scholarly journals Management zones using fuzzy clustering based on spatial-temporal variability of soil and corn yield

2015 ◽  
Vol 35 (3) ◽  
pp. 470-483 ◽  
Author(s):  
Marcos S. Rodrigues ◽  
José E. Corá
Keyword(s):  
Temporal Variability ◽  
Clustering Algorithm ◽  
Spatial And Temporal Variability ◽  
Optimum Number ◽  
Corn Yield ◽  
Management Zones ◽  
Soil Attributes ◽  
Fuzzy C Means ◽  
Management Zone ◽  
Fuzzy C Means Clustering

Clustering soil and crop data can be used as a basis for the definition of management zones because the data are grouped into clusters based on the similar interaction of these variables. Therefore, the objective of this study was to identify management zones using fuzzy c-means clustering analysis based on the spatial and temporal variability of soil attributes and corn yield. The study site (18 by 250-m in size) was located in Jaboticabal, São Paulo/Brazil. Corn yield was measured in one hundred 4.5 by 10-m cells along four parallel transects (25 observations per transect) over five growing seasons between 2001 and 2010. Soil chemical and physical attributes were measured. SAS procedure MIXED was used to identify which variable(s) most influenced the spatial variability of corn yield over the five study years. Basis saturation (BS) was the variable that better related to corn yield, thus, semivariograms models were fitted for BS and corn yield and then, data values were krigged. Management Zone Analyst software was used to carry out the fuzzy c-means clustering algorithm. The optimum number of management zones can change over time, as well as the degree of agreement between the BS and corn yield management zone maps. Thus, it is very important take into account the temporal variability of crop yield and soil attributes to delineate management zones accurately.

scholarly journals Delineation of irrigation management zones in a Quartzipsamment of the Brazilian semiarid region

2016 ◽  
Vol 51 (9) ◽  
pp. 1283-1294 ◽  
Author(s):  
Henrique Oldoni ◽  
Luís Henrique Bassoi
Keyword(s):  
Soil Properties ◽  
Clustering Algorithm ◽  
Irrigation Management ◽  
Optimal Number ◽  
Semiarid Region ◽  
Management Zones ◽  
Distribution Maps ◽  
Management Zone ◽  
Brazilian Semiarid ◽  
Fuzzy C Means Clustering

Abstract The objective of this work was to delineate irrigation management zones using geostatistics and multivariate analysis in different combinations of physical and hydraulic soil properties, as well as to determine the optimal number of management zones in order to avoid overlaping. A field experiment was carried out in a Quartzipsamment, for two years, in an irrigated orchard of table grape, in the Senador Nilo Coelho Irrigation Scheme, in the municipality of Petrolina, in the state of Pernanbuco, Brazil. Soil samples were collected for the determination of soil physico-hydraulic properties. A portable meter was used to measure soil apparent electrical conductivity. Spatial distribution maps were generated using ordinary kriging. Management zones for five different combinations of soil properties were defined using the fuzzy c-means clustering algorithm, and two indexes were applied to determine the optimal number of management zones. Two combinations of soil properties can be used in the management zone planning in order to monitor soil moisture.

scholarly journals Productivity of soybean in management zones with application of different sowing densities

2018 ◽  
Vol 48 (12) ◽  
Author(s):  
Rafaela Greici da Motta Camicia ◽  
Marcio Furlan Maggi ◽  
Eduardo Godoy de Souza ◽  
Claudio Leones Bazzi ◽  
Evandro André Konopatzki ◽  
...  
Keyword(s):  
Economic Analysis ◽  
Clustering Algorithm ◽  
Descriptive Analysis ◽  
Seeding Density ◽  
Row Spacing ◽  
Seed Density ◽  
Management Zones ◽  
Fuzzy C Means ◽  
Productive Potential ◽  
Fuzzy C Means Clustering

