scholarly journals Application of Clustering Method to Determine Production Zones of Field

2015 ◽  
Vol 18 (2) ◽  
pp. 42-45 ◽  
Author(s):  
Martin Ingeli ◽  
Jana Galambošová ◽  
Renáta Benda Prokeinová ◽  
Vladimír Rataj
Keyword(s):  
Input Data ◽  
Yield Potential ◽  
Monitoring Systems ◽  
Clustering Methods ◽  
Control Field ◽  
Yield Data ◽  
Site Specific ◽  
Growing Seasons ◽  
Hierarchical Clustering Methods ◽  
Yield Values

Abstract Determining the production zones of field is an important analysis in the precision farming technology as these may be used to control field operations in site-specific application. The aim of this paper was to evaluate the potential to identify the yield potential zones based on historical yield maps and to evaluate the procedure over the growing extent of input data. Standardized yield values from six growing seasons were considered. Suitable datasets were created, and hierarchical and non-hierarchical clustering methods were applied to create clusters. Results showed that using the data from commercial combine monitoring systems enables determining the zones. Multiple yield data are recommended as the values of analyses increase with the increased number of input datasets. However, commercial data have limitations in terms of complexity.

Effect of Heterogeneous Field Conditions on Corn Seeding Depth Accuracy and Uniformity

2018 ◽  
Vol 34 (5) ◽  
pp. 819-830 ◽  
Author(s):  
Aurelie M. Poncet ◽  
John P. Fulton ◽  
Timothy P. McDonald ◽  
Thorsten Knappenberger ◽  
Joey N. Shaw ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Yield Potential ◽  
Unit Weight ◽  
Soil Conditions ◽  
Volumetric Soil Water Content ◽  
Site Specific ◽  
Growing Seasons ◽  
Unit Depth ◽  
Planter Performance ◽  
Volumetric Soil Water

Abstract. Optimization of planter performance such as uniform seeding depth is required to maximize crop yield potential. Typically, seeding depth is manually adjusted prior to planting by selecting a row-unit depth and a row-unit downforce to ensure proper seed-soil contact. Once set, row-unit depth and downforce are usually not adjusted again for a field although soil conditions may vary. Optimization of planter performance requires automated adjustments of planter settings to varying soil conditions, but development of precision technologies with such capabilities requires a better understanding of soil-planter interactions. The objective of this study was to evaluate seeding depth response to varying soil conditions between and within fields and to discuss implications for development and implementation of active planting technologies. A 6-row John Deere MaxEmerge Plus planter equipped with heavy-duty downforce springs was used to plant corn ( L.) in central Alabama during the 2014 and 2015 growing seasons. Three depths (4.4, 7.0, and 9.5 cm) and three downforces (corresponding to an additional row-unit weight of 0.0, 1.1, and 1.8 kN) were selected to represent common practices. Depth and downforce were not readjusted between fields and growing seasons. Seeding depth was measured after emergence. Corn seeding depth significantly varied with heterogeneous soil conditions between and within fields and the planter failed to achieve uniform seeding depth across a field. Differences in corn seeding depth between fields and growing seasons were as high as 2.1 cm for a given depth and downforce combination. Corn seeding depth significantly co-varied with field elevation but not with volumetric soil water content. Seeding depth varied with elevation at a rate ranging from -0.1 cm/m to -0.6 cm/m. Seeding depth co-variation to field elevation account for some but not all site-specific seeding depth variability identified within each field trial. These findings provide a better understanding of site-specific seeding depth variability and issues to address for the development of site-specific planting technologies to control seeding depth accuracy and improve uniformity. Keywords: Depth control, Downforce, Planter, Precision agriculture, Seeding depth, Uniformity.

scholarly journals The Use of Crop Yield Autocorrelation Data as a Sustainable Approach to Adjust Agronomic Inputs

2021 ◽  
Vol 13 (4) ◽  
pp. 2362
Author(s):  
Thomas M. Koutsos ◽  
Georgios C. Menexes ◽  
Andreas P. Mamolos
Keyword(s):  
Decision Making ◽  
Spatial Analysis ◽  
Crop Yield ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Yield Data ◽  
Site Specific ◽  
Production Areas ◽  
Short Time ◽  
Monitor Data

