Jiang Ping, Qi Aolin, Zhong Jiao, Luo Yahui, Hu Wenwu, Shi Yixin, Liu Tianyu
College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha, 410128, China.
Plant Methods. 2024 Jun 20;20(1):96. doi: 10.1186/s13007-024-01226-y.
Pesticide efficacy directly affects crop yield and quality, making targeted spraying a more environmentally friendly and effective method of pesticide application. Common targeted cabbage spraying methods often involve object detection networks. However, complex natural and lighting conditions pose challenges in the accurate detection and positioning of cabbage.
In this study, a cabbage detection algorithm based on the YOLOv8n neural network (YOLOv8-cabbage) combined with a positioning system constructed using a Realsense depth camera is proposed. Initially, four of the currently available high-performance object detection models were compared, and YOLOv8n was selected as the transfer learning model for field cabbage detection. Data augmentation and expansion methods were applied to extensively train the model, a large kernel convolution method was proposed to improve the bottleneck section, the Swin transformer module was combined with the convolutional neural network (CNN) to expand the perceptual field of feature extraction and improve edge detection effectiveness, and a nonlocal attention mechanism was added to enhance feature extraction. Ablation experiments were conducted on the same dataset under the same experimental conditions, and the improved model increased the mean average precision (mAP) from 88.8% to 93.9%. Subsequently, depth maps and colour maps were aligned pixelwise to obtain the three-dimensional coordinates of the cabbages via coordinate system conversion. The positioning error of the three-dimensional coordinate cabbage identification and positioning system was (11.2 mm, 10.225 mm, 25.3 mm), which meets the usage requirements.
We have achieved accurate cabbage positioning. The object detection system proposed here can detect cabbage in real time in complex field environments, providing technical support for targeted spraying applications and positioning.
农药药效直接影响作物产量和质量，使精准喷雾成为一种更环保、更有效的农药施用方法。常见的甘蓝精准喷雾方法通常涉及目标检测网络。然而，复杂的自然和光照条件给甘蓝的准确检测和定位带来了挑战。
本研究提出了一种基于YOLOv8n神经网络的甘蓝检测算法（YOLOv8-甘蓝），并结合使用RealSense深度相机构建的定位系统。首先，对当前可用的四种高性能目标检测模型进行了比较，选择YOLOv8n作为田间甘蓝检测的迁移学习模型。应用数据增强和扩充方法对模型进行广泛训练，提出大内核卷积方法改进瓶颈部分，将Swin变压器模块与卷积神经网络（CNN）相结合以扩大特征提取的感知域并提高边缘检测效果，并添加非局部注意力机制以增强特征提取。在相同实验条件下对同一数据集进行消融实验，改进后的模型将平均精度均值（mAP）从88.8%提高到93.9%。随后，将深度图和彩色图逐像素对齐，通过坐标系转换获得甘蓝的三维坐标。三维坐标甘蓝识别与定位系统的定位误差为（11.2毫米，10.225毫米，25.3毫米），满足使用要求。
我们实现了甘蓝的精确定位。本文提出的目标检测系统能够在复杂的田间环境中实时检测甘蓝，为精准喷雾应用和定位提供技术支持。
