Autonomous Mapping of Grass-Clover Ratio Based on Unmanned Aerial Vehicles and Convolutional Neural Networks
This paper presents a method which can provide support in determining the grass-clover ratio, in grass-clover fields, based on images from an unmanned aerial vehicle. Automated estimation of the grass-clover ratio can serve as a tool for optimizing fertilization of grass-clover fields. A higher clover content gives a higher performance of the cows, when the harvested material is used for fodder, and thereby this has a direct impact on the dairy industry. An android application is implemented to make the drone fly fully autonomously and collect images at different locations within the field. In this android application it is possible to specify what location the drone should collect images from, which height, and upload the images to a server, which analyze the data based on a convolutional neural network. The convolutional neural network performs a semantic segmentation and thereby pixelwise classify the different classes: grass, clover, soil and weed. The classification of the pixels is used to determine the final grass-clover ratio. The results, presented in this paper, show that the CNN is able to segment the images into the different classes: grass, clover, soil and weed. It is possible to identify the different classes based on images captured at a height up to five meters. Thus, this paper shows a way to use UAVs to perform mapping of actual clover and grass ratio in dense grass-clover fields.