Manual harvesting of loose fruits in the oil palm plantation is both time consuming and physically laborious. Automatic harvesting system is an alternative solution for precision agriculture which requires accurate visual information of the targets. Current state-of-the-art one-stage object detection method provides excellent detection accuracy; however, it is computationally intensive and impractical for embedded system. This paper proposed an improved YOLO model to detect oil palm loose fruits from unmanned aerial vehicle images. In order to improve the robustness of the detection system, the images are augmented by brightness, rotation, and blurring to simulate the actual natural environment. The proposed improved YOLO model adopted several improvements; densely connected neural network for better feature reuse, swish activation function, multi-layer detection to enhance detection on small targets and prior box optimization to obtain accurate bounding box information. The experimental results show that the proposed model achieves outstanding average precision of 99.76% with detection time of 34.06 ms. In addition, the proposed model is also light in weight size and requires less training time which is significant in reducing the hardware costs. The results exhibit the superiority of the proposed improved YOLO model over several existing state-of-the-art detection models.

在油棕种植园中人工收获松散的水果既费时又费力。自动收割系统是精确农业的替代解决方案，它需要目标的准确视觉信息。当前最先进的一级目标检测方法可提供出色的检测精度；然而，对于嵌入式系统而言，这是计算密集型且不切实际的。本文提出了一种改进的YOLO模型，可以从无人机图像中检测出油棕松果。为了提高检测系统的鲁棒性，通过亮度，旋转和模糊来增强图像，以模拟实际的自然环境。提议的改进的YOLO模型采用了一些改进；紧密连接的神经网络，可实现更好的功能重用，swish激活功能，多层检测以增强对小目标的检测，并进行先验盒优化以获得准确的包围盒信息。实验结果表明，提出的模型具有34.06 ms的检测时间，具有99.76％的出色平均精度。另外，所提出的模型重量轻，并且需要较少的训练时间，这对于降低硬件成本具有重要意义。结果表明，提出的改进的YOLO模型优于几种现有的最先进的检测模型。提出的模型重量轻，需要的培训时间更少，这对降低硬件成本具有重要意义。结果表明，提出的改进的YOLO模型优于几种现有的最先进的检测模型。提出的模型重量轻，需要的培训时间更少，这对降低硬件成本具有重要意义。结果表明，提出的改进的YOLO模型优于几种现有的最先进的检测模型。