Agricultural fields have natural within-field soil variations that can be extensive, are usually contiguous, and are not always traceable. As a result, in many cases, site-specific attention is required to adjust inputs and optimize crop performance. Researchers, such as agronomists, agricultural engineers, or economists and other scientists, have shown increased interest in performing yield monitor data analysis to improve farmers’ decision-making concerning the better management of the agronomic inputs in the fields, while following a much more sustainable approach. In this case, spatial analysis of crop yield data with the form of spatial autocorrelation analysis can be used as a practical sustainable approach to locate statistically significant low-production areas. The resulted insights can be used as prescription maps on the tractors to reduce overall inputs and farming costs. This aim of this work is to present the benefits of conducting spatial analysis of yield crop data as a sustainable approach. Current work proves that the implementation of this process is costless, easy to perform and provides a better understanding of the current agronomic needs for better decision-making within a short time, adopting a sustainable approach.

scholarly journals Early generation selection strategy for yield and yield components in white oat

2005 ◽  
Vol 62 (4) ◽  
pp. 357-365 ◽  
Author(s):  
Giovani Benin ◽  
Fernando Irajá Félix de Carvalho ◽  
Antônio Costa de Oliveira ◽  
Claudir Lorencetti ◽  
Igor Pires Valério ◽  
...  
Keyword(s):  
Grain Yield ◽  
Indirect Selection ◽  
Grain Weight ◽  
Yield Potential ◽  
High Yield ◽  
Direct Selection ◽  
Selection Strategy ◽  
Breeding Method ◽  
Growing Seasons ◽  
Selection For

Several studies have searched for higher efficiency on plant selection in generations bearing high frequency of heterozygotes. This work aims to compare the response of direct selection for grain yield, indirect selection through average grain weight and combined selection for higher yield potential and average grain weight of oat plants (Avena sativa L.), using the honeycomb breeding method. These strategies were applied in the growing seasons of 2001 and 2002 in F3 and F4 populations, respectively, in the crosses UPF 18 CTC 5, OR 2 <FONT FACE=Symbol>´</FONT> UPF 7 and OR 2 <FONT FACE=Symbol>´</FONT> UPF 18. The ten best genetic combinations obtained for each cross and selection strategy were evaluated in greenhouse yield trials. Selection of plants with higher yield and average grain weight might be performed on early generations with high levels of heterozygosis. The direct selection for grain yield and indirect selection for average grain weight enabled to increase the average of characters under selection. However, genotypes obtained through direct selection presented lower average grain weight and those obtained through the indirect selection presented lower yield potential. Selection strategies must be run simultaneously to combine in only one genotype high yield potential and large grain weight, enabling maximum genetic gain for both characters.

scholarly journals Performance and profitability of rain-based thresholds for timing fungicide applications in soybean rust control

2020 ◽  
Author(s):  
Gustavo C. Beruski ◽  
Emerson Medeiros Del Ponte ◽  
André B. Pereira ◽  
Mark L. Gleason ◽  
Gil M. S. Câmara ◽  
...  
Keyword(s):  
Disease Severity ◽  
Disease Risk ◽  
Climatic Conditions ◽  
Soybean Rust ◽  
Yield Data ◽  
Growing Seasons ◽  
Action Threshold ◽  
Temperature Thresholds ◽  
Fungicide Mixtures ◽  
Control Disease

AbstractSoybean rust (SBR), caused by the fungus Phakopsora pachyrhizi, is the most damaging disease of soybean in Brazil. Effective management is achieved by means of calendar-timed sprays of fungicide mixtures, which do not explicitly consider weather-associated disease risk. Two rainfall-based action thresholds of Disease Severity Values (DSV50 and DSV80) were proposed and compared with two leaf wetness duration-temperature thresholds of Daily Values of Infection Probability (DVIP6 and DVIP9) and with a calendar (CAL) program, with regards to performance and profitability. An unsprayed check treatment plot was included for calculating relative control. Disease severity and yield data were obtained from 29 experiments conducted at six sites across four states in Brazil during 2012-13, 2014-15 and 2015-16 growing seasons, which represented different growing regions and climatic conditions. The less conservative rainfall action threshold (DSV80) resulted in fewer fungicide sprays compared with the other treatments and the more conservative one (DSV50) resulted in fewer sprays than the DVIP thresholds. Yield was generally higher with the increase of spray number, but the economic analysis showed no significant differences on the risk of not offsetting the costs of fungicide sprays regardless of the system. Therefore, based on the simplicity and the profitability of the rain-based model, the system is a good candidate for incorporating into management of SBR in soybean production fields in Brazil.

