scholarly journals Clustering Algorithm Based Straight and Curved Crop Row Detection Using Color Based Segmentation

2020 ◽  
Author(s):  
Nazmuzzaman Khan ◽  
Veera P. Rajendran ◽  
Mohammad Al Hasan ◽  
Sohel Anwar
Keyword(s):  
Precision Agriculture ◽  
Autonomous Navigation ◽  
Clustering Algorithm ◽  
Weather Condition ◽  
Detection Algorithm ◽  
Growth Stages ◽  
Clustering Methods ◽  
Tuning Parameters ◽  
Weed Growth ◽  
Spacing Variation

Abstract Autonomous navigation of agricultural robot is an essential task in precision agriculture, and success of this task critically depends on accurate detection of crop rows using computer vision methodologies. This is a challenging task due to substantial natural variations in crop row images due to various factors, including, missing crops in parts of a row, high and irregular weed growth between rows, different crop growth stages, different inter-crop spacing, variation in weather condition, and lighting. The processing time of the detection algorithm also needs to be small so that the desired number of image frames from continuous video can be processed in real-time. To cope with all the above mentioned requirements, we propose a crop row detection algorithm consisting of the following three linked stages: (1) color based segmentation for differentiating crop and weed from background, (2) differentiating crop and weed pixels using clustering algorithm and (3) robust line fitting to detect crop rows. We test the proposed algorithm over a wide variety of scenarios and compare its performance against four different types of existing strategies for crop row detection. Experimental results show that the proposed algorithm perform better than the competing algorithms with reasonable accuracy. We also perform additional experiment to test the robustness of the proposed algorithm over different values of the tuning parameters and over different clustering methods, such as, KMeans, MeanShift, Agglomerative, and HDBSCAN.

scholarly journals A novel navigation system for an autonomous mobile robot in an uncertain environment

Robotica ◽  
2021 ◽  
pp. 1-26
Author(s):  
Meng-Yuan Chen ◽  
Yong-Jian Wu ◽  
Hongmei He
Keyword(s):  
Mobile Robot ◽  
Navigation System ◽  
Autonomous Navigation ◽  
Clustering Algorithm ◽  
Modular Design ◽  
Optimal Path ◽  
Surrounding Environment ◽  
Computing Complexity ◽  
The Arts ◽  
Window Approach

Abstract In this paper, we developed a new navigation system, called ATCM, which detects obstacles in a sliding window with an adaptive threshold clustering algorithm, classifies the detected obstacles with a decision tree, heuristically predicts potential collision and finds optimal path with a simplified Morphin algorithm. This system has the merits of optimal free-collision path, small memory size and less computing complexity, compared with the state of the arts in robot navigation. The modular design of 6-steps navigation provides a holistic methodology to implement and verify the performance of a robot’s navigation system. The experiments on simulation and a physical robot for the eight scenarios demonstrate that the robot can effectively and efficiently avoid potential collisions with any static or dynamic obstacles in its surrounding environment. Compared with the particle swarm optimisation, the dynamic window approach and the traditional Morphin algorithm for the autonomous navigation of a mobile robot in a static environment, ATCM achieved the shortest path with higher efficiency.

scholarly journals Community Detection Based on Graph Representation Learning in Evolutionary Networks

2021 ◽  
Vol 11 (10) ◽  
pp. 4497
Author(s):  
Dongming Chen ◽  
Mingshuo Nie ◽  
Jie Wang ◽  
Yun Kong ◽  
Dongqi Wang ◽  
...  
Keyword(s):  
Community Detection ◽  
Network Structure ◽  
Clustering Algorithm ◽  
Laplacian Matrix ◽  
Representation Learning ◽  
Detection Algorithm ◽  
Graph Representation ◽  
Time Slice ◽  
Current Time ◽  
Evolutionary Networks

Aiming at analyzing the temporal structures in evolutionary networks, we propose a community detection algorithm based on graph representation learning. The proposed algorithm employs a Laplacian matrix to obtain the node relationship information of the directly connected edges of the network structure at the previous time slice, the deep sparse autoencoder learns to represent the network structure under the current time slice, and the K-means clustering algorithm is used to partition the low-dimensional feature matrix of the network structure under the current time slice into communities. Experiments on three real datasets show that the proposed algorithm outperformed the baselines regarding effectiveness and feasibility.

CLUSTERING USING AN IMPROVED HYBRID GENETIC ALGORITHM

2007 ◽  
Vol 16 (06) ◽  
pp. 919-934
Author(s):  
YONGGUO LIU ◽  
XIAORONG PU ◽  
YIDONG SHEN ◽  
ZHANG YI ◽  
XIAOFENG LIAO
Keyword(s):  
Genetic Algorithm ◽  
Clustering Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Sum Of Squares ◽  
Clustering Methods ◽  
Clustering Problem ◽  
Mutation Operation ◽  
Iteration Methods ◽  
Genetic Clustering ◽  
The Individual

In this article, a new genetic clustering algorithm called the Improved Hybrid Genetic Clustering Algorithm (IHGCA) is proposed to deal with the clustering problem under the criterion of minimum sum of squares clustering. In IHGCA, the improvement operation including five local iteration methods is developed to tune the individual and accelerate the convergence speed of the clustering algorithm, and the partition-absorption mutation operation is designed to reassign objects among different clusters. By experimental simulations, its superiority over some known genetic clustering methods is demonstrated.

