Implications of Very Deep Super-Resolution (VDSR) on RGB imagery for grain yield assessment in wheat

4 Pág.RGB imagery has been widely used for crop management practices and phenotyping applications in recent years. Although RGB wavelengths (400-700 nm) are not able to capture all essential plant data (such as with full ultraviolet, near and long infrared wavelength coverage), RGB cameras are the most common types of cameras and are among the versatile imaging devices for proximal remote sensing applications. Deep learning strategies have improved a wide range of processes and deep learning concepts can be included in many applications. This work uses the Very Deep Super-Resolution (VDSP) technique to improve low-resolution RGB images in order to study grain yield assessment in wheat using vegetation indexes. The results show no significant differences between indexes calculated from low-resolution images and low-resolution images processed using VDSP with grain yield.This work was supported by the Ministerio de Ciencia e Innovación, Spain [project number PID2019-106650RB-C21] as the primary funding support for the research project; and the project "Formación de Talento Humano de Alto Nivel" [project number BPIN 2013000100103] approved by the "Fondo de Ciencia, Tecnología e Innovación", from the "Sistema General de Regalías", "Gobernación del Tolima - Universidad del Tolima Tolima, Colombia" for the first author JAF. JLA acknowledges the support from ICREA Academia, Generalitat de Catalunya, Spain.Peer reviewe