scholarly journals Performance Analysis of Target Information Recognition System for Agricultural Robots

2021 ◽  
Vol 12 (2) ◽  
pp. 49-60
Author(s):  
Yun Ji ◽  
Rajeev Kumar ◽  
Daljeet Singh ◽  
Maninder Singh
Keyword(s):  
Decision Making ◽  
Production Efficiency ◽  
Vision System ◽  
Recognition Rate ◽  
Robot Vision ◽  
Recognition System ◽  
Economic Benefits ◽  
Prototype System ◽  
Target Information ◽  
Information Recognition

In this paper, an agricultural robot vision system is proposed for two typical environments—farmland and orchard—combined with weeding between crops. The system includes orchard production monitoring and prediction tasks, the target information recognition approach, and visual servo decision making. The results obtained from the proposed system show that using the region combination features of image 2D histogram as the decision-making basis, the accurate and rapid indirect identification and positioning of crop seedlings can be accomplished while skipping the complex process of accurately identifying crops and weeds. The algorithm performs reasonably good as the time of target recognition in the prototype system is found to be less than 16 ms, and the average accurate recognition rate of 97.43% is achieved. The benefits of the proposed system are the continuous improvement of the quality of agricultural products, the rise of production efficiency, and the increase of economic benefits.

scholarly journals Agricultural Transfer Labor Wage Platform Based on Image Content Feature Retrieval and Internet of Things

2021 ◽  
Author(s):  
Ying Wu ◽  
Jikun Liu
Keyword(s):  
Feature Recognition ◽  
Positional Information ◽  
Recognition System ◽  
Accurate Information ◽  
New Era ◽  
Testing Experiment ◽  
Global Vector ◽  
Information Recognition ◽  
Content Feature

Abstract Quick and accurate information identification of agricultural transfer labor wage platform is an essential function of labor intelligent management in the new era. Based on the content feature retrieval, this study constructs an artificial intelligence identity information recognition system and links the system to the salary platform. Simultaneously, this study uses the feature recognition to extract database information and realize intelligent salary assessment. In addition, the deep learning features used in this study are based on the positional information of the sift features and are finally calculated by the activation map to obtain a global vector of an image. Finally, this study design testing experiment to verify the performance of the algorithm. Through the output of the feature picture, it can be seen that the research algorithm has certain effects and can be used as a follow-up system practice.

scholarly journals Design of Vehicle Information Recognition System Based on Machine Vision

2015 ◽  
Vol 7 (1) ◽  
pp. 221-225 ◽  
Author(s):  
Ding Ji-feng ◽  
Zhang Jun-xing ◽  
Xu Shuang ◽  
Yang Ya-ning
Keyword(s):  
Machine Vision ◽  
Recognition System ◽  
Vehicle Information ◽  
Information Recognition

scholarly journals Building Recognition System Based on Improved SIFT Algorithm and Positioning Information

2021 ◽  
Vol 5 (3) ◽  
Author(s):  
Tao Liu ◽  
◽  
Feng Jiang ◽  
Yu Gao ◽  
◽  
...  
Keyword(s):  
General Problem ◽  
Recognition Rate ◽  
Response Speed ◽  
Recognition System ◽  
Small Extent ◽  
Sift Algorithm ◽  
Gps Positioning ◽  
Building Recognition ◽  
Processing Mechanism

In order to solve the general problem, that is, the accurate recognition rate is low in a small extent or when the image resources are few and scattered. This article puts forward a building recognition system that combines GPS positioning information with an improved SIFT algorithm, and adds a pre-processing mechanism to predict the possibility of the building existence in the system, which further reduces mismatch and improves response speed. The final verification shows that this research is actually effective.

Design of label information recognition system based on vision

2021 ◽  
Author(s):  
Zhonghua Miao ◽  
Chenchen Sun ◽  
Nan Li ◽  
Chuangxin He ◽  
Teng Sun
Keyword(s):  
Recognition System ◽  
Label Information ◽  
Information Recognition

scholarly journals An Algorithm for Determining SIFT Matching Score for Dorsal Vein Recognition System

2019 ◽  
Vol 8 (10S) ◽  
pp. 468-471
Keyword(s):  
Recognition System ◽  
Scale Invariant ◽  
Vein Pattern ◽  
Sift Algorithm ◽  
Dorsal Vein ◽  
Damage Recognition ◽  
Dark Color ◽  
Vein Recognition ◽  
Matching Score ◽  
Scale Invariant Feature

Vein pattern recognition has been an increasingly biometric branch nowadays. This technology has many advantages over other biometric technologies. This technology works only on alive person. Vein pattern of every person is unique even in case of twins. Whole procedure of registration for verification of a person gives a hygienic opportunity. Vein pattern of a person remains constant throughout the life until and unless, physically damage. Recognition through veins cannot be affected from aging, color and physical environment because veins are present underneath the skin. NIR cameras of wavelength of 700 nm to 1000 nm are used to capture the images of vein patterns. When infrared radiation falls on the veins, these get illuminated in dark color due to the absorption of radiation by the hemoglobin present in the veins. The SIFT (Scale-Invariant Feature Transform) algorithm has given very good results in feature extortion and matching but it does not provide matching score of features. In this work, we proposed an algorithm to determining the matching score of pattern matched through SIFT algorithm. For experimental purpose, we performed image acquisition, pre-processing, feature extraction and filtering to eliminate noise from the images. We tested our algorithm on the database of 160 persons and we calculated performance of our algorithm in terms of FAR and FRR 2.7% and 4.5% respectively

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