Empirical Evaluation of Genetic Clustering Methods Using Multilocus Genotypes From 20 Chicken Breeds

Genetics ◽  
2001 ◽  
Vol 159 (2) ◽  
pp. 699-713
Author(s):  
Noah A Rosenberg ◽  
Terry Burke ◽  
Kari Elo ◽  
Marcus W Feldman ◽  
Paul J Freidlin ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Population Structure ◽  
Clustering Algorithm ◽  
Empirical Evaluation ◽  
Unknown Origin ◽  
Clustering Methods ◽  
Genetic Cluster ◽  
Data Set ◽  
Multilocus Genotypes ◽  
Chicken Breeds

Abstract We tested the utility of genetic cluster analysis in ascertaining population structure of a large data set for which population structure was previously known. Each of 600 individuals representing 20 distinct chicken breeds was genotyped for 27 microsatellite loci, and individual multilocus genotypes were used to infer genetic clusters. Individuals from each breed were inferred to belong mostly to the same cluster. The clustering success rate, measuring the fraction of individuals that were properly inferred to belong to their correct breeds, was consistently ~98%. When markers of highest expected heterozygosity were used, genotypes that included at least 8–10 highly variable markers from among the 27 markers genotyped also achieved >95% clustering success. When 12–15 highly variable markers and only 15–20 of the 30 individuals per breed were used, clustering success was at least 90%. We suggest that in species for which population structure is of interest, databases of multilocus genotypes at highly variable markers should be compiled. These genotypes could then be used as training samples for genetic cluster analysis and to facilitate assignments of individuals of unknown origin to populations. The clustering algorithm has potential applications in defining the within-species genetic units that are useful in problems of conservation.

scholarly journals A Fast Detection Algorithm for the X-Ray Pulsar Signal

2017 ◽  
Vol 2017 ◽  
pp. 1-5 ◽  
Author(s):  
Hao Liang ◽  
Yafeng Zhan
Keyword(s):  
False Alarm ◽  
Navigation System ◽  
Detection Probability ◽  
Autonomous Navigation ◽  
Detection Algorithm ◽  
Observation Time ◽  
X Ray ◽  
Fast Detection ◽  
Simulation Results ◽  
Short Observation Time

The detection of the X-ray pulsar signal is important for the autonomous navigation system using X-ray pulsars. In the condition of short observation time and limited number of photons for detection, the noise does not obey the Gaussian distribution. This fact has been little considered extant. In this paper, the model of the X-ray pulsar signal is rebuilt as the nonhomogeneous Poisson distribution and, in the condition of a fixed false alarm rate, a fast detection algorithm based on maximizing the detection probability is proposed. Simulation results show the effectiveness of the proposed detection algorithm.

scholarly journals Influence of meteorological parameters on wheat yield under different sowing conditions

MAUSAM ◽  
2022 ◽  
Vol 73 (1) ◽  
pp. 161-172
Author(s):  
ANANTA VASHISTH ◽  
DEBASISH ROY ◽  
AVINASH GOYAL ◽  
P. KRISHNAN
Keyword(s):  
Relative Humidity ◽  
Soil Temperature ◽  
Air Temperature ◽  
Field Experiments ◽  
Wheat Yield ◽  
Weather Condition ◽  
Grain Filling ◽  
Crop Growth ◽  
Leaf Water ◽  
Growth Stages

Field experiments were conducted on the research farm of IARI, New Delhi during Rabi 2016-17 and 2017-18. Three varieties of wheat (PBW-723, HD-2967 and HD-3086) were sown on three different dates for generating different weather condition during various phenological stages of crop. Results showed that during early crop growth stages soil moisture had higher value and soil temperature had lower value and with progress of crop growth stage, the moisture in the upper layer decreased and soil temperature increased significantly as compared to the bottom layers. During tillering and jointing stage, air temperature within canopy was more and relative humidity was less while during flowering and grain filling stage, air temperature within canopy was less and relative humidity was more in timely sown crop as compared to late and very late sown crop. Radiation use efficiency and relative leaf water content had significantly higher value while leaf water potential had lower value in timely sown crop followed by late and very late sown crop. Yield had higher value in HD-3086 followed by HD-2967 and PBW-723 in all weather conditions. Canopy air temperature difference had positive value in very late sown crop particularly during flowering and grain-filling stages. This reflects in the yield. Yield was more in timely sown crop as compared to late and very late sown crop.  

scholarly journals A Novel High-Dimensional Trajectories Construction Network based on Multi-Clustering Algorithm

2021 ◽  
Author(s):  
Feiyang Ren ◽  
Yi Han ◽  
Shaohan Wang ◽  
He Jiang
Keyword(s):  
Economic Analysis ◽  
Clustering Algorithm ◽  
Transportation Network ◽  
High Dimensional ◽  
Clustering Methods ◽  
Marine Transportation ◽  
Network Construction ◽  
National Economic ◽  
Multi Level ◽  
State Of Art

