北京市农林科学院机构知识库

MOBILE SMART DEVICE-BASED VEGETABLE DISEASE AND INSECT PEST RECOGNITION METHOD

收藏
分享
全文链接

文献类型：外文期刊

作者： Wang, Kaiyi ¹ ; Zhang, Shuifa ¹ ; Wang, Zhibin ¹ ; Liu, Zhongqiang ¹ ; Yang, Feng ¹ ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

关键词： Computer Vision;Image Processing;Vegetable Diseases

期刊名称：INTELLIGENT AUTOMATION AND SOFT COMPUTING （影响因子：1.647；五年影响因子：1.469 ）

ISSN： 1079-8587

年卷期： 2013 年 19 卷 3 期

页码：

收录情况： SCI

摘要： Computer vision and image processing technology have been rapidly developed and widely applied in many fields. There are many potential applications in modern agriculture. In this paper, a novel vegetable disease and insect pest recognition method is proposed based on the current computer vision and image processing methods. To investigate the vegetable disease and insect pest state, it is convenient to use images captured using smart phones for judgment. To implement this application, the disease area and the insect number on the leaves should be detected and figured out. So a new extraction and classification algorithm is firstly introduced to recognize leaves from images. Then a region-labeling algorithm is applied to calculate the insect number and disease areas in the segmented images. To deal with the areas of adhesion, a mathematical morphology algorithm is used for separating the objects. The proposed method is implemented on mobile smart devices and tested with field experiments. The experimental results show that the proposed method has good recognition performance with high efficiency.

相关文献

[1]Design and Implementation of an Automatic Grading System of Diced Potatoes Based on Machine Vision. Wang, Chaopeng,Qian, Man,Fan, Shuxiang,Chen, Liping,Wang, Chaopeng,Huang, Wenqian,Zhang, Baohua,Yang, Jingjing,Qian, Man,Fan, Shuxiang,Chen, Liping,Wang, Chaopeng,Huang, Wenqian,Zhang, Baohua,Yang, Jingjing,Qian, Man,Fan, Shuxiang,Chen, Liping,Wang, Chaopeng,Huang, Wenqian,Zhang, Baohua,Yang, Jingjing,Qian, Man,Fan, Shuxiang,Chen, Liping,Wang, Chaopeng,Huang, Wenqian,Zhang, Baohua,Yang, Jingjing,Qian, Man,Fan, Shuxiang,Chen, Liping. 2016

[2]Computer vision detection of defective apples using automatic lightness correction and weighted RVM classifier. Zhang, Baohua,Gong, Liang,Zhao, Chunjiang,Liu, Chengliang,Huang, Danfeng,Zhang, Baohua,Huang, Wenqian,Li, Jiangbo,Zhao, Chunjiang.

[3]The intelligent diagnostic system of vegetable diseases based on a fuzzy neural network. Wei, Qingfeng,Luo, Changshou,Cao, Chengzhong,Guo, Qiang. 2013

[4]Design and Experiment of a Sorting System for Haploid Maize Kernel. Song, Peng,Wang, Cheng,Luo, Bin,Zhang, Han,Zhang, Jun Xiong. 2018

[5]Research on tomato seed vigor based on X-ray digital image. Zhao Xueguan,Gao Yuanyuan,Wang Xiu,Li Cuiling,Wang, Songlin,Feng Qingcun,Zhao Xueguan,Gao Yuanyuan,Wang Xiu,Li Cuiling,Wang, Songlin,Feng Qingcun,Zhao Xueguan,Gao Yuanyuan,Wang Xiu,Li Cuiling,Wang, Songlin,Feng Qingcun. 2016

[6]Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review. Zhang, Baohua,Huang, Wenqian,Li, Jiangbo,Zhao, Chunjiang,Fan, Shuxiang,Wu, Jitao,Zhang, Baohua,Zhao, Chunjiang,Liu, Chengliang. 2014

[7]Computer vision recognition of stem and calyx in apples using near-infrared linear-array structured light and 3D reconstruction. Zhang, Baohua,Gong, Liang,Zhao, Chunjiang,Liu, Chengliang,Huang, Danfeng,Huang, Wenqian,Wang, Chaopeng,Zhao, Chunjiang,Huang, Wenqian,Wang, Chaopeng,Zhao, Chunjiang,Huang, Wenqian,Wang, Chaopeng,Zhao, Chunjiang,Huang, Wenqian,Wang, Chaopeng,Zhao, Chunjiang.

