3n.<\/p>\r\n\r\n\r\n\r\n
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Si la imagen satelital completa est\u00e1 limpia (90%), a\u00fan existe una probabilidad de 10% de que su campo se encuentre bajo nubes o zonas de sombra. Lo contrario tambi\u00e9n es cierto: muchos sistemas no procesan im\u00e1genes con nubes por encima de 70%, aunque los 30% restantes pueden ayudar al agr\u00f3nomo o al agricultor a tomar la decisi\u00f3n correcta durante la temporada.<\/p>\r\n\r\n\r\n\r\n
En GeoPard, resolvemos este problema con la ayuda de varios algoritmos de aprendizaje autom\u00e1tico que funcionan con una precisi\u00f3n muy alta.<\/p>\r\n<\/div>\r\n\r\n\r\n\r\n
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Definimos una m\u00e1scara de nubes y una m\u00e1scara de sombras a nivel de toda la imagen, y para cada par campo-imagen, consideramos qu\u00e9 porcentaje del campo est\u00e1 cubierto por nubes o sombras de las nubes.<\/p>\r\n\r\n\r\n\r\n
En el an\u00e1lisis autom\u00e1tico, solo utilizamos im\u00e1genes completamente libres de nubes para un campo espec\u00edfico, lo que permite al usuario tomar decisiones con confianza bas\u00e1ndose en an\u00e1lisis de varios a\u00f1os.<\/p>\r\n\r\n\r\n\r\n
El usuario de GeoPard tiene la oportunidad de visualizar e incluso ejecutar los an\u00e1lisis por s\u00ed mismo, incluso en im\u00e1genes parcialmente nubosas.<\/p>\r\n<\/div>\r\n\r\n\r\n\r\n
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Esto puede resultar muy \u00fatil, por ejemplo, durante la temporada de aplicaci\u00f3n de herbicidas de tasa variable, bas\u00e1ndose en la \u00faltima imagen satelital, donde el 101% del \u00e1rea del campo est\u00e1 cubierta por nubes. Esto significa que, para el 90% restante del \u00e1rea del campo, la decisi\u00f3n se basar\u00e1 en datos verificados.<\/p>\r\n\r\n\r\n\r\n
Adem\u00e1s, los usuarios de GeoPard pueden comprobar f\u00e1cilmente las im\u00e1genes de origen a nivel regional en la vista de infrarrojo cercano, lo que ayuda a distinguir el terreno despejado de las nubes y las sombras (v\u00e9ase la imagen adjunta).<\/p>\r\n\r\n\r\n\r\n
En la interfaz de usuario, el filtro de nube se encuentra en el men\u00fa superior del m\u00f3dulo "Monitoreo satelital" (ver capturas de pantalla adjuntas).<\/p>\r\n<\/div>\r\n\r\n\r\n\r\n
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Tenga en cuenta que las empresas de agricultura digital a\u00fan pueden tener dificultades con la detecci\u00f3n precisa de nubes y sombras. En la captura de pantalla adjunta, una de estas empresas crea un mapa de fertilizaci\u00f3n en realidad virtual basado en datos de nubes y sombras. Por lo tanto, un mapa incorrecto conlleva decisiones agron\u00f3micas err\u00f3neas y, en \u00faltima instancia, resultados desfavorables.<\/p>\r\n\r\n\r\n\r\n
GeoPard detecta nubes y sombras con gran precisi\u00f3n y no basa la decisi\u00f3n sobre la tasa variable en la presencia de nubes. Siempre puede visualizar las im\u00e1genes originales desde diferentes \u00e1ngulos e \u00edndices y alejar la imagen para ver el entorno.<\/p>\r\n<\/div>\r\n\r\n\r\n\r\n
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No todas las soluciones son iguales, \u00a1elige las mejores opciones disponibles en el mercado!<\/p>\r\n\r\n
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\u00bfQu\u00e9 es la detecci\u00f3n de nubes en la agricultura de precisi\u00f3n?<\/h2>\r\n
La detecci\u00f3n de nubes en la agricultura de precisi\u00f3n se refiere al proceso de identificar y monitorear la nubosidad en \u00e1reas agr\u00edcolas mediante tecnolog\u00edas de teledetecci\u00f3n. Las nubes pueden afectar significativamente las pr\u00e1cticas agr\u00edcolas y la productividad de los cultivos, por lo que su detecci\u00f3n y an\u00e1lisis son cruciales para una gesti\u00f3n agr\u00edcola eficaz.<\/p>\r\n
