|Table of Contents|

The Combination of HOG Features with LBP Features Applied toTomato Disease and Pest Detection(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2019年03期
Page:
21-
Research Field:
计算机工程
Publishing date:

Info

Title:
The Combination of HOG Features with LBP Features Applied toTomato Disease and Pest Detection
Author(s):
Zou Yongjie; Zhang Yongjun; Qin Yongbin; Zheng Shijun
College of Computer Science and Technology,Guizhou University,Guiyang 550025,China
Keywords:
tomato; insect and disease detection; HOG features; LBP features
PACS:
TP391
DOI:
10.3969/j.issn.1672-1292.2019.03.004
Abstract:
Plant diseases and insect pests are the main challenges facing the agricultural sector. Accurate and rapid detection of plant diseases and insect pests can help to identify early treatment methods,while significantly reducing economic losses. Target detection method based on machine learning can greatly improve the accuracy of object detection and recognition system. This paper presents a tomato pest detection method based on machine learning. The method extracts the HOG and LBP features of tomato samples with and without pests and diseases,and then combines the training samples of SVM classifier to get the detection model. HOG features can better describe the edge characteristics of tomato leaves,and LBP features can better describe the texture characteristics of tomato leaves,so the two features can complement each other to a certain extent. The experimental results show that based on the combination of HOG and LBP features,the detection of tomato leaves with diseases and insect pests obtains good results,and achieves 99.49% detection rate on tomato data set of crop diseases in the global AI challenge.

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Last Update: 2019-09-30