[8]Survey of Support Vector Machine in the Processing of Remote Sensing Image. Li, Su,Wang, Wenchao. 2013

[9]Quick image processing method of HJ satellites applied in agriculture monitoring. Yu Haiyang,Liu Yanmei,Yang Guijun,Yang Xiaodong,Yu Haiyang,Liu Yanmei,Yang Guijun,Yang Xiaodong. 2016

[10]Detection of defects on apple using B-spline lighting correction method. Li, Jiangbo,Huang, Wenqian,Guo, Zhiming. 2013

[11]Image processing methods to evaluate tomato and zucchini damage in post-harvest stages. Antonio Alvarez-Bermejo, Jose,Giagnocavo, Cynthia,Ming, Li,Yang Xinting,Castillo Morales, Encarnacion,Morales Santos, Diego P.. 2017

[12]THE INFRARED THERMAL IMAGE-BASED MONITORING PROCESS OF PEACH DECAY UNDER UNCONTROLLED TEMPERATURE CONDITIONS. Jiao, L. Z.,Wu, W. B.,Zheng, W. G.,Dong, D. M.. 2015

[13]Detection of early bruises on peaches (Amygdalus persica L.) using hyperspectral imaging coupled with improved watershed segmentation algorithm. Li, Jiangbo,Chen, Liping,Huang, Wenqian,Li, Jiangbo,Chen, Liping,Huang, Wenqian,Li, Jiangbo,Chen, Liping,Huang, Wenqian,Li, Jiangbo,Chen, Liping,Huang, Wenqian. 2018

[14]Fast detection and visualization of early decay in citrus using Vis-NIR hyperspectral imaging. Li, Jiangbo,Huang, Wenqian,Tian, Xi,Wang, Chaopeng,Fan, Shuxiang,Zhao, Chunjiang,Li, Jiangbo,Huang, Wenqian,Tian, Xi,Wang, Chaopeng,Fan, Shuxiang,Zhao, Chunjiang,Li, Jiangbo,Huang, Wenqian,Zhao, Chunjiang.

[15]Detection of Early Rottenness on Apples by Using Hyperspectral Imaging Combined with Spectral Analysis and Image Processing. Zhang, Baohua,Fan, Shuxiang,Li, Jiangbo,Huang, Wenqian,Zhao, Chunjiang,Qian, Man,Zheng, Ling,Zhang, Baohua,Zhao, Chunjiang.

[16]Real-time Monitoring of Exhaust Fan Operation Status in a Livestock House Using Image Analysis. Luyu Ding,Yang Lv,Qifeng Li,Ligen Yu,Ronghua Gao,Weihong Ma,Qinyang Yu. 2021

作者其他论文更多>>

Prediction of maize cultivar yield based on machine learning algorithms for precise promotion and planting

作者：Han, Yanyun;Wang, Kaiyi;Yang, Feng;Pan, Shouhui;Liu, Zhongqiang;Zhang, Qiusi;Zhang, Qi;Han, Yanyun;Wang, Kaiyi;Yang, Feng;Pan, Shouhui;Liu, Zhongqiang;Zhang, Qiusi;Zhang, Qi

关键词：Prediction of maize cultivar yield; Machine learning; Random forest; Levenberg - Marquardt neural network; Multilayer perceptron neural network; Assessment of varieties
Developing a comprehensive evaluation model of variety adaptability based on machine learning method

作者：Han, Yanyun;Wang, Kaiyi;Zhang, Qi;Yang, Feng;Pan, Shouhui;Liu, Zhongqiang;Zhang, Qiusi;Han, Yanyun;Wang, Kaiyi;Zhang, Qi;Yang, Feng;Pan, Shouhui;Liu, Zhongqiang;Zhang, Qiusi