Este m\u00e9todo utiliza diversos sensores, como sat\u00e9lites, drones o instrumentos terrestres, para capturar im\u00e1genes de campos agr\u00edcolas. Estos sensores registran datos en forma de luz visible e infrarroja, que pueden analizarse para determinar la presencia y las caracter\u00edsticas de las nubes.<\/p>\r\n
Su principal objetivo en la agricultura de precisi\u00f3n es evaluar la distribuci\u00f3n espacial y temporal de las nubes sobre las zonas agr\u00edcolas.<\/p>\r\n
Esta informaci\u00f3n ayuda a los agricultores y agr\u00f3nomos a tomar decisiones fundamentadas sobre riego, fertilizaci\u00f3n, control de plagas y otras pr\u00e1cticas agr\u00edcolas. Al comprender los patrones de nubosidad, los agricultores pueden optimizar la asignaci\u00f3n de recursos y minimizar el riesgo de da\u00f1os en los cultivos o reducci\u00f3n del rendimiento.<\/p>\r\n
Los algoritmos de detecci\u00f3n de nubes analizan las im\u00e1genes capturadas para diferenciar entre p\u00edxeles que corresponden a nubes y p\u00edxeles que no corresponden a nubes.<\/p>\r\n
Estos algoritmos utilizan diversas t\u00e9cnicas, como la umbralizaci\u00f3n, el an\u00e1lisis espectral y el aprendizaje autom\u00e1tico, para identificar y clasificar con precisi\u00f3n las formaciones nubosas.<\/p>\r\n
El resultado de los algoritmos suele incluir el porcentaje de cobertura nubosa, la clasificaci\u00f3n del tipo de nube y los patrones de movimiento de las nubes.<\/p>\r\n
Una vez detectadas las nubes y determinadas sus caracter\u00edsticas, los agricultores pueden utilizar esta informaci\u00f3n para implementar t\u00e9cnicas de agricultura de precisi\u00f3n.<\/p>\r\n
Por ejemplo, si se detecta una nube sobre una zona espec\u00edfica de un campo, los agricultores pueden retrasar el riego o la aplicaci\u00f3n de pesticidas en esa regi\u00f3n hasta que la nube pase. Al sincronizar las actividades agr\u00edcolas con el movimiento de las nubes, los agricultores pueden optimizar el uso de los recursos y minimizar los insumos innecesarios.<\/p>\r\n
Resulta especialmente beneficioso en regiones con alta nubosidad o donde los patrones clim\u00e1ticos cambian r\u00e1pidamente. Al monitorear continuamente la nubosidad, los agricultores pueden adaptar sus pr\u00e1cticas agr\u00edcolas en tiempo real, realizando ajustes seg\u00fan las condiciones clim\u00e1ticas actuales.<\/p>\r\n
En resumen, la detecci\u00f3n de nubes en la agricultura de precisi\u00f3n desempe\u00f1a un papel crucial en la optimizaci\u00f3n de las pr\u00e1cticas agr\u00edcolas y la gesti\u00f3n de recursos. Al identificar y monitorear con precisi\u00f3n la nubosidad, los agricultores pueden tomar decisiones informadas para mejorar la productividad de los cultivos, reducir el desperdicio de insumos y mitigar los riesgos asociados con las condiciones clim\u00e1ticas cambiantes.<\/p>","protected":false},"excerpt":{"rendered":"
La detecci\u00f3n de nubes y sombras es una de las tareas m\u00e1s importantes en las soluciones anal\u00edticas de teledetecci\u00f3n. Si toda la imagen satelital es 90% limpia, entonces\u2026<\/p>","protected":false},"author":210157960,"featured_media":1383,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","_eb_attr":"","content-type":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_wpcom_ai_launchpad_first_post":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"{title}\n\n{excerpt}\n\n{url}","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"_wpas_customize_per_network":false,"jetpack_post_was_ever_published":false},"categories":[1378],"tags":[1599],"class_list":["post-206","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-remote-sensing","tag-remote-sensing"],"acf":[],"yoast_head":"\n
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