Real-time apple detection in orchards is one of the most effective ways of
estimating apple yields, which helps in managing apple supplies more
effectively. Traditional detection methods used highly computational machine
learning algorithms with intensive hardware set up, which are not suitable for
infield real-time apple detection due to their weight and power constraints. In
this study, a real-time embedded solution inspired from "Edge AI" is proposed
for apple detection with the implementation of YOLOv3-tiny algorithm on various
embedded platforms such as Raspberry Pi 3 B+ in combination with Intel Movidius
Neural Computing Stick (NCS), Nvidia's Jetson Nano and Jetson AGX Xavier. Data
set for training were compiled using acquired images during field survey of
apple orchard situated in the north region of Italy, and images used for
testing were taken from widely used google data set by filtering out the images
containing apples in different scenes to ensure the robustness of the
algorithm. The proposed study adapts YOLOv3-tiny architecture to detect small
objects. It shows the feasibility of deployment of the customized model on
cheap and power-efficient embedded hardware without compromising mean average
detection accuracy (83.64%) and achieved frame rate up to 30 fps even for the
difficult scenarios such as overlapping apples, complex background, less
exposure of apple due to leaves and branches. Furthermore, the proposed
embedded solution can be deployed on the unmanned ground vehicles to detect,
count, and measure the size of the apples in real-time to help the farmers and
agronomists in their decision making and management skills.

本文提出了一种基于嵌入式解决方案和 YOLOv3-tiny 算法的实时苹果检测方法，以达到较高的检测准确率和帧速，同时将算法部署在便宜而功耗低的嵌入式硬件上，可用于农业智能化领域中的无人车苹果检测。