Vision-Based Cranberry Crop Ripening Assessment

TL;DR

This paper presents a novel drone-based computer vision framework for quantitatively assessing cranberry crop ripening, enabling real-time monitoring, crop breeding comparisons, and disease detection with potential applications across various crops.

Contribution

It introduces a new method combining drone imaging, photometric calibration, and semi-supervised deep learning for crop ripening analysis, which is the first of its kind.

Findings

Quantified ripening rates for four cranberry varieties.

Enabled real-time overheating risk assessment.

Facilitated large-scale crop breeding comparisons.

Abstract

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.