|Table of Contents|

Research on Field Weed Intelligent Detection System Based on Cloud Service

《北方园艺》[ISSN:1001-0009/CN:23-1247/S]

Issue:
2020年16
Page:
144-150
Research Field:
Publishing date:

Info

Title:
Research on Field Weed Intelligent Detection System Based on Cloud Service
Author(s):
WANG Jian1; 2; GUO Junxian1; MA Shengjian2
(1.College of Mechanical and Electronic Engineering,Xinjiang Agricultural University,Urumqi,Xinjiang 830052；2.Life Science and Biotechnology College,Lingnan Normal University,Zhanjiang,Guangdong 524048)
Keywords:
weed detection; disease identification; NASNet-mobile; deep learning; fine-grained classification
PACS:
-
DOI:
10.11937/bfyy.20194764
Abstract:
In order to solve the problem of the poor accuracy of weed recognition by traditional image processing in the complex environment of the field,this study collected 8 kinds of common weeds.The data set was composed of 17 509 labeled images.Weeds were identified and the trained model is fine-tuned to further improve the accuracy of the recognition.By comparing the four models of VGG,Inception,ResNeXt and NASNet,the NASNet-mobile model with small model parameters and high accuracy was selected and deployed to the cloud service.The cloud server used Gin to build model interactions for identifying weeds and returning identification information.It used CSS and Javascript language and components encapsulated by Element to develop front-end services for data collection,upload,and information feedback.The performance of NASNet-mobile model in the deployed server was 285 ms per image,the accuracy of 8 weeds was 91.43%,and the recognition rate of flat axis wood and plane grass was 98%,which could provide technical support for weed information detection and investigation in the field.

References:

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Last Update: 2020-11-24