Data Mining in Agriculture

Abstract

Aims and ScopeOptimization has been expanding in all directions at an astonishing rate during the last few decades. New algorithmic and theoretical techniques have been developed, the diffusion into other disciplines has proceeded at a rapid pace, and our knowledge of all aspects of the field has grown even more profound. At the same time, one of the most striking trends in optimization is the constantly increasing emphasis on the interdisciplinary nature of the field. Optimization has been a basic tool in all areas of applied mathematics, engineering, medicine, economics and other sciences.The Springer Optimization and Its Applications series publishes undergraduate and graduate textbooks, monographs and state-of-the-art expository works that focus on algorithms for solving optimization problems and also study applications involving such problems. Some of the topics covered include nonlinear optimization (convex and nonconvex), network flow problems, stochastic optimization, optimal control, discrete optimization, multiobjective programming, description of software packages, approximation techniques and heuristic approaches. VOLUME 34 . This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights.Printed on acid-free paper PrefaceData mining is the process of finding useful patterns or correlations among data. These patterns, associations, or relationships between data can provide information about a specific problem being studied, and information can then be used for improving the knowledge on the problem. Data mining techniques are widely used in various sectors of the economy. Initially they were used by large companies to analyze consumer data from different perspectives. Data was then analyzed and useful information was extracted with the goal of increasing profitability. The idea of using information hidden in relationships among data inspired researchers in agricultural fields to apply these techniques for predicting future trends of agricultural processes. For example, data collected during wine fermentation can be used to predict the outcome of the fermentation while still in the early days of this process. In the same way, soil water parameters for a certain soil type can be estimated knowing the behavior of similar soil types.The principles used by some data mining techniques are not new. In ancient Rome, the famous orator Cicero used to say pares cum paribus facill...

Data Mining Techniques for the performance Analysis of a Learning Model A Case Study

“…It has been used to automatically discover information in large databases [2]. Good amount of work with regard to DM applications in various fields like medicine, bioinformatics, agriculture, meteorology and other fields are available [3] [30]. Here they have made a detailed study of neural network approach for the performance analysis of the learning model.…”

A Geo-Visual Analytics Approach to Biological Shepherding: Modelling Animal Movements and Impacts

“…A feature of data mining is the automatic search for hidden patterns in large multi-dimensional databases (Kantardzie, 2003). A variety of data mining techniques can be XXII ISPRS Congress, 25 August -01 September 2012, Melbourne, Australia used in combination with the spatial data to quantify processed information --in a process of guided discovery in the pursuit of new knowledge (Mucherino et al, 2009). This combination of advanced graphics and statistics may include cluster analysis, time-series analysis and digital image processing (including thresholding, segmentation and classification).…”

“…The data set is then stored as a relational database and processed using visual analytics and data mining. Agent-based models of sheep can be combined with simulation techniques for predictive modelling (Mucherino et al, 2009 …”

“…m i = 1 (for i = 1, …, n), the position is referred to as the centroid of the set of coordinates (Johnson, 2007). In the case of spatially-variant excreta for sheep or cows, geo-visualisation of the data set (x, y, z, t), at a given point in time provides a global picture revealing clustering effects or trends not readily apparent in localised numerical analysis (Mucherino et al, 2009). …”

“…Novel visual analytics and data mining techniques are useful for detection of trends, clusters and behavioural patterns in animal interaction and movement across a paddock. Multi-agent simulation using an agent-based model (ABM) is possible combining autonomous agents, decision-making rules, an interaction framework and data from the surrounding environment as described schematically in the Appendix (see also Brown et al, 2005;Mucherino et al, 2009). An agent-based model of sheep behaviour can be calibrated using visual analytic techniques in order to predict behaviour under different conditions, e.g.…”