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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 has focused on detecting medium to large fields, while there is strong evidence that around 40% of the fields worldwide and 70% of the fields in Asia and Africa are small. The lack of adequate labelled images for small fields, huge variations in their colour, 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 …