Effects of magnetization on the complex modulus of kappa-carrageenan magnetic gels have been investigated. The magnetic gel was made of a natural polymer, kappa-carrageenan, and a ferrimagnetic particle, barium ferrite. The complex modulus was measured before and after magnetization of the gel by dynamic viscoelastic measurements with a compressional strain. The gels showed a giant reduction in the storage modulus of approximately 10(7) Pa and also in the loss modulus of approximately 10(6) Pa due to magnetization. The reduction increased with increasing volume fraction of ferrite, and it was nearly independent of the frequency. It was also found that the change in the modulus was nearly independent of the magnetization direction and irradiation time of the magnetic fields to the gel. The magnetic gels demonstrating the giant reduction in the dynamic modulus showed a large nonlinear viscoelastic response. It was observed that the magnetic gel was deformed slightly due to magnetization. The observed giant complex modulus reduction could be attributed to the nonlinear viscoelasticity and deformation caused by magnetization. Magnetism, nonlinear viscoelasticity, and effects of magnetization on the morphological and shape changes were discussed.

Recent advances in spectral imaging technology have enabled the development of models that estimate various crop parameters from spectral imagery data. We developed partial least square (PLS) models to predict fruit yield of Satsuma mandarin using airborne hyperspectral imagery obtained several months before harvesting. Hyperspectral images in the 72 visible and near-infrared (NIR) wavelengths (from 407 to 898 nm) were acquired over a citrus orchard during the early growing seasons of 2003, 2004 and 2005. The canopy features of individual trees were identified using pixel-based average spectral reflectance values for all 72 wavelengths from the acquired images. The acquired canopy features were then used as prediction variables to develop yield prediction models. These were developed using three techniques: (1) normalized difference vegetation index (NDVI), simple ratio (SR) and photochemical reflectance index (PRI), (2) conventional multiple linear regression (MLR) models, and (3) PLS regression models. As we intended to predict yield several months before the harvesting season (generally late December), the conventional techniques (vegetation indices and MLR) did not predict well. In contrast, PLS models gave successful predictions for the three years. These results confirmed the hypothesized correlation between canopy features and citrus yield. The successful forecasting of yields several months or even one year ahead of the harvest season is expected to contribute to planning harvest schedules, generating prescription maps for dealing with fluctuations of yield in specific trees, control measures, and management practices.

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The goal of this study is to develop an approach to determine the internal qualities in oil palm (Elaeis guineensis Jacq. var. tenera). Bunches and fruits belonging to 4 classes of ripeness (overripe, ripe, underripe and unripe) were used for this study. For these bunches, three of internal qualities as ripeness, oil content and free fatty acid content were examined. Since the estimation of internal qualities based on the overall data for a bunch was difficult, we focused on the average reflectance and the average relative reflectance values of fruits that were not concealed by fronds in bunch. By our approach, it was necessary to estimate the ripeness of the bunch before the oil content and free fatty acid content were determined. To classify ripeness of a bunch, the average relative reflectance values of bunches in different classes of ripeness were used and classified based on Euclidean distance. In addition, ratio of chlorophyll to carotenoids (R p ) was also used for estimating ripeness of a bunch. Then oil content (OC) and free fatty acid (FFA) content were predicted by calibration models corresponding to the class of ripeness. Correct estimation results in all classes of ripeness were obtained by both methods. The coefficients of determination (R 2 ) were 99.7% and 99.5% with a standard error of prediction (SEP) of 0.421 and 0.190 in the validation of oil content and free fatty acid models, respectively. For oil palm fruits, methods to estimate the ripeness of the fruits were developed. Ripeness estimation using the average relative reflectance values in lower part of the fruit was compared with ripeness estimation using the ratio of a not-pale greenish yellow area, a not-yellow area and a not-reddish orange area to the entire area of fruit. The correct estimation in all classes of ripeness was obtained by using the average relative reflectance at lower part of fruit while a correct ripeness estimation rate of 97.92% was gained by using ratio of area in fruit. Since the ripeness estimation using the ratio of the area of the fruits can be done automatically, it may provide more practically applicable for the assessment of fruit ripeness in the factory.