Abstract A novel marine transportation network based on high-dimensional AIS data with a multi-level clustering algorithm is proposed to discover important waypoints in trajectories based on selected navigation features. This network contains two parts: the calculation of major nodes with CLIQUE and BIRCH clustering methods and navigation network construction with edge construction theory. Unlike the state-of-art work for navigation clustering with only ship coordinate, the proposed method contains more high-dimensional features such as drafting, weather, and fuel consumption. By comparing the historical AIS data, more than 220,133 lines of data in 30 days were used to extract 440 major nodal points in less than 4 minutes with ordinary PC specs (i5 processer). The proposed method can be performed on more dimensional data for better ship path planning or even national economic analysis. Current work has shown good performance on complex ship trajectories distinction and great potential for future shipping transportation market analytical predictions.

scholarly journals A Hybrid Deep Clustering Approach for Robust Cell Type Profiling Using Single-cell RNA-seq Data

2019 ◽  
Author(s):  
Suhas Srinivasan ◽  
Nathan T. Johnson ◽  
Dmitry Korkin
Keyword(s):  
Deep Learning ◽  
Single Cell ◽  
Clustering Algorithm ◽  
Hybrid Approach ◽  
Feature Learning ◽  
Specific Cell ◽  
Clustering Methods ◽  
Model Based Clustering ◽  
Clustering And Classification ◽  
Living Organisms

AbstractSingle-cell RNA sequencing (scRNA-seq) is a recent technology that enables fine-grained discovery of cellular subtypes and specific cell states. It routinely uses machine learning methods, such as feature learning, clustering, and classification, to assist in uncovering novel information from scRNA-seq data. However, current methods are not well suited to deal with the substantial amounts of noise that is created by the experiments or the variation that occurs due to differences in the cells of the same type. Here, we develop a new hybrid approach, Deep Unsupervised Single-cell Clustering (DUSC), that integrates feature generation based on a deep learning architecture with a model-based clustering algorithm, to find a compact and informative representation of the single-cell transcriptomic data generating robust clusters. We also include a technique to estimate an efficient number of latent features in the deep learning model. Our method outperforms both classical and state-of-the-art feature learning and clustering methods, approaching the accuracy of supervised learning. The method is freely available to the community and will hopefully facilitate our understanding of the cellular atlas of living organisms as well as provide the means to improve patient diagnostics and treatment.

scholarly journals Pedestrian detection algorithm based on improved muti-scale feature fusion

2021 ◽  
Vol 2078 (1) ◽  
pp. 012008
Author(s):  
Hui Liu ◽  
Keyang Cheng
Keyword(s):  
Clustering Algorithm ◽  
Feature Fusion ◽  
Pedestrian Detection ◽  
Detection Algorithm ◽  
Data Sets ◽  
False Detection ◽  
Scale Feature ◽  
Multi Scale ◽  
Dilated Convolution ◽  
Small Targets

Abstract Aiming at the problem of false detection and missed detection of small targets and occluded targets in the process of pedestrian detection, a pedestrian detection algorithm based on improved multi-scale feature fusion is proposed. First, for the YOLOv4 multi-scale feature fusion module PANet, which does not consider the interaction relationship between scales, PANet is improved to reduce the semantic gap between scales, and the attention mechanism is introduced to learn the importance of different layers to strengthen feature fusion; then, dilated convolution is introduced. Dilated convolution reduces the problem of information loss during the downsampling process; finally, the K-means clustering algorithm is used to redesign the anchor box and modify the loss function to detect a single category. The experimental results show that the improved pedestrian detection algorithm in the INRIA and WiderPerson data sets under different congestion conditions, the AP reaches 96.83% and 59.67%, respectively. Compared with the pedestrian detection results of the YOLOv4 model, the algorithm improves by 2.41% and 1.03%, respectively. The problem of false detection and missed detection of small targets and occlusion has been significantly improved.

scholarly journals Hybrid K Mean Clustering Algorithm for Crop Production Analysis in Agriculture

2019 ◽  
Vol 9 (2S) ◽  
pp. 9-13
Keyword(s):  
Cluster Analysis ◽  
Precision Agriculture ◽  
Crop Production ◽  
Clustering Algorithm ◽  
Research Work ◽  
K Value ◽  
Clustering Technique ◽  
Production Analysis ◽  
Initial Cluster ◽  
The Stability

The proposed research work aims to perform the cluster analysis in the field of Precision Agriculture. The k-means technique is implemented to cluster the agriculture data. Selecting K value plays a major role in k-mean algorithm. Different techniques are used to identify the number of cluster value (k-value). Identification of suitable initial centroid has an important role in k-means algorithm. In general it will be selected randomly. In the proposed work to get the stability in the result Hybrid K-Mean clustering is used to identify the initial centroids. Since initial cluster centers are well defined Hybrid K-Means acts as a stable clustering technique.

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