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Understanding how an object moves along a path requires distinguishing between motion over a time span and motion at a precise moment. A useful example is a vehicle traveling along a straight and level path, where its position at any given time is known. The initial step in analyzing this motion is to measure how far the vehicle travels over a fixed time period. This measurement, called average velocity, is computed by dividing the total change in position by the duration over which the change...
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A neutral atom consists of a positively charged nucleus surrounded by a negatively charged electron cloud. When placed in an external electric field, the external electric force pulls the electrons and nucleus apart, opposite to the intrinsic attraction between the nucleus and the electrons. The opposing forces balance each other with a slight shift between the center of masses of the nucleus and the electron cloud, resulting in a polarized atom. On the other hand, a few molecules, like water,...
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Magnetic dipoles in magnetic materials are aligned when placed under an external magnetic field. For paramagnets and ferromagnets, dipole alignment occurs in the direction of the magnetic field. However, the dipoles align opposite to the field in the case of diamagnets. This state of magnetic polarization due to the external field is called magnetization. Magnetization is defined as the dipole moment per unit volume. It plays a similar role to polarization in electrostatics.
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Un conjunto de datos de imágenes de vehículos aéreos no tripulados para la detección de objetos con anotaciones

Anindita Das1, Vinitha Hannah Subburaj1, Yong Yang1

  • 1West Texas A&M University, Canyon, TX 79016, USA.

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Este conjunto de datos de imágenes de drones ayuda a la agricultura de precisión y al aprendizaje automático para la detección de cultivos y malezas. Apoya el monitoreo automatizado y la agricultura sostenible, avanzando la IA en la agricultura.

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AnotaciónVisión por computadoraConjunto de datos de imágenesLabelImgDetección de objetosTeledetecciónRoboflowUAVYOLOv7

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Área de la Ciencia:

  • Ciencias Agrícolas
  • Ciencias de la Computación
  • Ciencias Ambientales

Sus antecedentes:

  • La agricultura de precisión requiere una diferenciación precisa de cultivos y malezas para un manejo efectivo.
  • Los modelos de aprendizaje automático para la detección de malezas necesitan conjuntos de datos grandes y diversos para el entrenamiento y la validación.

Objetivo del estudio:

  • Presentar un nuevo conjunto de datos de imágenes de drones de campos de algodón.
  • Facilitar el desarrollo de modelos de detección de objetos para distinguir cultivos de malezas.
  • Establecer un punto de referencia para evaluar el rendimiento de los modelos de IA en entornos agrícolas.

Principales métodos:

  • Adquisición de imágenes de drones de alta resolución en diversas condiciones de campos de algodón.
  • Anotación de imágenes para identificar y etiquetar instancias de cultivos y malezas.
  • Curación de conjuntos de datos para garantizar la calidad y la idoneidad para tareas de aprendizaje automático.

Principales resultados:

  • Un conjunto de datos completo que permite un entrenamiento de modelos robusto para la detección de cultivos y malezas.
  • Un recurso estandarizado para el análisis comparativo de diferentes algoritmos de IA.
  • Base para mejorar los sistemas de monitoreo agrícola automatizado.

Conclusiones:

  • El conjunto de datos publicado es crucial para avanzar en la agricultura de precisión impulsada por la IA.
  • Apoya el desarrollo de prácticas agrícolas sostenibles a través de una mejor gestión de malezas.
  • Este recurso contribuye al campo más amplio de las aplicaciones de IA en la agricultura y la gestión ambiental.