Huang Heqing, Huang Tongbin, Li Zhen, Lyu Shilei, Hong Tao
College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China.
National Research Laboratory of Mechanization of Citrus Industry Technical System, Guangzhou 510642, China.
Sensors (Basel). 2021 Dec 23;22(1):59. doi: 10.3390/s22010059.
Citrus fruit detection can provide technical support for fine management and yield determination of citrus orchards. Accurate detection of citrus fruits in mountain orchards is challenging because of leaf occlusion and citrus fruit mutual occlusion of different fruits. This paper presents a citrus detection task that combines UAV data collection, AI embedded device, and target detection algorithm. The system used a small unmanned aerial vehicle equipped with a camera to take full-scale pictures of citrus trees; at the same time, we extended the state-of-the-art model target detection algorithm, added the attention mechanism and adaptive fusion feature method, improved the model's performance; to facilitate the deployment of the model, we used the pruning method to reduce the amount of model calculation and parameters. The improved target detection algorithm is ported to the edge computing end to detect the data collected by the unmanned aerial vehicle. The experiment was performed on the self-made citrus dataset, the detection accuracy was 93.32%, and the processing speed at the edge computing device was 180 ms/frame. This method is suitable for citrus detection tasks in the mountainous orchard environment, and it can help fruit growers to estimate their yield.
柑橘果实检测可为柑橘果园的精细管理和产量测定提供技术支持。由于山地果园中叶片遮挡以及不同果实间的相互遮挡，准确检测柑橘果实具有挑战性。本文提出了一种结合无人机数据采集、人工智能嵌入式设备和目标检测算法的柑橘检测任务。该系统使用配备摄像头的小型无人机对柑橘树进行全景拍照；同时，我们扩展了先进的模型目标检测算法，添加了注意力机制和自适应融合特征方法，提高了模型性能；为便于模型部署，我们使用剪枝方法减少模型计算量和参数。将改进后的目标检测算法移植到边缘计算端，以检测无人机采集的数据。在自制的柑橘数据集上进行实验，检测准确率为93.32%，边缘计算设备的处理速度为180毫秒/帧。该方法适用于山地果园环境中的柑橘检测任务，可帮助果农估算产量。
