用无人机+深度学习精准数蓝莓，提升产量预估准确率
Accurate Crop Yield Estimation of Blueberries using Deep Learning and Smart Drones
- 双模型协同：识别灌木与单个蓝莓，支持无人机智能拍摄
- 在中心区域图像上，检测精度与召回率表现良好
- 适合农业监测、智能农场及小目标检测研究者参考
本文提出一种基于智能无人机与计算机视觉的AI流程，用于更准确地估算蓝莓田中的果实数量和产量。核心由两个基于YOLO架构的物体检测模型构成：一是能够从低空多角度图像中识别蓝莓灌木的灌木模型；二是可检测灌木上可见单个蓝莓的蓝莓模型。两者协同工作，实现对灌木侧视图的近距离安全拍摄，从而提升检测效果。实验表明，在以前景中心灌木为裁剪区域的图像中，模型在精确率与召回率方面均表现良好。此外，文章还介绍了如何通过不同采样策略部署模型进行蓝莓田测绘，并讨论了极小目标（蓝莓）标注困难及模型评估挑战。
原文摘要 · Abstract (English)
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.
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