Descriptive Analytics

2017 ◽  
pp. 88-112 ◽  
Author(s):  
Sheik Abdullah A ◽  
Selvakumar S ◽  
Ramya C
Keyword(s):  
Social Media ◽  
Predictive Analytics ◽  
Data Retrieval ◽  
Social Media Analytics ◽  
Clustering Methods ◽  
Rule Mining ◽  
Mining Sequence ◽  
Hierarchical Clustering Methods ◽  
Tools And Techniques ◽  
Sequence Rule

Data analytics has becoming one of the challenging platforms across various domains such as telecom, health care, social media and so on. The challenging and most promising task in analytics is the understanding of various patterns in the data. The mechanism of data retrieval and analysis seems to be the promising one in which the algorithms, techniques, way of processing data are in need with the ability to target upon large volumes of data. There are various types of analytical methods such as predictive analytics, descriptive analytics, text analytics, social media analytics and survival analytics. This chapter mainly focuses towards the mechanism of descriptive analytics its types, algorithms and applications. There are various forms of tools and techniques such as association rule mining, sequence rule mining, and data categorization such as hierarchical and non-hierarchical clustering methods with its variants.

scholarly journals Evaluation of crop yield under different nitrogen doses of mineral fertilization

2010 ◽  
Vol 56 (No. 4) ◽  
pp. 163-167 ◽  
Author(s):  
Š. Matějková ◽  
J. Kumhálová ◽  
J. Lipavský
Keyword(s):  
Nitrogen Fertilizer ◽  
Total Yield ◽  
Yield Potential ◽  
Yield Variability ◽  
Production Potential ◽  
Mineral Fertilization ◽  
Winter Rape ◽  
Site Specific ◽  
Combine Harvester ◽  
Different Doses

Yields of winter wheat, winter rape and oats were evaluated in the field; the field was divided into the site-specific zones and treated with variable doses of nitrogen fertilizer in years 2004–2006. Measurements of the yields were carried out with a yield monitor placed in a combine harvester. The measured data were processed into the yield maps by means of ArcGIS 9.2 software. Variable application of fertilizer should balance yield potential of the field. Generally, total yield variability on the field after the application of various doses of experimental fertilizer was similar in the years 2004 (11.3%), 2005 (14.7%) and 2006 (11.7%) in comparison with the year 2003 (25.02%). Variable application of nitrogen in the site-specific zones, created on the basis of the yield levels, decreased the yield variability in comparison with the uniform dose. Different doses of nitrogen fertilizer also enabled to increase utilization of production potential of the experimental field.

R*-Tree Based Similarity and Clustering Analysis for Images

2011 ◽  
pp. 50-61
Author(s):  
Jiaxiong Pi ◽  
Yong Shi ◽  
Zhengxin Chen
Keyword(s):  
Cluster Analysis ◽  
Spatial Data ◽  
Image Data ◽  
Multimedia Retrieval ◽  
Spatial Proximity ◽  
Similarity Analysis ◽  
Analysis Tool ◽  
Clustering Methods ◽  
Hierarchical Clustering Methods ◽  
And Cluster Analysis

Image content analysis plays an important role for adaptive multimedia retrieval. In this chapter, the authors present their work on using a useful spatial data structure, R*-tree, for similarity analysis and cluster analysis of image contents. First, they describe an R*-tree based similarity analysis tool for similarity retrieval of images. They then move on to discuss R*-tree based clustering methods for images, which has been a tricky issue: although objects stored in the same R* tree leaf node enjoys spatial proximity, it is well-known that R* trees cannot be used directly for cluster analysis. Nevertheless, R* tree’s indexing feature can be used to assist existing cluster analysis methods, thus enhancing their performance of cluster quality. In this chapter, the authors report their progress of using R* trees to improve well-known K-means and hierarchical clustering methods. Based on R*-Tree’s feature of indexing Minimum Bounding Box (MBB) according to spatial proximity, the authors extend R*-Tree’s application to cluster analysis containing image data. Two improved algorithms, KMeans-R and Hierarchy-R, are proposed. Experiments have shown that KMeans-R and Hierarchy-R have achieved better clustering quality.

