Data Mining in Agriculture (Springer Optimization and Its Applications)

Data Mining in Agriculture (Springer Optimization and Its Applications)

Antonio Mucherino

Language: English

Pages: 274

ISBN: 0387886141

Format: PDF / Kindle (mobi) / ePub


Data Mining in Agriculture represents a comprehensive effort to provide graduate students and researchers with an analytical text on data mining techniques applied to agriculture and environmental related fields. This book presents both theoretical and practical insights with a focus on presenting the context of each data mining technique rather intuitively with ample concrete examples represented graphically and with algorithms written in MATLABĀ®.

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100]. For instance, the optimization problem arising when training support vector machines has a convex quadratic function and linear constraints (see Chapter 6 for details). Methods for solving these particular kinds of problems include the active set methods and the interior point methods [33, 100]. However, there are methods tailored to the support vector machines for solving such quadratic optimization problems, and hence the general methods are often not used. If the objective function and

The chicken breast lost redness during time: in particular, its redness decreases while its yellowness increases. The meats deboned at earlier postmortem time require more force to shear and therefore they are less tender. The attributes evaluated by the panelists also decrease gradually. The two sensory flavor attributes (cardboardy and wet feathers), the seven sensory texture attributes (springiness, cohesiveness, hardness, moisture release, particle size, bolus size, and chewiness), and the

possibility that T1 and T2 are both on agriculture. In such a case, the two documents are similar and their relative distance should be smaller. For instance, T1 might be a short text: shorter texts have fewer words in general, and in particular they may contain less occurrences of the word agriculture. For this reason, this distance is not a good measure of text similarities. In general in text mining, the cosine similarity function is used. The samples are normalized for overcoming the problem

In [203] watercores in apples have been detected with an accuracy of more than 90% by using still X-ray images. In this approach, apples have been scanned by X-ray and successively sliced and photographed (see Figure 5.6). The obtained images, both normal and X-ray images, have then been used to characterize them as defective or not. In this phase, both kinds of images are inspected and evaluated by human experts. In order to create an automatic classifier, computational procedures are needed for

MATLAB function plot. . . . . . . . . . . . . . . . . . . 225 The sine and cosine functions drawn with MATLAB. . . . . . . . . . . . . 227 The function fun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228 The graphic of the MATLAB function fun. . . . . . . . . . . . . . . . . . . . . . 229 B.1 B.2 B.3 B.4 B.5 B.6 B.7 B.8 B.9 B.10 The function hmeans. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232 The prototypes of the

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