Monitoreo preliminar de incidencia de fisiopatías en cultivos de fresa usando procesamiento digital de imágenes

Processes executed for Identifying anomalies in agricultural crops using image processing have been effective, however these methods involve tear off plant’s leaflets and fruits under study. For this paper it is shown the results of the development and validation of an algorithm that allows the execution of incidence monitoring in strawberry crops (Fragaria x ananassa), able to make a first approximation to distinguish senescence and mechanical damage in their leaflets, implementing an indirect (non-destructive) methodology. Image processing techniques used for this research include smoothing, erosion, dilation, edge detection, pattern matching, thresholding, among others. The results were visualized in an application developed in C # using the Emgu CV library. It is concluded that is possible to offer a preliminary monitoring of incidence using this algorithm, saving time for producers and researchers who require a first approximation of the state of the crop, with the ability to run on desktops, notebooks and aerial robots (drones) that allow to automate this task