基于YOLOv4-tiny的设施番茄智能喷药无人车设计与试验

doi:10.12398/j.issn.2096-7217.2023.02.005

摘要/Abstract

摘要： 喷药无人车被应用于设施农业的生产中，但设施番茄具有种植间距窄、引导线缠绕多等特点，因此需设计一种小型化且能变量喷药的设施番茄智能喷药无人车。该智能喷药无人车主要由病害检测定位模块、升降平台、舵机摇臂机构和全自动承载底盘等结构组成。本设计创新性地结合深度学习技术实现病害目标的自动检测。将由Kinect V2采集的设施番茄RGB图像作为输入进行病害目标检测。将病害检测结果的二维像素坐标转换为三维空间坐标，从而实现病害定位。采用滚珠丝杠与步进电机调节升降平台的高度，并通过舵机摇臂机构完成对设施番茄病害的定位，进而完成变量智能喷药。通过对训练的YOLOv4-tiny模型进行测试，结果表明其在复杂环境下对果实簇和病害的目标检测准确率达到了75.15%，综合评价值为79.96。该模型每秒可对37.6张图像进行检测，因此能够将其部署于嵌入式开发板上进行设施番茄病害目标检测。与手动定位病害的结果相对比，结果表明其对病害的定位的绝对误差在±3.5cm以内。通过对该智能喷药无人车实地试验，设施番茄智能喷药无人车整机工作成功率在75%以上，农药错误喷洒率在20%以下，可实现对设施番茄病害的精准定位及根据病害程度实现变量智能喷药。本设计可为其他喷药智能农业装备的设计提供参考借鉴，具有较好的推广应用前景。

关键词: 设施农业, 智能喷药, 设施番茄, 无人车, 目标检测

Abstract: Unmanned vehicle for spraying is used in production of facility agriculture and smart agriculture. Tomato in greenhouse has characteristics of narrow planting spacing and many twisted lead wires, which required special vehicles. Therefore, it is necessary to design a miniaturized, variable, and intelligent spraying unmanned vehicle for greenhouse tomato. It is mainly composed of a disease detection and positioning module, a lifting platform, a steering gear rocker mechanism, and a fully automatic bearing chassis. This design innovatively combines deep learning technology to achieve automatic detection of disease targets. RGB image of greenhouse tomato collected by Kinect V2 was used as input for disease detection. Two-dimensional pixel coordinates of the disease detection results were converted into three-dimensional spatial coordinates for realizing disease location. Ball screw and stepping motor were used to adjust height of the lifting platform. Then, the steering gear rocker mechanism was employed to locate disease of the greenhouse tomato. Variable and intelligent spraying was thus completed. Results showed that target detection accuracy of fruit clusters and diseases in complex environment reached 75.15% with F1 score of 79.96 through testing the trained YOLOv4-tiny model. It detected 37.6 images per second, so it was able to be deployed on the embedded development board to detect disease targets of greenhouse tomato. Compared with the results of manual disease location, results showed that absolute error of disease location was within ±3.5cm. Through field test of the spraying unmanned vehicle, the success rate of the whole machine of the intelligent spraying unmanned vehicle for greenhouse tomato was above 75%, and the error rate of spraying pesticide was below 20%, which achieved accurate positioning of diseases of greenhouse tomato and variable intelligent spraying according to disease degree. This design can provide reference for the design of other spraying unmanned vehicle and has a good promotion and application prospect.

Key words: facility agriculture, intelligent spraying, greenhouse tomato, unmanned vehicle, object detection

中图分类号:

S223.2

李搴曦, 孙晓明, 江晗慧, 吴爱茹, 傅隆生, 李瑞. 基于YOLOv4-tiny的设施番茄智能喷药无人车设计与试验[J]. 智能化农业装备学报（中英文）, 2023, 4(2): 44-52.

LI Qianxi, SUN Xiaoming, JIANG Hanhui, WU Airu, FU Longsheng, LI Rui. Design and test of intelligent spraying unmanned vehicle for greenhouse tomato based on YOLOv4-tiny[J]. Journal of Intelligent Agricultural Mechanization, 2023, 4(2): 44-52.

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