Understanding metalloids and the potential impact they can have upon crop success or failure Metalloids have a complex relationship with plant life. Exhibiting a combination of metal and non-metal characteristics, this small group of elements - which includes boron (B), silicon (Si), germanium (Ge), arsenic (As), antimony (Sb), and tellurium (Te) - may hinder or enhance the growth and survival of crops. The causes underlying the effects that different metalloids may have upon certain plants range from genetic variance to anatomical factors, the complexities of which can pose a challenge to botanists and agriculturalists of all backgrounds. With Metalloids in Plants, a group of leading plant scientists present a complete guide to the beneficial and adverse impacts of metalloids at morphological, anatomical, biochemical, and molecular levels. Insightful analysis of data on genetic regulation helps to inform the optimization of farming, indicating how one may boost the uptake of beneficial metalloids and reduce the influence of toxic ones. Contained within this essential new text, there are: Expert analyses of the role of metalloids in plants, covering their benefits as well as their adverse effects Explanations of the physiological, biochemical, and genetic factors at play in plant uptake of metalloids Outlines of the breeding and genetic engineering techniques involved in the generation of resistant crops Written for students and professionals in the fields of agriculture, botany, molecular biology, and biotechnology, Metalloids in Plants is an invaluable overview of the relationship between crops and these unusual elements.

Texture describes the content of many real world images: for example, clouds, trees, bricks, hair, fabric etc. all of which have textural characteristics.Feature extraction is one of the most important tasks for efficient and accurate image retrieval purpose. In this book we are going to use Cosine-modulated wavelet transform based technique for extraction of texture features. The major advantages of Cosine-modulated wavelet transform are less implementation complexity, good filter quality, and ease in imposing the regularity conditions. Texture features are obtained by computing the energy, standard deviation and their combination on each subband of the decomposed image. To check the retrieval performance, texture database of 1856 textures is created from Brodatz album. Retrieval efficiency and accuracy using Cosine-modulated wavelet based features will be found to be superior to other existing methods.