  The recent thrust on digital agriculture (DA) has renewed significant
research interest in the automated delineation of agricultural fields. Most
prior work addressing this problem have focused on detecting medium to large
fields, while there is strong evidence that around 40\% of the fields
world-wide and 70% of the fields in Asia and Africa are small. The lack of
adequate labeled images for small fields, huge variations in their color,
texture, and shape, and faint boundary lines separating them make it difficult
to develop an end-to-end learning model for detecting such fields. Hence, in
this paper, we present a multi-stage approach that uses a combination of
machine learning and image processing techniques. In the first stage, we
leverage state-of-the-art edge detection algorithms such as holistically-nested
edge detection (HED) to extract first-level contours and polygons. In the
second stage, we propose image-processing techniques to identify polygons that
are non-fields, over-segmentations, or noise and eliminate them. The next stage
tackles under-segmentations using a combination of a novel ``cut-point'' based
technique and localized second-level edge detection to obtain individual
parcels. Since a few small, non-cropped but vegetated or constructed pockets
can be interspersed in areas that are predominantly croplands, in the final
stage, we train a classifier for identifying each parcel from the previous
stage as an agricultural field or not. In an evaluation using high-resolution
imagery, we show that our approach has a high F-Score of 0.84 in areas with
large fields and reasonable accuracy with an F-Score of 0.73 in areas with
small fields, which is encouraging.