关键词：Maize variety adaptability evaluation; Variety adaptability comprehensive evaluation index; Entropy weight method; Machine learning method
TrG2P: A transfer-learning-based tool integrating multi-trait data for accurate prediction of crop yield

作者：Li, Jinlong;Zhang, Dongfeng;Yang, Feng;Zhang, Qiusi;Pan, Shouhui;Zhao, Xiangyu;Zhang, Qi;Han, Yanyun;Wang, Kaiyi;Zhao, Chunjiang;Li, Jinlong;Zhang, Dongfeng;Yang, Feng;Zhang, Qiusi;Pan, Shouhui;Zhao, Xiangyu;Zhang, Qi;Han, Yanyun;Wang, Kaiyi;Zhao, Chunjiang;Yang, Jinliang;Yang, Jinliang

关键词：crop; genotype to phenotype; transfer learning; yield prediction; multi-trait
IoT-based system of prevention and control for crop diseases and insect pests

作者：Wang, Zhibin;Qiao, Xiaojun;Wang, Ying;Yu, Hao;Wang, Zhibin;Qiao, Xiaojun;Wang, Ying;Yu, Hao;Mu, Cuixia

关键词：ozone; Internet of Things (IoT); diseases; insect pests; pesticide-free; light trap; remote control
Prediction of corn variety yield with attribute-missing data via graph neural network

作者：Yang, Feng;Zhang, Dongfeng;Han, Yanyun;Zhang, Qiusi;Zhang, Qi;Liu, Zhongqiang;Wang, Kaiyi;Zhang, Yuqing;Zhang, Yong;Han, Yanyun;Liu, Zhongqiang;Wang, Kaiyi;Zhang, Qiusi;Zhang, Qi;Zhang, Chenghui

关键词：Corn variety yield prediction; Graph neural network; Data imputation; Adversarial learning
Maize yield prediction using federated random forest

作者：Zhang, Qiusi;Wang, Kaiyi;Zhao, Chunjiang;Zhang, Qiusi;Zhao, Xiangyu;Han, Yanyun;Yang, Feng;Pan, Shouhui;Liu, Zhongqiang;Wang, Kaiyi;Zhao, Chunjiang;Zhang, Qiusi;Zhao, Xiangyu;Han, Yanyun;Yang, Feng;Pan, Shouhui;Liu, Zhongqiang;Wang, Kaiyi;Zhao, Chunjiang

关键词：Yield prediction; Joint breeding; Federated learning; Intelligent agriculture
Precise Recommendation Method of Suitable Planting Areas of Maize Varieties Based on Knowledge Graph

作者：Zou, Yidong;Zhao, Chunjiang;Zou, Yidong;Pan, Shouhui;Yang, Feng;Zhang, Dongfeng;Han, Yanyun;Zhao, Xiangyu;Wang, Kaiyi;Zhao, Chunjiang;Zou, Yidong;Pan, Shouhui;Yang, Feng;Zhang, Dongfeng;Han, Yanyun;Zhao, Xiangyu;Wang, Kaiyi;Zhao, Chunjiang

关键词：maize varieties; knowledge graph; recommendation model; RippleNet; county-scale

MOBILE SMART DEVICE-BASED VEGETABLE DISEASE AND INSECT PEST RECOGNITION METHOD

作者其他论文 更多>>

Prediction of maize cultivar yield based on machine learning algorithms for precise promotion and planting

Developing a comprehensive evaluation model of variety adaptability based on machine learning method

TrG2P: A transfer-learning-based tool integrating multi-trait data for accurate prediction of crop yield

IoT-based system of prevention and control for crop diseases and insect pests

Prediction of corn variety yield with attribute-missing data via graph neural network

Maize yield prediction using federated random forest

Precise Recommendation Method of Suitable Planting Areas of Maize Varieties Based on Knowledge Graph

意 见 箱

作者其他论文更多>>

意见箱