  Vision-based navigation systems in arable fields are an underexplored area in
agricultural robot navigation. Vision systems deployed in arable fields face
challenges such as fluctuating weed density, varying illumination levels,
growth stages and crop row irregularities. Current solutions are often
crop-specific and aimed to address limited individual conditions such as
illumination or weed density. Moreover, the scarcity of comprehensive datasets
hinders the development of generalised machine learning systems for navigating
these fields. This paper proposes a suite of deep learning-based perception
algorithms using affordable vision sensors for vision-based navigation in
arable fields. Initially, a comprehensive dataset that captures the intricacies
of multiple crop seasons, various crop types, and a range of field variations
was compiled. Next, this study delves into the creation of robust infield
perception algorithms capable of accurately detecting crop rows under diverse
conditions such as different growth stages, weed density, and varying
illumination. Further, it investigates the integration of crop row following
with vision-based crop row switching for efficient field-scale navigation. The
proposed infield navigation system was tested in commercial arable fields
traversing a total distance of 4.5 km with average heading and cross-track
errors of 1.24{\deg} and 3.32 cm respectively.
