Vision-Based Cranberry Crop Ripening Assessment
Faith Johnson Jack Lowry Kristin Dana Peter Oudemans
Agricultural domains are being transformed by recent advances in AI and
computer vision that support quantitative visual evaluation. Using drone
imaging, we develop a framework for characterizing the ripening process of
cranberry crops. Our method consists of drone-based time-series collection over
a cranberry growing season, photometric calibration for albedo recovery from
pixels, and berry segmentation with semi-supervised deep learning networks
using point-click annotations. By extracting time-series berry albedo
measurements, we evaluate four different varieties of cranberries and provide a
quantification of their ripening rates. Such quantification has practical
implications for 1) assessing real-time overheating risks for cranberry bogs;
2) large scale comparisons of progeny in crop breeding; 3) detecting disease by
looking for ripening pattern outliers. This work is the first of its kind in
quantitative evaluation of ripening using computer vision methods and has
impact beyond cranberry crops including wine grapes, olives, blueberries, and
maize.