scholarly journals Machine-learning identification of asteroid groups

2019 ◽  
Vol 488 (1) ◽  
pp. 1377-1386 ◽  
Author(s):  
V Carruba ◽  
S Aljbaae ◽  
A Lucchini
Keyword(s):  
Machine Learning ◽  
Hierarchical Clustering ◽  
Clustering Algorithms ◽  
Family Members ◽  
Parent Body ◽  
Clustering Methods ◽  
New Family ◽  
Hierarchical Clustering Methods ◽  
Rotational Failure ◽  
Asteroid Families

ABSTRACT Asteroid families are groups of asteroids that share a common origin. They can be the outcome of a collision or be the result of the rotational failure of a parent body or its satellites. Collisional asteroid families have been identified for several decades using hierarchical clustering methods (HCMs) in proper elements domains. In this method, the distance of an asteroid from a reference body is computed, and, if it is less than a critical value, the asteroid is added to the family list. The process is then repeated with the new object as a reference, until no new family members are found. Recently, new machine-learning clustering algorithms have been introduced for the purpose of cluster classification. Here, we apply supervised-learning hierarchical clustering algorithms for the purpose of asteroid families identification. The accuracy, precision, and recall values of results obtained with the new method, when compared with classical HCM, show that this approach is able to found family members with an accuracy above 89.5 per cent, and that all asteroid previously identified as family members by traditional methods are consistently retrieved. Values of the areas under the curve coefficients below Receiver Operating Characteristic curves are also optimal, with values consistently above 85 per cent. Overall, we identify 6 new families and 13 new clumps in regions where the method can be applied that appear to be consistent and homogeneous in terms of physical and taxonomic properties. Machine-learning clustering algorithms can, therefore, be very efficient and fast tools for the problem of asteroid family identification.

scholarly journals Vaginal microbiome topic modeling of laboring Ugandan women with and without fever

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Mercedeh Movassagh ◽  
Lisa M. Bebell ◽  
Kathy Burgoine ◽  
Christine Hehnly ◽  
Lijun Zhang ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Sub Saharan Africa ◽  
Vaginal Microbiome ◽  
Clustering Methods ◽  
Clinical Variables ◽  
Hypervariable Regions ◽  
Microbiome Research ◽  
Hierarchical Clustering Methods ◽  
Sub Saharan ◽  
The Impact

AbstractThe composition of the maternal vaginal microbiome influences the duration of pregnancy, onset of labor, and even neonatal outcomes. Maternal microbiome research in sub-Saharan Africa has focused on non-pregnant and postpartum composition of the vaginal microbiome. Here we aimed to illustrate the relationship between the vaginal microbiome of 99 laboring Ugandan women and intrapartum fever using routine microbiology and 16S ribosomal RNA gene sequencing from two hypervariable regions (V1–V2 and V3–V4). To describe the vaginal microbes associated with vaginal microbial communities, we pursued two approaches: hierarchical clustering methods and a novel Grades of Membership (GoM) modeling approach for vaginal microbiome characterization. Leveraging GoM models, we created a basis composed of a preassigned number of microbial topics whose linear combination optimally represents each patient yielding more comprehensive associations and characterization between maternal clinical features and the microbial communities. Using a random forest model, we showed that by including microbial topic models we improved upon clinical variables to predict maternal fever. Overall, we found a higher prevalence of Granulicatella, Streptococcus, Fusobacterium, Anaerococcus, Sneathia, Clostridium, Gemella, Mobiluncus, and Veillonella genera in febrile mothers, and higher prevalence of Lactobacillus genera (in particular L. crispatus and L. jensenii), Acinobacter, Aerococcus, and Prevotella species in afebrile mothers. By including clinical variables with microbial topics in this model, we observed young maternal age, fever reported earlier in the pregnancy, longer labor duration, and microbial communities with reduced Lactobacillus diversity were associated with intrapartum fever. These results better defined relationships between the presence or absence of intrapartum fever, demographics, peripartum course, and vaginal microbial topics, and expanded our understanding of the impact of the microbiome on maternal and potentially neonatal outcome risk.

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