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In this paper an efficient indexing and retrieval technique is proposed for identification of plant images. The plant images have been retrieved as herbs shrubs and trees based on color and texture features. Plant images have rich content of information such as leaves, bark and stem etc. This content can be extracted as various content features. Plant image database often represent the image objects as highdimensional feature vectors and access them via the feature vectors and similarity measures. Hence in order to retrieve plant images an indexing method is proposed based image retrieval to improve the retrieval performance. In this work color and texture features are extracted for characterizing leafy mass and stem images of plants. There are 20 images of five plant species, amounting to total of 100 images, herbs, shrubs, and trees. Hence, totally there are 300 images of leafy mass and bark of 15 plant species. All the images are captured in natural environment using a digital camera. In the preprocessing stage, the images of leafy mass and bark with complex background information and noise is filtered out by using Unsharp filtering method. In the segmentation method 2D-OTSU threshold based segmentation technique is used to separate the object from the background For Ex: small leaves surrounded by bright sky, small openings among dense canopy, and mixed pixels. For feature extraction the Modified Color Co-occurrence Matrix (MCCM) and Gabour Filter is used to retrieve the color and texture of the images from the database. Usually the retrieval performance of an image retrieval system is greatly influenced by different similarities or distance measures. We have used Euclidean distance measures for similarity matching. Finally for retrieval of the images from the database Fast Indexing scheme for CBIR method is used. Then multi-dimensional indexing technique is employed using tree structures, R* tree. The effective indexing and fast searching of images on bases of visual features pose a significant issue in CBIR. We have used R*-Tree structure to achieve better performance and efficiency. A graph is plotted to compare the retrieval result of both technique. Indexing Technique gives better retrieval rate.

The particulate pollution in the atmosphere can be assessed using various methods like measuring aerosol mass concentration, aerosol optical depth, number density of airborne particles, etc. In the present study, measurements on aerosol optical depth using sunphotometer, aerosol mass concentration in different size ranges using QCM particle analyzer and black carbon aerosol using Aethalometer have been carried out during January to May, 2003, to characterize the atmospheric turbidity over Hyderabad. Spectral variation of aerosol optical depth suggests dominance of accumulation mode particles over the study area. Frequency analysis of turbidity coefficient suggests that polluted atmosphere is prevailing over the study area. Measured values of turbidity parameters in the present study have been compared with that in other major cities of the world and the results are discussed in the article.

Crop surface temperature (CST) is an important parameter to monitor crop status. Satellite data in thermal region provide an opportunity to estimate CST over large regions at frequent intervals. In the present study, various splitwindow algorithms are employed to estimate CST over rice areas in irrigation projects of Krishna basin, South India using multi-resolution MODIS satellite data. NDVI is used to approximate the mean pixel emissivity, by taking known values for emissivity and NDVI for pure vegetation and bare soil pixels. Diurnal ground measurements are made to evaluate satellite-derived CST. CST values obtained using the Sobrino method have been found to be closer to the groundmeasured values compared with other algorithms, as it takes into account view angle, atmospheric transmittance, and water vapour corrections. It has been observed that the error in estimating CST is comparatively lower for well-grown crops.