Row Detection Using a Machine Learning Approach for Autonomous Agriculture Vehicles

Autonomous agriculture vehicles require robust vision systems to navigate farm fields within rows. Commonly, image processing methods are used for row detection in applications like forestry and crop production to collect growth information and yield data based on location. Often these methods are implemented at a bird's eye view by images obtained via drones to determine distinct patterns, but this does not offer closeup and specific information. Autonomous agriculture vehicles can offer a ground-level view of crops and are capable of relaying more nuanced data to the user. The proposed analysis focuses on determining the effectiveness of various features for a machine learning based image classification of crops at a ground-level view. Due to the similarities in the color and patterns observed in the images, classification for autonomous navigation is a difficult problem. The proposed solution is a machine learning based model constructed from color and texture features to predict the occurrence of the crop. The input image data is divided into pieces, and this prediction is performed on a segment by segment basis to construct a coordinate map of distinct areas of interest in the input image. A combination of color features produced by K-means clustering and texture features produced by Haralick textures was found to deliver 95% accuracy. Autonomous navigation is a real-time application, and computational efficiency is important. Therefore we also target to reduce processing time while achieving high accuracy for real-time navigation.