scholarly journals Visual Guidance and Egg Collection Scheme for a Smart Poultry Robot for Free-Range Farms

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6624
Author(s):  
Chung-Liang Chang ◽  
Bo-Xuan Xie ◽  
Chia-Hui Wang
Keyword(s):  
Recognition Rate ◽  
Climatic Conditions ◽  
Egg Laying ◽  
Egg Recognition ◽  
Labor Costs ◽  
Natural Behavior ◽  
Feature Extraction Method ◽  
Free Range ◽  
Smart Mobile ◽  
Navigation Method

Free-range chicken farming allows egg-laying hens to move freely through their environment and perform their natural behavior, including laying her eggs. However, it takes time to gather these eggs manually, giving rise to high labor costs. This study proposes a smart mobile robot for poultry farms that can recognize eggs of two different colors on free-range farms. The robot can also pick up and sort eggs without damaging them. An egg feature extraction method with automatic thresholding is employed to detect both white and brown eggs, and a behavior-based navigation method is applied to allow the robot to reach the eggs while avoiding obstacles. The robot can move towards the position of each egg via visual tracking. Once the egg is within the collection area of the robot, it is gathered, sorted and stored in the tank inside the robot. Experiments are carried out in an outdoor field of size 5 m × 5 m under different climatic conditions, and the results showed that the average egg recognition rate is between 94.7% and 97.6%. The proposed mobile poultry robot is low in production cost and simple in operation. It can provide chicken farmers with automatic egg gathering on free-range farms.

scholarly journals Texture Feature Extraction Method for Ground Nephogram Based on Hilbert Spectrum of Bidimensional Empirical Mode Decomposition

2014 ◽  
Vol 31 (9) ◽  
pp. 1982-1994 ◽  
Author(s):  
Xiaoying Chen ◽  
Aiguo Song ◽  
Jianqing Li ◽  
Yimin Zhu ◽  
Xuejin Sun ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Recognition Rate ◽  
Texture Feature ◽  
Intrinsic Mode Functions ◽  
Hilbert Spectrum ◽  
Feature Extraction Method ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Bidimensional Empirical Mode Decomposition

Abstract It is important to recognize the type of cloud for automatic observation by ground nephoscope. Although cloud shapes are protean, cloud textures are relatively stable and contain rich information. In this paper, a novel method is presented to extract the nephogram feature from the Hilbert spectrum of cloud images using bidimensional empirical mode decomposition (BEMD). Cloud images are first decomposed into several intrinsic mode functions (IMFs) of textural features through BEMD. The IMFs are converted from two- to one-dimensional format, and then the Hilbert–Huang transform is performed to obtain the Hilbert spectrum and the Hilbert marginal spectrum. It is shown that the Hilbert spectrum and the Hilbert marginal spectrum of different types of cloud textural images can be divided into three different frequency bands. A recognition rate of 87.5%–96.97% is achieved through random cloud image testing using this algorithm, indicating the efficiency of the proposed method for cloud nephogram.

scholarly journals A Walking-in-Place Method for Virtual Reality Using Position and Orientation Tracking

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2832 ◽  
Author(s):  
Juyoung Lee ◽  
Sang Chul Ahn ◽  
Jae-In Hwang
Keyword(s):  
Virtual Reality ◽  
Inertial Measurement Unit ◽  
Recognition Rate ◽  
Head Tilt ◽  
The Body ◽  
Measurement Unit ◽  
Position And Orientation ◽  
Intentional Motion ◽  
Navigation Method ◽  
Virtual Velocity

People are interested in traveling in an infinite virtual environment, but no standard navigation method exists yet in Virtual Reality (VR). The Walking-In-Place (WIP) technique is a navigation method that simulates movement to enable immersive travel with less simulator sickness in VR. However, attaching the sensor to the body is troublesome. A previously introduced method that performed WIP using an Inertial Measurement Unit (IMU) helped address this problem. That method does not require placement of additional sensors on the body. That study proved, through evaluation, the acceptable performance of WIP. However, this method has limitations, including a high step-recognition rate when the user does various body motions within the tracking area. Previous works also did not evaluate WIP step recognition accuracy. In this paper, we propose a novel WIP method using position and orientation tracking, which are provided in the most PC-based VR HMDs. Our method also does not require additional sensors on the body and is more stable than the IMU-based method for non-WIP motions. We evaluated our method with nine subjects and found that the WIP step accuracy was 99.32% regardless of head tilt, and the error rate was 0% for squat motion, which is a motion prone to error. We distinguish jog-in-place as “intentional motion” and others as “unintentional motion”. This shows that our method correctly recognizes only jog-in-place. We also apply the saw-tooth function virtual velocity to our method in a mathematical way. Natural navigation is possible when the virtual velocity approach is applied to the WIP method. Our method is useful for various applications which requires jogging.

scholarly journals Hot summers, long life: egg laying strategies of Maniola butterflies are affected by geographic provenance rather than adult diet

