|Table of Contents|

Visual Identification System of Soybean Frogeye Leaf Spot Based on SURF Feature Extraction(PDF)

《大豆科学》[ISSN:1000-9841/CN:23-1227/S]

Issue:
2019年01期
Page:
90-96
Research Field:
Publishing date:

Info

Title:
Visual Identification System of Soybean Frogeye Leaf Spot Based on SURF Feature Extraction
Author(s):
LI Jian-jun1;  SHI Chun-mei2;  SHAN Qi-kai1;  HUA Xiu-ping1;  MENG Qing-xiang1;  WANG Yan1;  WANG Li-li1;  JIANG Yong-cheng1
(1.Institute of Intelligent Detection and Control, Jiamusi University, Jiamusi 154007, China; 2.Heilongjiang Water Conservancy School, Daqing 163000, China)
Keywords:
Machine vision;  Soybean gray spot;  SURF;  SIFT;  Open CV
PACS:
-
DOI:
10.11861/j.issn.1000-9841.2019.01.0090
Abstract:
Machine vision technology is one of the key technologies of farmland information collection system, which is widely used in precision agriculture. As premises and keys of precision pesticide application, recognition accuracy of crop disease site has strong influences on the diseases control. The badness of recognition accuracy is urgent to be solved. Combining machine vision and computer image technology, visual studio 2010 is used as the experimental platform to build a computer vision identification system of soybean gray spot. The image of soybean leaf was collected by computer camera, and the color image was preprocessed by graying. Two image feature detection methods from Open CV (Open Source Computer Vision Library)-SURF(Speeded Up Robust Features) algorithm and SIFT(Scale-Invariant Feature Transform) algorithm were used to detect gray spot feature points. There were obvious differences between two methods in output frame rate: The range of SIFT algorithm output frame rate was between 0.3 and 0.5 fps, and SURF algorithm was between 0.6 and 0.9 fps. Considering the efficiency and equipment performance, SURF algorithm was selected. The Hessian Matrix of pixel points in the image was established. The feature points were determined by using the Gauss filter and the non-maximum suppression, and then the extreme points were located by the spatial interpolation. The main direction of the feature points was selected according to the Hal wavelet. The descriptor of SURF feature point was constructed to extract feature points, and the program codes were compiled to detect the gray spot in soybean leaves of Heinong 44 in branching stage and pod bearing stages respectively. The results showed that the leaf texture and the feature points in branching stage were few, detection effect was good, the correct rate of detection was 97.3%, and detected time was 0.97 s. The leaf texture and the feature points increase in pod bearing stages, the correct rate of detection was 89.49% and detected time was 1.19 s, which basically meets the functional requirements of soybean gray spot recognition system. Through field trials and using FLANN algorithm to extract feature points of video images, detecting of video image frame rate and matching of feature points were achieved. The detection rate was 90.7% and the matching rate was 93.8%, which provides ideas and references for the next research on precision pesticide application and the design of other farmland information collection system.

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Last Update: 2019-01-22