ABSTRACT: The present study aimed to assess the efficiency of sowing at variable rates for soybean cultivation in two management zones (MZs) which were defined based on stable attributes and correlated with productivity using the Fuzzy C-means clustering algorithm and the kriging interpolation.Seeding was carried out in the 2015/2016 and 2017/2018 crops with a variation of 20% of seeds and crop row spacing of 0.70m. In each MZ, 8 plots with higher and lower seed density were established. Productivity was measured using a harvest monitor connected to a harvester. Data were filtered and submitted to descriptive analysis. Productivity maps were generated using the inverse square distance interpolation for each seeding density. In the MZ with the highest productive potential (MZ 1), the productivity was 3.39 and 3.18t ha-1, and in the MZ with the lowest productive potential (MZ 2) the productivity was 3.30 and 3.11t ha-1 for the years 2016 and 2018, respectively. Interpolation estimated higher productivity with the application of 15 plants m-1. Based on the economic analysis, it is suggested in this study the application of 214,000 plants ha-1 in both MZs.

scholarly journals Mapping of yield, economic return, soil electrical conductivity, and management zones of irrigated corn for silage

2018 ◽  
Vol 53 (12) ◽  
pp. 1289-1298 ◽  
Author(s):  
Alberto Carlos de Campos Bernardi ◽  
Oscar Tupy ◽  
Karoline Eduarda Lima Santos ◽  
Giulia Guillen Mazzuco ◽  
Giovana Maranhão Bettiol ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Spatial Variability ◽  
Precision Agriculture ◽  
Temporal Variability ◽  
Production Cost ◽  
Spatial And Temporal Variability ◽  
Corn Yield ◽  
Soil Electrical Conductivity ◽  
Economic Return ◽  
Management Zones

Abstract: The objective of this work was to evaluate the spatial and temporal variability of the dry matter yield of irrigated corn for silage, as well as its economic return. The study was conducted in an irrigated silage corn field of 18.9 ha in the municipality of São Carlos, in the state of São Paulo, Brazil. The spatial variability of the yield of three crop seasons, normalized yield indexes, production cost, profit, and soil electrical conductivity (EC) were modeled using semivariograms. Yield maps were obtained by kriging, and management zones were mapped based on average yield, normalized index, and EC. The results showed a structured spatial variability of corn yield, production cost, profit, and soil EC within the irrigated area. The adopted precision agriculture tools were useful to indicate zones of higher yield and economic return. The sequences of yield maps and the analysis of spatial and temporal variability allow the definition of management zones, and soil EC is positively related to corn yield.

scholarly journals Automatic measurement of traditional Chinese costume from its silhouette through Fuzzy c-means clustering method

2020 ◽  
Vol 15 ◽  
pp. 155892502097832
Author(s):  
Jiaqin Zhang ◽  
Jingan Wang ◽  
Le Xing ◽  
Hui’e Liang
Keyword(s):  
Industrial Application ◽  
Clustering Algorithm ◽  
Color Space ◽  
Automatic Measurement ◽  
Feature Point ◽  
Feature Points ◽  
Point Location ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Environmental Robustness

As the precious cultural heritage of the Chinese nation, traditional costumes are in urgent need of scientific research and protection. In particular, there are scanty studies on costume silhouettes, due to the reasons of the need for cultural relic protection, and the strong subjectivity of manual measurement, which limit the accuracy of quantitative research. This paper presents an automatic measurement method for traditional Chinese costume dimensions based on fuzzy C-means clustering and silhouette feature point location. The method is consisted of six steps: (1) costume image acquisition; (2) costume image preprocessing; (3) color space transformation; (4) object clustering segmentation; (5) costume silhouette feature point location; and (6) costume measurement. First, the relative total variation model was used to obtain the environmental robustness and costume color adaptability. Second, the FCM clustering algorithm was used to implement image segmentation to extract the outer silhouette of the costume. Finally, automatic measurement of costume silhouette was achieved by locating its feature points. The experimental results demonstrated that the proposed method could effectively segment the outer silhouette of a costume image and locate the feature points of the silhouette. The measurement accuracy could meet the requirements of industrial application, thus providing the dual value of costume culture research and industrial application.

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