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Vision-Based Cranberry Crop Ripening Assessment
Agricultural domains are being transformed by recent advances in AI and computer vision that support quantitative visual evaluation.
AI on the Bog: Monitoring and Evaluating Cranberry Crop Risk
Toward this goal, we propose two main deep learning-based modules for: 1) cranberry fruit segmentation to delineate the exact fruit regions in the cranberry field image that are exposed to sun, 2) prediction of cloud coverage conditions and sun irradiance to estimate the inner temperature of exposed cranberries.
Finding Berries: Segmentation and Counting of Cranberries using Point Supervision and Shape Priors
Precision agriculture has become a key factor for increasing crop yields by providing essential information to decision makers.
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