|Table of Contents|

A Tomato Leaf Disease Detection Algorithm Based on CNN Multi-convolution Feature and HOG

《北方园艺》[ISSN:1001-0009/CN:23-1247/S]

Issue:
2020年04
Page:
147-152
Research Field:
Publishing date:

Info

Title:
A Tomato Leaf Disease Detection Algorithm Based on CNN Multi-convolution Feature and HOG
Author(s):
LIU Jun; WANG Xuewei
(Facility Horticulture Laboratory of Universities in Shandong,Weifang University of Science and Technology,Weifang,Shandong 262700)
Keywords:
tomato leaf disease detection; convolutional neural network; multiple convolution features; HOG
PACS:
-
DOI:
10.11937/bfyy.20193405
Abstract:
Traditional tomato leaf disease detection relies on time-consuming and laborious artificial feature design and must be carefully designed for different types of tomato diseases.Symptoms of tomato leaf diseases are complex and adopting the methods of artificial design features is difficult.Convolutional Neural Network (CNN) can automatically discover the abstract features hidden in the diseased images,and its performance is superior in the field of image recognition.In this paper,the method combining CNN with traditional HOG+SVM algorithm was proposed to extract the shallow features of tomato leaf diseases,input them into HOG to generate HOG feature and merge them,and finally input them into SVM classifier to obtain disease detection results.Experiments showed that this method could improve the precision of tomato leaf disease detection.

References:

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Last Update: 2020-04-05