Semantic Segmentation Approaches in Crop Row Detection

Abstract

Crop row detection is a vital task in precision agriculture. Earlier works for solving this task follow traditional computer vision based methodologies. However, in recent years deep learning based approaches are being adopted for solving this task. Among various deep learning methodologies, semantic segmentation has found to be most successful for obtaining meaningful representation of images in a plethora of domains, such as, medical image analysis and autonomous driving. Scene parsing is a subcategory of semantic segmentation where all objects of interest in a scene are color coded as a way to simultaneously classify and localize their presence. In this way, scene parsing technique is a very good fit for solving crop row detection task; However, no existing research has yet ventured this direction. In this work we investigate the performance of five latest semantic segmentation methodologies on real-life crop row datasets for solving the crop row detection task. Our experimental results validate that most of the semantic segmentation methods provide substantially good results for solving the crop row detection task; Importantly, LinkNet architecture provides the best results among the competitors. We also discuss various reallife challenges for solving crop row detection in real-life scenarios.