  Detection, segmentation and tracking of fruits and vegetables are three
fundamental tasks for precision agriculture, enabling robotic harvesting and
yield estimation applications. However, modern algorithms are data hungry and
it is not always possible to gather enough data to apply the best performing
supervised approaches. Since data collection is an expensive and cumbersome
task, the enabling technologies for using computer vision in agriculture are
often out of reach for small businesses. Following previous work in this
context, where we proposed an initial weakly supervised solution to reduce the
data needed to get state-of-the-art detection and segmentation in precision
agriculture applications, here we improve that system and explore the problem
of tracking fruits in orchards. We present the case of vineyards of table
grapes in southern Lazio (Italy) since grapes are a difficult fruit to segment
due to occlusion, color and general illumination conditions. We consider the
case in which there is some initial labelled data that could work as source
data (\eg wine grape data), but it is considerably different from the target
data (e.g. table grape data). To improve detection and segmentation on the
target data, we propose to train the segmentation algorithm with a weak
bounding box label, while for tracking we leverage 3D Structure from Motion
algorithms to generate new labels from already labelled samples. Finally, the
two systems are combined in a full semi-supervised approach. Comparisons with
state-of-the-art supervised solutions show how our methods are able to train
new models that achieve high performances with few labelled images and with
very simple labelling.
