Identification of agricultural Mediterranean soils using mid-infrared photoacoustic spectroscopy

This study investigated the use of Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS) for rapid identification of agricultural soil samples. The PAS spectra of 166 air-dried samples belonging to five Mediterranean soil types most common in Israeli agriculture were recorded. The various soil types exhibited distinctive mid-IR bands, especially around the 2900–3700 cm^? 1, 2500–2550 cm^? 1, 1800–2050 cm^? 1 and 900–1600 cm^? 1 regions. Following smoothing and normalization of the spectra, principal component analysis (PCA) was used to reduce the dimensionality of the data and the PCA scores were used in classifiers based either on linear discriminant analysis or on probabilistic neural networks. The two classifiers based on four PCA scores yielded very similar results and correctly identified over 96% of the 77 validation samples. Comparison with the attenuated total reflectance (ATR) spectra of similar soils used in a previous study showed that the PAS spectra contained more information than the ATR ones, both in terms of the number of soil-specific bands and in terms of the bands' distinctiveness. The results clearly show that FTIR-PAS can be used for rapid soil identification and the abundance of information in the PAS spectra indicates that this technique could be further developed to assess important soil features.     

A procedure for estimating the number of green mature apples in night-time orchard images using light distribution and its application to yield estimation

A procedure for estimating the number of mature apples in orchard images captured at night-time with artificial illumination was developed and its potential for estimating yield was investigated. The procedure was tested using four datasets totaling more than 800 images taken with cameras positioned at three heights. The procedure for detecting apples was based on the observation that the light distribution on apples follows a simple pattern in which the perceived light intensity decreases with the distance from a local maximum due to specular reflection. Accordingly, apple detection was achieved by detecting concentric circles (or parts of circles) in binary images obtained via threshold operations. For each dataset, after calibration of the procedure using 12 images, the estimates of the number of apples were within a few percent of the number of apples counted by visual inspection. Yield estimations were obtained via multi-linear models that used between two and six images per tree. The results obtained using all three cameras were only slightly better than those obtained using only two cameras. Using images from only one side of the tree did not worsen the results significantly. Overall, the yield estimated by the best models was within \(\pm\)10 % of the actual yield. However, the standard deviation of the yield estimation errors corresponded to ~26–37 % of the average tree yield, indicating that improvements are still needed in order to achieve accurate yield estimation at the single-tree level.     

A comparison between spatial clustering models for determining N-fertilization management zones in orchards

Precision Agriculture - Site-specific agricultural management (SSM) relies on identifying within-field spatial variability and is used for variable rate input of resources. Precision agricultural...     

A Magnetic Signature at Io: Initial Report from the Galileo Magnetometer

During the inbound pass of the Galileo spacecraft, the magnetometer acquired 1 minute averaged measurements of the magnetic field along the trajectory as the spacecraft flew by Io. A field decrease, of nearly 40 percent of the background jovian field at closest approach to Io, was recorded. Plasma sources alone appear incapable of generating perturbations as large as those observed and an induced source for the observed moment implies an amount of free iron in the mantle much greater than expected. On the other hand, an intrinsic magnetic field of amplitude consistent with dynamo action at Io would explain the observations. It seems plausible that Io, like Earth and Mercury, is a magnetized solid planet.     

Machine vision for counting fruit on mango tree canopies

Machine vision technologies hold the promise of enabling rapid and accurate fruit crop yield predictions in the field. The key to fulfilling this promise is accurate segmentation and detection of fruit in images of tree canopies. This paper proposes two new methods for automated counting of fruit in images of mango tree canopies, one using texture-based dense segmentation and one using shape-based fruit detection, and compares the use of these methods relative to existing techniques:—(i) a method based on K-nearest neighbour pixel classification and contour segmentation, and (ii) a method based on super-pixel over-segmentation and classification using support vector machines. The robustness of each algorithm was tested on multiple sets of images of mango trees acquired over a period of 3 years. These image sets were acquired under varying conditions (light and exposure), distance to the tree, average number of fruit on the tree, orchard and season. For images collected under the same conditions as the calibration images, estimated fruit numbers were within 16 % of actual fruit numbers, and the F1 measure of detection performance was above 0.68 for these methods. Results were poorer when models were used for estimating fruit numbers in trees of different canopy shape and when different imaging conditions were used. For fruit-background segmentation, K-nearest neighbour pixel classification based on colour and smoothness or pixel classification based on super-pixel over-segmentation, clustering of dense scale invariant feature transform features into visual words and bag-of-visual-word super-pixel classification using support vector machines was more effective than simple contrast and colour based segmentation. Pixel classification was best followed by fruit detection using an elliptical shape model or blob detection using colour filtering and morphological image processing techniques. Method results were also compared using precision–recall plots. Imaging at night under artificial illumination with careful attention to maintaining constant illumination conditions is highly recommended.