2013 ◽  
Vol 82 (1) ◽  
pp. 27-36 ◽  
Author(s):  
Andrea Grill ◽  
Andrea Cerny ◽  
Konrad Fiedler
Keyword(s):  
Amino Acids ◽  
Egg Production ◽  
Sucrose Solution ◽  
Reproductive Period ◽  
Climatic Conditions ◽  
Egg Laying ◽  
Endogenous Factors ◽  
Central European ◽  
Oviposition Strategy ◽  
Adult Diet

Maniola butterflies undergo summer dormancy in dry and hot habitats and deposit their eggs only in early autumn when conditions become more favourable for their offspring. Female individuals of this genus are therefore relatively long-lived. For long-lived butterflies adult diet is of particular importance. We tested if added amino acids in nectar substitute fed to the butterflies affected timing of oviposition, fecundity and longevity. A hundred Maniola females were sampled from Mediterranean and Central European populations and made to oviposit under controlled laboratory conditions. Forty individuals were offered sucrose solution with additional amino acids while the remainder were fed with plain sucrose solution. We found that egg-laying strategies and longevity depended on geographic provenance rather than diet. Supplementary amino acids in adult diet did neither prolong lifetime nor increase total egg production. Maniola females from Sardinia started to lay eggs at least 20 days later relative to Central European M. jurtina and lived three times as long. Mediterranean individuals had on average twice the length of reproductive period and lifespan relative to Central European ones, and individuals of Pannonian origin lived longer than Alpine butterflies. Average total egg numbers were 200-350 eggs per female and did not differ significantly between populations. The fact that oviposition strategy could not be altered through diet may indicate that for univoltine butterflies, like Maniola, diet-quality at the adult stage is less important than endogenous factors, or factors the butterflies are exposed to in an earlier developmental stage than the imago. Oviposition strategy closely matched the climatic conditions that prevail in the geographic regions where these butterflies fly.

scholarly journals Research on Fault Feature Extraction Method Based on NOFRFs and Its Application in Rotor Faults

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Yang Liu ◽  
Yulai Zhao ◽  
Jintao Li ◽  
Fangquan Xi ◽  
Shuanghe Yu ◽  
...  
Keyword(s):  
Early Stage ◽  
Recognition Rate ◽  
High Order ◽  
Nonlinear Phenomena ◽  
Contribution Rate ◽  
Output Frequency ◽  
Nonlinear Characteristics ◽  
Feature Extraction Method ◽  
Rotor Faults ◽  
Fault Feature Extraction

Rub-impact between the rotating and static parts is a more common fault. The occurrence of faults is often accompanied by the generation of nonlinear phenomena. However, it is difficult to find out because the nonlinear characteristics are not obvious at the beginning of the fault. As a new frequency domain-based method, nonlinear output frequency response functions (NOFRFs) use the vibration response to extract the nonlinear characteristics of the system. This method has a better recognition rate for fault detection. Also, it has been applied in structural damages detection, but the high-order NOFRFs have the characteristics that the signals are weak and the features are difficult to extract. On this basis, the concept of the weighted contribution rate of the NOFRFs is proposed in this paper. The variable weighted coefficients with orders are used to amplify the influence of high-order NOFRFs on the nonlinearity of the system so as to extract its fault characteristics. The new index RI is proposed based on Clenshaw–Curtis quadrature formula to eliminate the effect of artificially selected weighted coefficients on sensitivity. Especially in the early stage of the fault, the new index varies greatly with the deepening of the fault. Both simulation and experimental results verify the validity and practicability of the new index. The new index has certain guiding significance in the detection of mechanical system faults.

Feature Extraction by Decryption to Target

2014 ◽  
Vol 1008-1009 ◽  
pp. 1509-1512
Author(s):  
Qing E Wu ◽  
Hong Wang ◽  
Li Fen Ding
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Extraction Method ◽  
Recognition Rate ◽  
Region Of Interest ◽  
Correct Recognition ◽  
Palmprint Recognition ◽  
Feature Extraction Method ◽  
Energy Feature ◽  
Correct Recognition Rate

To carry out an effective classification and recognition for target, this paper studied the target owned characteristics, discussed a decryption algorithm, gave a feature extraction method based on the decryption process, and extracted the feature of palmprint in region of interest. Moreover, this paper used the wavelet transform to extract the energy feature of target, gave an approach on matching and recognition to improve the correctness and efficiency of existing recognition approaches, and compared it with existing approaches of palmprint recognition by experiments. The experiment results show that the correct recognition rate of the approach in this paper is improved averagely by 2.34% than that of the existing recognition approaches.

Research of Face Recognition Methods Based on Binding Feature Extraction

2014 ◽  
Vol 568-570 ◽  
pp. 668-671
Author(s):  
Yi Long ◽  
Fu Rong Liu ◽  
Guo Qing Qiu
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Extraction Method ◽  
Local Binary Pattern ◽  
Feature Vector ◽  
Recognition Rate ◽  
Principal Component ◽  
Face Image ◽  
Feature Extraction Method ◽  
Histogram Feature

To address the problem that the dimension of the feature vector extracted by Local Binary Pattern (LBP) for face recognition is too high and Principal Component Analysis (PCA) extract features are not the best classification features, an efficient feature extraction method using LBP, PCA and Maximum scatter difference (MSD) has been introduced in this paper. The original face image is firstly divided into sub-images, then the LBP operator is applied to extract the histogram feature. and the feature dimensions are further reduced by using PCA. Finally,MSD is performed on the reduced PCA-based feature.The experimental results on ORL and Yale database demonstrate that the proposed method can classify more effectively and can get higher recognition rate than the traditional recognition methods.

