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Understanding the impact of irrigation and fertilizer on rabbiteye blueberry (Vaccinium virgatum) physiology is necessary for its precision planting. Here, we applied varied irrigation and fertilizer under completely randomized experimental design to see its impact on the physiological characteristics and bush growth of rabbiteye blueberries. A comprehensive evaluation of the membership function was used to establish the best water–fertilizer coupling regimes. Rabbiteye blueberry enhanced the net photosynthetic rate, stomatal conductance and transpiration rate of leaf and improved its photosynthetic capacity at maximum level of irrigation water and fertilizer application (F3W4). The high fertilizer–medium water treatment (F3W3) increased leaf-soluble protein contents. The medium fertilizer–medium water treatment (F2W3, F2W2) increased leaf- soluble sugar, superoxide dismutase, and chlorophyll contents; decreased the malondialdehyde content; and enhanced leaf resistance and metabolism. It also promoted the growth of flower buds and new shoots. Combined membership function and cluster analyses revealed that the optimal water and fertilizer conditions for promoting rabbiteye blueberry plant growth were the medium fertilizer–medium water [(NH4)2SO4:Ca(H2PO4)2:K2SO4 at 59:10:20 g plant-1; 2.5 L water plant-1], medium fertilizer–medium-high water [(NH4)2SO4:Ca(H2PO4)2:K2SO4 at 59:10:20 g plant-1; 3.75 L water plant-1], and high fertilizer–medium-high water [(NH4)2SO4:Ca(H2PO4)2:K2SO4 at 118:20:40 g plant-1; 3.75 L water plant-1] treatments. The findings of this study could be used in improving the precision and efficacy of rabbiteye blueberry planting in Guizhou, China. Such an approach can increase the productivity and profitability for local fruit farmers.

Under dynamic conditions, the centroiding accuracy of the motion-blurred star image decreases and the number of identified stars reduces, which leads to the degradation of the attitude accuracy of the star sensor. To improve the attitude accuracy, a region-confined restoration method, which concentrates on the noise removal and signal to noise ratio (SNR) improvement of the motion-blurred star images, is proposed for the star sensor under dynamic conditions. A multi-seed-region growing technique with the kinematic recursive model for star image motion is given to find the star image regions and to remove the noise. Subsequently, a restoration strategy is employed in the extracted regions, taking the time consumption and SNR improvement into consideration simultaneously. Simulation results indicate that the region-confined restoration method is effective in removing noise and improving the centroiding accuracy. The identification rate and the average number of identified stars in the experiments verify the advantages of the region-confined restoration method.

The attitude accuracy of a star sensor decreases rapidly when star images become motion-blurred under dynamic conditions. Existing techniques concentrate on a single frame of star images to solve this problem and improvements are obtained to a certain extent. An attitude-correlated frames (ACF) approach, which concentrates on the features of the attitude transforms of the adjacent star image frames, is proposed to improve upon the existing techniques. The attitude transforms between different star image frames are measured by the strap-down gyro unit precisely. With the ACF method, a much larger star image frame is obtained through the combination of adjacent frames. As a result, the degradation of attitude accuracy caused by motion-blurring are compensated for. The improvement of the attitude accuracy is approximately proportional to the square root of the number of correlated star image frames. Simulations and experimental results indicate that the ACF approach is effective in removing random noises and improving the attitude determination accuracy of the star sensor under highly dynamic conditions.

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