Intra-row Weed Detection Method in Field Based on Texture and Color Feature

2013 ◽  
Vol 5 (8) ◽  
pp. 806-812 ◽  
Author(s):  
Qin Ma ◽  
Dehai Zhu ◽  
Junming Liu ◽  
Wei Xiong ◽  
Hong Chen
Keyword(s):  
Detection Method ◽  
Weed Detection ◽  
Color Feature

scholarly journals Weed Density Detection Method Based on Absolute Feature Corner Points in Field

Agronomy ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 113
Author(s):  
Yanlei Xu ◽  
Run He ◽  
Zongmei Gao ◽  
Chenxiao Li ◽  
Yuting Zhai ◽  
...  
Keyword(s):  
Weed Management ◽  
Detection Method ◽  
Digital Camera ◽  
Weed Detection ◽  
Position Information ◽  
Weed Density ◽  
Harris Corner ◽  
Weed Distribution ◽  
First Time ◽  
Precision Spraying

Field weeds identification is challenging for precision spraying, i.e., the automation identification of the weeds from the crops. For rapidly obtaining weed distribution in field, this study developed a weed density detection method based on absolute feature corner point (AFCP) algorithm for the first time. For optimizing the AFCP algorithm, image preprocessing was firstly performed through a sub-module processing capable of segmenting and optimizing the field images. The AFCP algorithm improved Harris corner to extract corners of single crop and weed and then sub-absolute corner classifier as well as absolute corner classifier were proposed for absolute corners detection of crop rows. Then, the AFCP algorithm merged absolute corners to identify crop and weed position information. Meanwhile, the weed distribution was obtained based on two weed density parameters (weed pressure and cluster rate). At last, the AFCP algorithm was validated based on the images that were obtained using one typical digital camera mounted on the tractor in field. The results showed that the proposed weed detection method manifested well given its ability to process an image of 2748 × 576 pixels using 782 ms as well as its accuracy in identifying weeds reaching 90.3%. Such results indicated that the weed detection method based on AFCP algorithm met the requirements of practical weed management in field, including the real-time images computation processing and accuracy, which provided the theoretical base for the precision spraying operations.

scholarly journals Real Time Weed Detection using a Boosted Cascade of Simple Features

2016 ◽  
Vol 6 (6) ◽  
pp. 2755
Author(s):  
Adil Tannouche ◽  
Khalid Sbai ◽  
Miloud Rahmoune ◽  
Rachid Agounoun ◽  
Abdelhai Rahmani ◽  
...  
Keyword(s):  
Real Time ◽  
Precision Agriculture ◽  
Detection Method ◽  
Crucial Issue ◽  
Weed Detection ◽  
New Horizons ◽  
Discriminative Classifiers ◽  
Simple Features ◽  
Different Cultures

<p>Weed detection is a crucial issue in precision agriculture. In computer vision, variety of techniques are developed to detect, identify and locate weeds in different cultures. In this article, we present a real-time new weed detection method, through an embedded monocular vision. Our approach is based on the use of a cascade of discriminative classifiers formed by the Haar-like features. The quality of the results determines the validity of our approach, and opens the way to new horizons in weed detection.</p>

Real Time Weed Detection using a Boosted Cascade of Simple Features

2016 ◽  
Vol 6 (6) ◽  
pp. 2755 ◽  
Author(s):  
Adil Tannouche ◽  
Khalid Sbai ◽  
Miloud Rahmoune ◽  
Rachid Agounoun ◽  
Abdelhai Rahmani ◽  
...  
Keyword(s):  
Real Time ◽  
Precision Agriculture ◽  
Detection Method ◽  
Crucial Issue ◽  
Weed Detection ◽  
New Horizons ◽  
Discriminative Classifiers ◽  
Simple Features ◽  
Different Cultures

<p>Weed detection is a crucial issue in precision agriculture. In computer vision, variety of techniques are developed to detect, identify and locate weeds in different cultures. In this article, we present a real-time new weed detection method, through an embedded monocular vision. Our approach is based on the use of a cascade of discriminative classifiers formed by the Haar-like features. The quality of the results determines the validity of our approach, and opens the way to new horizons in weed detection.</p>

Rice Weed detection method on YOLOv4 convolutional neural network

2021 ◽  
Author(s):  
Dong Hu ◽  
Chao Ma ◽  
Zhihui Tian ◽  
Guohui Shen ◽  
Linyi Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Detection Method ◽  
Weed Detection

scholarly journals Rapid Detection Method of Moldy Maize Kernels Based on Color Feature

2014 ◽  
Vol 6 ◽  
pp. 625090 ◽  
Author(s):  
Xuan Chu ◽  
Yong Tao ◽  
Wei Wang ◽  
Ying Yuan ◽  
Mingjie Xi
Keyword(s):  
Machine Vision ◽  
Rapid Detection ◽  
Detection Method ◽  
Color Feature ◽  
Maize Kernel ◽  
Color Features ◽  
Maize Kernels ◽  
Rgb Model

In order to find the moldy maize kernels quickly, a method based on machine vision was proposed in this paper. Firstly, images of maize kernels were taken by the moldy maize sorting equipment, and three parts of every kernel, that is, moldy plaques, healthy endosperm and healthy embryo, were selected from these images. Then a threshold was set in R channel by analyzing color features of those three parts in RGB model. In this method, moldy plaques can be identified roughly. After that the location of the moldy plaques on the kernels was studied, a circle, whose centre was approximately the centroid of a maize kernel and diameter was about the width of embryos, was set to exclude the interference caused by shadow. This method, with the accuracy of 92.1%, laid a good foundation for the further study of moldy maize sorting equipment.

Research of Face Detection Method Based on Skin Color Feature

2013 ◽  
Vol 373-375 ◽  
pp. 478-482
Author(s):  
Qing Ye
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Detection Method ◽  
Recognition System ◽  
Human Face ◽  
Color Feature ◽  
Binary Images ◽  
Face Recognition System ◽  
The Face ◽  
To Receive

Human face detection is the first critical step of face recognition system. This paper proposed a face detection method based on skin color feature. Firstly, the method of building a skin color feature from RGB to YCbCr and extracting skin color region according the chrominance similarity was used to extract the face gray image. Secondly, image smoothness and image binarization were used to receive the binary image, then mathematical morphology operators were used to eliminate the binary images noise and disturbance. At last, human face regions are detected through projection operation. The result of experimentation affirms that the method is efficient to detect human face.

Sign in / Sign up

Export Citation Format

Share Document