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Machine Learning in Agriculture

This topical collection belongs to the section “Physical Sensors“.

Topical Collection Information

Dear Colleagues,

With big data innovations and high-performance computing, machine learning has emerged to create new possibilities for data-intensive research in Agriculture and specially in the field of multi-disciplinary agro-technology. Applications relate to crop management, including yield prediction applications, remote and proximal sensing, crop phenotyping, disease detection, crop quality, weed detection, and species recognition; A significant field is the management of livestock, including animal protection and livestock production applications; water management; and soil management. Machine learning systems are benefiting from agriculture. Farm management systems are evolving into real-time artificial intelligence powered programs by applying machine learning to sensor data, providing rich suggestions and insights for far-reaching impact.

Prof. Dr. Dimitrios Moshou
Guest Editor

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Keywords

  • Artificial intelligence
  • deep learning
  • neural networks
  • generative adversarial networks
  • computer vision
  • sensor fusion
  • multisensor systems
  • sensor networks
  • crop management
  • water management
  • soil management
  • livestock management
  • precision agriculture
  • remote sensing, proximal sensing
  • crop phenotyping
  • crop pest detection
  • crop disease detection
  • robot sensors

Published Papers

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Sensors - ISSN 1424-8220