Research of Feature Extraction Method on Facial Expression Change

2011 ◽  
Vol 211-212 ◽  
pp. 813-817 ◽  
Author(s):  
Jin Qing Liu ◽  
Qun Zhen Fan
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Facial Expression ◽  
Extraction Method ◽  
Dimensional Space ◽  
Recognition Rate ◽  
Low Frequency ◽  
Expression Change ◽  
Feature Extraction Method ◽  
Low Dimensional

In this paper, the purpose is to find a method that can be more suited to facial expression change and also improve the recognition rate. The proposed system contains three parts, wavelet transform, Fisher linear discriminant method feature extraction and face classification. The basic idea of the proposed method is that first extract the low-frequency components through wavelet transform, then the low-frequency images mapped into a low-dimensional space by PCA transform, and finally the utilization of LDA feature extraction method in low-dimensional space. The algorithms were tested on ORL and Yale face database, respectively. Experimental results shows that the proposed method not only improve the recognition rate, but also improve the recognition speed. This method can effectively overcome the impact of expression changes on face recognition, and play a certain role in inhibition of expression.

The population of laying hens loses important genes: a case history

1997 ◽  
Vol 22 ◽  
pp. 71-78 ◽  
Author(s):  
P. Sørensen
Keyword(s):  
Laying Hens ◽  
Consumer Preferences ◽  
Genetic Material ◽  
Considerable Change ◽  
Egg Laying ◽  
Poultry Production ◽  
Production Potential ◽  
Strong Concentration ◽  
Free Range ◽  
Cage System

SummaryThe switch from keeping laying hens in a floor or free range system into a cage system led to a considerable change in the way that breeding and selection took place. In the past 40–50 years up to the present date, the increase in genetic improvement of the egg laying trait was substantial. However, cage-adapted populations of laying hens seem to have lost some of their abilities to an adequate performance when returned to the old floor\free range systems. The strong concentration of all parts of the poultry production has meant that less than 10 international breeding companies supply most hens for laying purposes in the world and they have very little interest in developing genetic material for the West-European region where there are marked consumer preferences for eggs produced in non-cage systems. A particular Danish line, of White Leghorn origin named “The Skalborg hen” seems to have survived during an era of cage production system and they seems to have a production potential at farm level.

A Self-Organizing Hierarchical Classifier for Multi-Lingual Large-Set Oriental Character Recognition

1998 ◽  
Vol 12 (02) ◽  
pp. 191-208 ◽  
Author(s):  
Hee-Seon Park ◽  
Hee-Heon Song ◽  
Seong-Whan Lee
Keyword(s):  
Character Recognition ◽  
Learning Algorithm ◽  
Recognition Rate ◽  
Large Set ◽  
Feature Extraction Method ◽  
Tree Classifier ◽  
Hierarchical Neural Network ◽  
Dot Density ◽  
Hierarchical Classifier ◽  
Self Organizing

In this paper, we propose a practical scheme for multi-lingual, multi-font and multi-size large-set Oriental character recognition using a self-organizing hierarchical neural network classifier. In order to absorb the variation of the character shapes in multi-font and multi-size characters, a modified nonlinear shape normalization method based on dot density was introduced, and also to represent the different topological structures of multi-lingual characters effectively, a hierarchical feature extraction method was adopted. For coarse classification, a tree classifier and SOFM/LVQ based classifier which is composed of an adaptive SOFM coarse-classifier and an LVQ4 language-classifier were considered. For fine classification, a classifier based on LVQ4 learning algorithm has been developed. The experimental results revealed that the proposed scheme has the highest recognition rate of 98.27% for testing data with 7,320 kinds of multi-lingual classes and the time performance of more than 40 characters per second on 486DX-2 66MHz PC.

scholarly journals Feature Recognition of Crop Growth Information in Precision Farming

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Hanqing Sun ◽  
Xiaohui Zhang ◽  
Zhou Yu ◽  
Gang Xi
Keyword(s):  
Principal Component Analysis ◽  
Feature Recognition ◽  
Recognition Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Electrical Signals ◽  
Feature Extraction Method ◽  
Signal Type ◽  
Combined Features ◽  
New Feature

To identify plant electrical signals effectively, a new feature extraction method based on multiwavelet entropy and principal component analysis is proposed. The wavelet energy entropy, wavelet singular entropy, and the wavelet variance entropy of plants’ electrical signals are extracted by a wavelet transformation to construct the combined features. Principal component analysis (PCA) is applied to treat the constructed features and eliminate redundant information among those features and extract features which can reflect signal type. Finally, the classification method of BP neural network is used to classify the obtained feature vectors. The experimental results show that this method can acquire comparatively high recognition rate, which proposed a new efficient solution for the identification of plant electrical signals.

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