Weed Identification in Sugarcane Plantation Through Images Taken from Remotely Piloted Aircraft (RPA) and kNN Classifier

The sugarcane is one of the most important crops in Brazil, the world′s largest sugar producer and the second largest ethanol producer. The presence of weeds in the sugarcane plantation can cause losses up to 90% of the production, caused by the competition for light, water and nutrients, between the crop and the weeds. Usually sugarcane plantations occupy large fields, and due to this, the weeds control is mostly chemical, which is more practical and cheaper than mechanical control. In the chemical control, the dosage and type of herbicides has been calculated by sampling, which causes problems of waste and misapplication of herbicides, since the degree of infestation may be variant from one location to another, as well as the species presents in the plantation. In order to avoid unnecessary waste in the herbicides application, there are some studies about weed identification using images taken from satellites, solution that have proved to have the advantage of covering the whole plantation, solving the problems of sample surveying, nevertheless, this method its dependent of a high weed density to ensure a good pattern recognition and its affected by the influence of clouds in the imagery quality. This work proposes a system for weed identification based on pattern recognition in imagery taken from a Remotely Piloted Aircraft (RPA). The RPA is able to fly at low altitude, so it is possible to take images closer to the plants and make the weed identification even in low infestation levels. In an initial evaluation, the system reached an overall accuracy of 83.1% and kappa coefficient of 0.775, using k-Nearest Neighbors (kNN) classifier.