Unsupervised Segmentation of Agricultural Crops in UAV RGB Images

Crop inventory is a precision agricultural task that allows for the planning and estimation of yields per hectare cultivated. The use of Unmanned Aerial Vehicles (UAV) has gained a great boom in the development of these applications given its low cost and fast possibilities of obtaining quality images. This paper presents a method for unsupervised segmentation of agricultural UAV RGB color images. We propose the combination of a set of texture features under a segmentation framework, based on the active contour without edges model with level set representation and a connected component filtering strategy. The experiments show that it can be applied for the segmentation of agricultural crops, with an average segmentation quality of 90%. It exceeds in efficacy other methods of supervised segmentation of the state of the art. It was demonstrated the robustness of the approach for images taken with UAVs of low performance which makes cheaper its application with low costs by agricultural producers.