We present an AI pipeline that involves using smart drones equipped with computer vision to obtain a more accurate fruit count and yield estimation of the number of blueberries in a field. The core components are two object-detection models based on the YOLO deep learning architecture: a Bush Model that is able to detect blueberry bushes from images captured at low altitudes and at different angles, and a Berry Model that can detect individual berries that are visible on a bush. Together, both models allow for more accurate crop yield estimation by allowing intelligent control of the drone's position and camera to safely capture side-view images of bushes up close. In addition to providing experimental results for our models, which show good accuracy in terms of precision and recall when captured images are cropped around the foreground center bush, we also describe how to deploy our models to map out blueberry fields using different sampling strategies, and discuss the challenges of annotating very small objects (blueberries) and difficulties in evaluating the effectiveness of our models.
@article{arxiv.2501.02344,
title = {Accurate Crop Yield Estimation of Blueberries using Deep Learning and Smart Drones},
author = {Hieu D. Nguyen and Brandon McHenry and Thanh Nguyen and Harper Zappone and Anthony Thompson and Chau Tran and Anthony Segrest and Luke Tonon},
journal= {arXiv preprint arXiv:2501.02344},
year = {2025}
}