A segmentation algorithm for the delineation of agricultural management zones
Computers and Electronics in AgriculturePublished 18 November 2009
Moacir Pedroso, James A. Taylor, Bruno Tisseyre, Brigitte Charnomordic, Serge Guillaume
Citations100
SJR quartileQ1
SJR score1.83
SNIP2.35
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
The algorithm presented is a first generation segmentation algorithm and several aspects still need to be developed, in particular methods for eliminating edge effects and converting management zones into management (treatment) classes.
Abstract
International audience
Keywords
Agricultural and Biological SciencesEnvironmental Science
Agronomy JournalManagement Zone Analyst (MZA)
267 Citations2004Jon J. Fridgen, Newell R. Kitchen +4 more
Concepts and theory behind MZA, a fuzzy c-means unsupervised clustering algorithm that assigns field information into like classes, or potential management zones, are presented as are the sequential steps of the program.
HAL (Le Centre pour la Communication Scientifique Directe)Analyse d'images : Filtrage et segmentation
247 Citations1995Philippe Bolon, Jean‐Marc Chassery +8 more
Agronomy JournalEstablishing Management Classes for Broadacre Agricultural Production
188 Citations2007James A. Taylor, Alex B. McBratney +1 more
The protocol has been developed for non-irrigated broadacre (>20 ha) Australian grain production systems but is readily transferable to other production systems with suitable local agronomic knowledge.
Mathematical GeologyA geostatistical basis for spatial weighting in multivariate classification
168 Citations1989Margaret A. Oliver, R. Webster
Annals of Applied BiologyClassification as a first step in the interpretation of temporal and spatial variation of crop yield
116 Citations1997R. M. Lark, J. V. Stafford
Automated pattern recognition by multivariate clustering is proposed as a tool for interpreting the temporal and spatial variation of crop yield, and a trial showed that some general patterns of season-to-season variation could be identified and related to soil variability.
Agricultural SystemsA preliminary approach to assessing the opportunity for site-specific crop management in a field, using yield monitor data
92 Citations2003M. Pringle, Alex B. McBratney +2 more
IEEE Transactions on Pattern Analysis and Machine IntelligenceAn Iterative Segmentation Method Based on a Contextual Color and Shape Criterion
88 Citations1984J.-M. Chassery, Catherine Garbay
An iterative segmentation method is presented and illustrated on specific examples by combining local and global properties according to a model of the image structure to evaluate adequacy of segmentation.
International Journal of Geographical Information SystemsForming spatially coherent regions by classification of multi-variate data: an example from the analysis of maps of crop yield
77 Citations1998R. M. Lark
A method is proposed for the generation, from multi-variate data, of classes with a spatially coherent distribution based on fuzzy clustering of the data, followed by spatially weighted averaging of the class memberships within a local neighbourhood.
Precision AgricultureA technical opportunity index based on mathematical morphology for site-specific management: an application to viticulture
64 Citations2008Bruno Tisseyre, Alex B. McBratney
A method that enables a farmer to decide whether or not the spatial variation of a field is suitable for a reliable variable-rate application and to determine if a particular threshold based on the within-field data is technically feasible with respect to the equipment for application is provided.
Agronomy JournalCreating Spatially Contiguous Yield Classes for Site‐Specific Management
59 Citations2003J. L. Ping, Achim Dobermann
Soil Use and ManagementIdentifying management zones in agricultural fields using spatially constrained classification of soil and ancillary data
57 Citations2007Z. L. Frogbrook, Margaret A. Oliver
Precision AgricultureManagement zone delineation using a modified watershed algorithm
40 Citations2008Pierre Roudier, Bruno Tisseyre +2 more
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 is proposed.
Soil and Tillage ResearchSome tools for parsimonious modelling and interpretation of within-field variation of soil and crop systems
31 Citations2001R. M. Lark
It is proposed in this paper that, in different guises, the principle of parsimony (using no more complex a model or representation of reality than absolutely necessary) is essential for agricultural research at the scale of the field or larger regions.
Precision AgricultureIs it economically feasible to harvest by management zone?
20 Citations2007Peter R. Tozer, Bindi Isbister
A management Opportunity Index for Precision Agriculture.
19 Citations2000Alex B. McBratney, B. M. Whelan +2 more
Management Zones for High Valued Crops in Greece
2 Citations2007Donald M. Davis, S. Fountas +3 more
HAL (Le Centre pour la Communication Scientifique Directe)Extraction de zones floues sur des cartes
2 Citations2006L. Lardon, Serge Guillaume +1 more
