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Machine Learning in Agriculture
This special issue belongs to the section “Physical Sensors“.
Special Issue 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
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