Delineation of site-specific management zones using fuzzy clustering analysis in a coastal saline land

Li, Yan; Shi, Zhou; Li, Feng; Li, Hongyi

doi:10.1016/j.compag.2007.01.013

Cited by 181 publications

(101 citation statements)

References 16 publications

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“…The knowledge, laws and rules are implied in them, unclear in advance, potentially useful and spatial or non-spatial which ultimately easy to understand [3]. The purpose of spatial data mining is that help people to improve the accuracy and reliability of the decision-making, and enable people to maximize the efficient use of data.…”

Section: The Integrated Application Of Gis and Technology Of Spatial mentioning

confidence: 99%

Visual Space Research and Application of the Data Mining in Soil Fertility Evaluation

Chen

2013

Computer and Computing Technologies in Agriculture VI

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Abstract. China is a populous agricultural country, the arable land is the most basic resources which is needed for agricultural production, the quality of arable land has a direct impact on the entire agricultural production and development ,so to carry out the Evaluation of Farmland is a very important foundation work which is advantage to know china's arable land resources and to improve the efficiency of use for arable land and promote the development of modern agricultural , and which is helpful to further identify china fertility status of cultivated soils, the distribution of nutrients, soil fertility and agricultural land degradation, etc., so as to achieve the realization of agricultural modernization and to improve the comprehensive capacity of agricultural production. This paper use visualization of spatial data mining technology which is used to research and apply on soil fertility evaluation, which is put the technology of spatial data mining and visualization technology, GIS, technology tightly integrated, that makes a research to the elements of soil fertility, yield and other data mining, and achieve the application of visualization of spatial data mining techniques in the evaluation of soil fertility, this application is more conducive to promotion and application of precision agriculture for variable rate fertilization, variable spraying, at the same time it provides real-time, accurate decision making basis for agricultural production management department, to promote the transformation of traditional agriculture to modern agriculture, which has important theoretical significance and practical value.Keywords: Soil fertility evaluation, GIS, spatial data mining. IntroductionIn resent years, with the development of visualization techniques and data mining technology, in order to solve the visible and interactive problem of the data mining process, under the combination of the two information visualization techniques and data mining technology to form a visual data mining ,Visual data mining technology make full use of the advantages of data mining and visualization technology to solve the problem of the "black box" data mining operation, and gradually being valued by the people, which is an important aspect of data mining research and has a very high research value.to carry out the evaluation of cultivated land which is use spatial data mining technology and agricultural technology can reduce the manpower, material and financial resources of arable land evaluation work which is required，and it can improve the efficiency of the evaluation, but also can improve the accuracy of the evaluation results, and conducive to the evaluation results summary, management, updates, cartographic output and promote the application. So as to identify the production capacity of China's cultivated land base, soil fertility status, to achieve the purpose of a strong impetus to the modernization of agriculture and sustainable development. Soil Spatial Data AcquisitionIn support of GIS and related technologies, we can...

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Section: The Integrated Application Of Gis and Technology Of Spatial mentioning

confidence: 99%

Visual Space Research and Application of the Data Mining in Soil Fertility Evaluation

Chen

2013

Computer and Computing Technologies in Agriculture VI

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“…As described by Yao et al (2014), MZs have many other applications besides representing areas of the same productive potential, optimizing soil sampling grid, and reducing the number of tests required for development of nutrient application maps and fertilizers (LI et al, 2007). Such a methodology also allows conventional agricultural equipment to be used, since the application is constant within each zone and varies only between areas.…”

Section: Introductionmentioning

confidence: 99%

“…In the process of generating MZs, clustering methods have been widely used (FRAISSE et al, 2001;LI et al, 2007;REYNIERS et al, 2006;SCHENATTO et al, 2016;TAYLOR et al, 2003). The algorithm Fuzzy C-Means (BAZZI et al, 2013;LI et al, 2007;MORARI et al, 2009;MILNE et al, 2012;XIN-ZHONG et al, 2009) is a clustering method based on the fuzzy logic, defined by Zadeh (1965), which matches uncertainties associated with class and association boundaries (DOBERMANN et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

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Use of the farmer’s experience variable in the generation of management zones

Schenatto

Souza

Bazzi

et al. 2017

SCA

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In the spatial variability management of fields, the approach based on management zones (MZs) divides the area into sub-regions, which have spatially homogeneous topography and soil conditions. Such MZs should lead to the same potential yields. Farmers understand which areas of a field have high and low yields, and use of this knowledge may allow the identification of MZs in a field based on production history. The objective of the present study was to evaluate the application of farmer's experience to determine MZs. The study was conducted in three agricultural fields located in the west of the Paraná State in Brazil, and the MZs were generated considering three cases: a) without the use of the farmer's experience variable; b) with the variable of farmer's experience and the stable soil properties selected at the variable selection stage; and c) only with the farmer's experience variable. The generated MZs were evaluated using the Variance Reduction (VR) index, Fuzziness Performance Index (FPI), Modified Partition Entropy (MPE), Smooth Index (SI), and Analysis of Variance (ANOVA). The study showed that the use of farmer's experience to set MZs could be an efficient and simple tool, that it could reduce costs for the processes of setting MZs, compared to the traditional method of using stable soil variables and relief. Key words: Precision agriculture. Clustering. Farmer feeling. Management units. ResumoNo gerenciamento da variabilidade espacial das lavouras a abordagem baseada em zonas de manejo (ZMs) divide o talhão em sub-regiões, que apresentam topografia e condições do solo espacialmente homogêneas, de tal forma que tais ZMs devem conduzir aos mesmos resultados em potencial de rendimento das culturas. Os produtores têm experiência de quais áreas de um talhão apresentam altas e baixas produtividades e fazer uso dessa base de conhecimento pode permitir a identificação de ZMs em um campo com base no histórico de produção. O objetivo desse trabalho foi avaliar a eficiência de utilização da experiência do produtor na definição de ZMs. A pesquisa foi realizada em três áreas agrícolas localizadas na região Oeste do estado do Paraná/ Brasil e as ZMs foram geradas considerando

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“…Several authors proposed to use the k-means algorithm Whelan and McBratney, 2003;Hornung et al, 2006). Finally, the fuzzy c-means algorithm is now widely applied (Lark and Stafford, 1997;Fridgen et al, 2000;Vrindts et al, 2005;Li et al, 2007). The expansion of the latter approach has been made easier for researchers with the release of user-friendly software like Management Zone Analyst (Fridgen et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Management zone delineation using a modified watershed algorithm

Roudier¹,

Tisseyre

Poilvé³

et al. 2008

Precision Agric

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Site-specific management (SSM) is a common way to manage within-field variability. This concept divides fields into site-specific management zones (SSMZ) according to one or several soil or crop characteristics. This paper proposes an original methodology for SSMZ delineation which is able to manage different kinds of crop and/or soil images using a powerful segmentation tool: the watershed algorithm. This image analysis algorithm was adapted to the specific constraints of precision agriculture. The algorithm was tested on high-resolution bio-physical images of a set of fields in France.

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Delineation of site-specific management zones using fuzzy clustering analysis in a coastal saline land

Cited by 181 publications

References 16 publications

Visual Space Research and Application of the Data Mining in Soil Fertility Evaluation

Visual Space Research and Application of the Data Mining in Soil Fertility Evaluation

Use of the farmer’s experience variable in the generation of management zones

Management zone delineation using a modified watershed algorithm

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