Vision-Based Cranberry Crop Ripening Assessment
This work addresses the need for quantitative ripening evaluation in agriculture, specifically for cranberry crops, with potential broader impact on other crops like grapes and maize, though it is incremental as it applies existing methods to a new domain.
The paper tackled the problem of assessing cranberry crop ripening by developing a drone-based computer vision framework to quantify ripening rates from time-series berry albedo measurements, evaluating four varieties with practical applications in risk assessment, breeding, and disease detection.
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.