Visual Detection of Productive Crop and Pasture Fields from Aerial Image Analysis

Abstract

The use of unmanned aerial vehicles (UAV) is expanding rapidly throughout the world. Nowadays, it is common to find some useful applications that use them in both urban and rural environments. Especially in the second case, the UAV is promoting significant changes in traditional agricultural activities. Thus, current technologies have been incorporated into inspection, surveillance, and agricultural management. In this study, we investigated some practical uses of aerial images in rural areas. A new method for allowing a UAV to understand and interpret visual information from static imagery is presented. Tree detection and shadow segmentation are essential requirements for navigation and visual examination purposes. Therefore, our method deals with these tasks to be a starting point to enable a machine to perform visual inspections in production fields. The proposed method uses computer vision techniques such as visual color enhancement, morphological operations, and segmentation approaches. We performed an evaluation of our system based on a dataset with different types of crop areas and pasture lands. Moreover, we assessed our approach to verify tree canopy and shadow detection. We also verified delineating agricultural fields, and segmentation of sunlight exposed vegetation, as well as vegetation areas covered by shadows. Results demonstrate that our approach provides an exciting and robust approach to be adopted in the field.