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Trend Technologies for Robotic Fertilization Process in Row Crops.

Christyan Cruz Ulloa1, Anne Krus2, Antonio Barrientos1

  • 1Centro de Automática y Robótica, Consejo Superior de Investigaciones Científicas, Universidad Politécnica de Madrid, Madrid, Spain.

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This summary is machine-generated.

Precision agriculture utilizes advanced sensors and robots for efficient crop management. This study highlights robotic fertilization

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multi-spectral imagespoint cloudsprecision agriculturerobotics in agriculturesmart farming

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Area of Science:

  • Agricultural Engineering
  • Robotics
  • Computer Vision

Background:

  • Growing demand for organic vegetables drives innovation in precision agriculture.
  • Advancements in sensory and robotic technologies enable targeted crop management solutions.

Purpose of the Study:

  • To analyze optical sensory systems for data acquisition in robotic fertilization.
  • To evaluate the SUREVEG project's robotic platform for precision agriculture applications.

Main Methods:

  • Developed a robotic platform with Laser, Multispectral, and RGB sensors, and a robotic arm.
  • Utilized Robot Operating System (ROS) for data processing and trajectory planning.
  • Tested the platform on cabbage and red cabbage crops for row reconstruction and fertilization.

Main Results:

  • Vegetable characterization achieved 93.1% efficiency using Point Cloud processing.
  • Vegetable detection error was 4.6% using RGB images.
  • Quantitative comparison of sensory data (NDVI, RGB Histograms, Point Cloud Clusters) analyzed.

Conclusions:

  • Optical sensory systems are effective for data acquisition in robotic fertilization.
  • The developed robotic platform demonstrates high efficiency in vegetable characterization and detection.
  • Precision agriculture technologies can significantly improve crop management and yield.