As part of the development of a sweet-pepper harvesting robot, obstacles should be detected. Objectives were to classify sweet-pepper vegetation into five plant parts: stem, top of a leaf (TL), bottom of a leaf (BL), fruit and petiole (Pet); and to improve classification results by post-processing. A multi-spectral imaging set-up with artificial lighting was developed to acquire images of sweet-pepper plants. The background was segmented from the vegetation and vegetation was classified into five plant parts, through a sequence of four two-class classification problems. True-positive detection rate/scaled false-positive rate achieved, on a pixel basis, were 40.0/179% for stem, 78.7/59.2% for top of a leaf (TL), 68.5/54.8% for bottom of a leaf (BL), 54.5/17.2% for fruit and 49.5/176.0% for petiole (Pet), before post-processing. The opening operations applied were unable to remove false stem detections to an acceptable rate. Also, many false detections of TL (>10%), BL (14%) and Pet (>15%) remained after post-processing, but these false detections are not critical for the application because these three plant parts are soft obstacles. Furthermore, results indicate that TL and BL can be distinghuished. Green fruits were post-processed using a sequence of fill-up, opening and area-based segmentation. Several area-based thresholds were tested and the most effective threshold resulted in a true-positive detection rate, on a blob basis, of 56.7 % and a scaled false-positive detection rate of 6.7 % for green fruits (N=60). Such fruit detection rates are a reasonable starting point to detect obstacles for sweet-pepper harvesting. But, additional work is required to complement the obstacle map into a complete representation of the environment.
