Dear Colleagues,

The use of red, green, and blue (RGB) color imaging can provide a wide range of crop monitoring and plant phenotyping applications, with their utility in application depending basically on the reliability of the algorithms implemented. This is one of the reasons why a low-cost sensor, such as a color digital (RGB) camera, is being included more and more as a key component in the most advanced platforms, whether under controlled or field conditions. In the latter case, RGB cameras can be installed in portable platforms on the ground, from a mobile phone mounted on a pole to sophisticated multisensor platforms, or used aerially (e.g., mounted on unmanned or manned aerial platforms, from drones to aircraft to satellites).

Besides the formulation of vegetation indices, RGB images are amenable for assessing a wide range of other traits, even in the field, such as counting agronomical yield components, assessing phenological stages, conducting regular monitoring of crop development, measuring the growth of individual plants, and identifying foliar symptoms associated to a myriad of biotic and abiotic stresses. This is due to the wide versatility of the data collected by RGB cameras, which is essentially linked to the high resolution of these images and the general high quality of factory color calibration. Other than the appropriate software (to run the specific algorithms), and the use of advanced statistical and modeling approaches, including deep learning, machine learning, and artificial intelligence, which are all developing quickly, the other main limitation is the need for high-performance computing capable of applying results in real-time. Solving that point may pave the way for a wide number of applications to be implemented based on the interpretation and use of RGB images alone.

Prof. Dr. José Luis Araus Ortega
Prof. Dr. Jose A. Fernandez-Gallego
Prof. Dr. Isabel Roldán-Ruiz
Dr. Peter Lootens
Prof. Dr. Shawn C. Kefauver
Guest Editors

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• Color digital photography
• RGB (red, green, blue)
• Crop monitoring
• Plant phenotyping
• Functional traits
• Yield prediction
• Machine learning
• Deep learning
• Artificial intelligence
• Smartphones
• Drones
• Airborne science
• Satellite
• Proximal sensing
• Remote sensing
• Greenhouse
• Field research
• Phenology
• Biotic stress
• Abiotic stress
• Real-time