Aerial Images were used to Detect Curved-Crop Rows and Failures in Sugarcane Production

Sugarcane production is in increasing demand due to the interest in the sugar and alcohol industry, bioethanol and biomass production, as well as other manufacturing sectors. In particular, the constant scientific and technological advances have optimized agricultural activities and maximized the productivity of sugarcane crops. In this sense, digital image processing, computer vision techniques, and machine learning algorithms have supported automated processes that were previously performed manually and at a high cost. In this study, we present a novel method to detect crop rows and measure gaps in crop fields. Our method is also robust to deal with curved crop rows, which is a real problem and substantially limits numerous solutions in practical applications. The proposed method is evaluated using a database of real scene images that was prepared with the support of a small unmanned aerial vehicle (UAV). Experimental tests showed a low relative error of approximately 1.65% compared to manual mapping in the planting regions, even for regions with failures in the curved crop rows. It means that our proposal can identify and measure crop rows accurately, which enables automated inspections with high precision measurements.