Towards agricultural autonomy: crop row detection under varying field conditions using deep learning

T owards agricultural autonomy: crop row detection under varying field conditions using deep learning Rajitha de Silva 1, Grzegorz Cielniak 2 and Junfeng Gao 3 Abstract -- This paper presents a novel metric to evaluate the robustness of deep learning based semantic segmentation approaches for crop row detection under different field conditions encountered by a field robot. A dataset with ten main categories encountered under various field conditions was used for testing. The effect on these conditions on the angular accuracy of crop row detection was compared. A deep convolutional encoder decoder network is implemented to predict crop row masks using RGB input images. The predicted mask is then sent to a post processing algorithm to extract the crop rows. The deep learning model was found to be robust against shadows and growth stages of the crop while the performance was reduced under direct sunlight, increasing weed density, tramlines and discontinuities in crop rows when evaluated with the novel metric. I NTRODUCTION Computer vision algorithms has been identified as one of the key areas that need to be improved to promote the current agricultural systems[1]. Crop row detection is a key element in developing vision based navigation robots in agricultural robotics. Vision based crop row detection has been a popular research question in classical computer vision.