Spatial variability in a crop field creates a need for precision agriculture. Economical and rapid means of identifying spatial variability is obtained through the use of geotechnology (remotely sensed images of the crop field, image processing, GIS modeling approach, and GPS usage) and data mining techniques for model development. Higher-end image processing techniques are followed to establish more precision. The goal of this paper was to investigate the strength of key spectral vegetation indices for agricultural crop yield prediction using neural network techniques. Four widely used spectral indices were investigated in a study of irrigated corn crop yields in the Oakes Irrigation Test Area research site of North Dakota, USA. These indices were: (a) red and near-infrared (NIR) based normalized difference vegetation index (NDVI), (b) green and NIR based green vegetation index (GVI), (c) red and NIR based soil adjusted vegetation index (SAVI), and (d) red and NIR based perpendicular vegetation index (PVI). These four indices were investigated for corn yield during 3 years (1998, 1999, and 2001) and for the pooled data of these 3 years. Initially, Back-propagation Neural Network (BPNN) models were developed, including 16 models (4 indices * 4 years including the data from the pooled years) to test for the efficiency determination of those four vegetation indices in corn crop yield prediction. The corn yield was best predicted using BPNN models that used the means and standard deviations of PVI grid images. In all three years, it provided higher prediction accuracies, OPEN ACCESSRemote Sensing 2010, 2 674 coefficient of determination (r 2 ), and lower standard error of prediction than the models involving GVI, NDVI, and SAVI image information. The GVI, NDVI, and SAVI models for all three years provided average testing prediction accuracies of 24.26% to 94.85%, 19.36% to 95.04%, and 19.24% to 95.04%, respectively while the PVI models for all three years provided average testing prediction accuracies of 83.50% to 96.04%. The PVI pool model provided better average testing prediction accuracy of 94% with respect to other vegetation models, for which it ranged from 89-93%. Similarly, the PVI pool model provided coefficient of determination (r 2 ) value of 0.45 as compared to 0.31-0.37 for other index models. Log 10 data transformation technique was used to enhance the prediction ability of the PVI models of years 1998, 1999, and 2001 as it was chosen as the preferred index. Another model (Transformed PVI (Pool)) was developed using the log 10 transformed PVI image information to show its global application. The transformed PVI models provided average corn yield prediction accuracies of 90%, 97%, and 98% for years 1998, 1999, and 2001, respectively. The pool PVI transformed model provided as average testing accuracy of 93% along with r 2 value of 0.72 and standard error of prediction of 0.05 t/ha.

Solid-phase microextraction (SPME) is a relatively new sampling technique wherein sample extraction and pre-concentration could be achieved in a single step. The handling of an SPME device is simple, and the analysis of volatiles could be easy. However, the process becomes complex while analyzing sample matrices of heterogeneous nature. The complexity also increases depending upon the nature of compounds to be extracted. Careful selection and optimization of extraction parameters like fiber coating selection, extraction time, agitation, addition of salt, and extraction temperature have to be undertaken to improve the sensitivity and the reproducibility of this method. This paper reviews the principles associated with SPME technique from a general application viewpoint. Also, a comprehensive review of prior research related to characterization of food quality has been reported. SPME-related solutions for environmental applications have also been analyzed to be applied for new food-related applications.

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Oat (Avena sativa L.) kernel size uniformity is important to the oat milling industry because oat‐processing mills separate oats according to size to optimize dehulling efficiency. In this study, we compared two different approaches for analyzing oat kernel size uniformity, namely the sequential sieving of oat samples with a gradient of slotted sieve sizes and digital image analysis. Image analysis of size fractions provided evidence that sieving separated oat kernels according to their depth, whereas, digital image analysis measured kernel length and width, and derived a measure of the area of the oat kernel image. Samples identified by sieving with superior uniformity were those with greater proportions of large kernels. Histograms of oat kernel sizes derived from digital image analysis suggested oat kernel sizes were (within a genotype and location) composed of bimodal populations. A new statistical analysis allowed for the derivation of means and variances of each of these subpopulations, the numerical balance between the two subpopulations, and the extent of bimodality. Oat samples with lower levels of bimodality tended to be of higher test weight and groat percentage and thus, of better milling quality. Both methods appear satisfactory for evaluating oat kernel size uniformity, although the sequential sieving method is likely to be more useful to breeding programs because of its relative technical ease and simplicity.

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