|Table of Contents|

Weed Identification Technology of Greenhouse Vegetable Crops inGreenhouse Based on Improved Artificial Neural Network(PDF)

《北方园艺》[ISSN:1001-0009/CN:23-1247/S]

Issue:
2017年22
Page:
79-82
Research Field:
Publishing date:

Info

Title:
Weed Identification Technology of Greenhouse Vegetable Crops inGreenhouse Based on Improved Artificial Neural Network
Author(s):
DONG Liang; LEI Liangyu; LI Xueyuan; LIU Bing; ZHANG Hui
School of Engineering，Zhejiang A & F University，Lin′an,Zhejiang 311300
Keywords:
neural network; improvement; greenhouse; weed identification
PACS:
-
DOI:
10.11937/bfyy.20163690
Abstract:
The greenhouse has been widely used in the cultivation of vegetables，in the production of high efficiency,weed control problems need to be solved urgently.The design was used an improved artificial neural network algorithm to deal with the weed identification in the seedling stage，and the optimization of the parameters of the genetic algorithm in order to reduce the number of errors.The results showed that with the radial basis kernel function of support vector machine algorithm，improved artificial neural network algorithm to identify the correct rate was more high，more than 94% and high efficiency of identification could provide technical support for further weeding robot development.

References:

[1]张琨，王新宇，王志强.田间杂草的危害及防治技术[J].现代农业，2011(9)：40-41.

[2]王宏艳，吕继兴.基于纹理特征与改进SVM算法的玉米田间杂草识别[J].湖北农业科学，2014，53(13)：3163-3166.
[3]戴香粮.图像处理技术在水稻杂草识别中的应用[J].湘潭师范学院学报(自然科学版)，2008，30(2)：53-54.
[4]李欣，张晋国，张孟杰，等.麦田杂草的图像识别技术的研究[J].农机化研究，2007(5)：64-68.
[5]侯晨伟，陈丽.基于概率神经网络的玉米苗期杂草识别方法的研究[J].农机化研究，2010，32(11)：41-43.
[6]蔡云骧，田振锡，毕道鵾.背景典型斑点特征的聚类分析[J].光电技术应用，2012，27(3)：69-73.
[7]王献锋，张善文，王震，等.基于叶片图像和环境信息的黄瓜病害识别方法[J].农业工程学报，2014，30(14)：148-153.
[8]李晓阳.基于改进贝叶斯正则化BP神经网络模型的网络安全态势预测方法研究[J].无线互联科技，2014(3)：9，28.
[9]孔康，汪群山，梁万路.L1正则化机器学习问题求解分析[J].计算机工程，2011，37(17)：175-177.
[10]吴卫邦，朱烨雷，陶卿.一种非光滑损失坐标下降算法[J].计算机应用研究，2012，29(10)：3688-3692.

Memo

Memo:
-
Last Update: 2017-11-22