This book offers an overview of the current knowledge on beta glucanase enzymes derived from actinobacterial source, their structure, functions and industrial applications. It summarizes the exploitation of actinomycetes, assay methods for beta glucanase screening, structural characterisation, genetic engineering, protein engineering, strategies for purification, characterisation and finally how to synthesize a hybrid enzyme of beta glucanases.Enzyme beta glucanases have considerable interest in industrial biotechnology owing to its distinct effect on the hydrolysis of insoluble beta glucan molecules and applications in particular industrial sectors such as food, feed and beverages industries. The beta glucanases are characterized extensively in microorganisms viz. bacteria, fungi and also in plants. A wide varieties of actinobacterial - glucanases have exclusively active on -glucan substrates and cleave the mixed linkage -glucan chains at different points. This is significant for picking precise substrates for the assay of each type of enzymes within this enzyme groups. The well recognized specificity and mechanism of action within the enormous substrates allow to understand that most of them are varied from cellulases, excluding -1,4-glucanases. The progress in genetic and protein engineering in actinobacterial beta glucanases improves the stability, activity and thermodynamic properties in relation with industrial relevance. This book will be useful for researchers and students engaged in industrial biotechnology, enzyme production, molecular biology, protein engineering and many more.

THE SERIES: FRONTIERS IN COMPUTATIONAL INTELLIGENCE The series Frontiers In Computational Intelligence is envisioned to provide comprehensive coverage and understanding of cutting edge research in computational intelligence. It intends to augment the scholarly discourse on all topics relating to the advances in artifi cial life and machine learning in the form of metaheuristics, approximate reasoning, and robotics. Latest research findings are coupled with applications to varied domains of engineering and computer sciences. This field is steadily growing especially with the advent of novel machine learning algorithms being applied to different domains of engineering and technology. The series brings together leading researchers that intend to continue to advance the field and create a broad knowledge about the most recent research. Series Editor Dr. Siddhartha Bhattacharyya, CHRIST (Deemed to be University), Bangalore, India Editorial Advisory Board Dr. Elizabeth Behrman, Wichita State University, Kansas, USA Dr. Goran Klepac Dr. Leo Mrsic, Algebra University College, Croatia Dr. Aboul Ella Hassanien, Cairo University, Egypt Dr. Jan Platos, VSB-Technical University of Ostrava, Czech Republic Dr. Xiao-Zhi Gao, University of Eastern Finland, Finland Dr. Wellington Pinheiro dos Santos, Federal University of Pernambuco, Brazil

Since agriculture is one of the key parameters in assessing the gross domestic product (GDP) of any country, it has become crucial to transition from traditional agricultural practices to smart agriculture. New agricultural technologies provide numerous opportunities to maximize crop yield by recognizing and analyzing diseases and other natural variables that may affect it. Therefore, it is necessary to understand how computer-assisted technologies can best be utilized and adopted in the conversion to smart agriculture. Modern Techniques for Agricultural Disease Management and Crop Yield Prediction is an essential publication that widens the spectrum of computational methods that can aid in agriculture disease management, weed detection, and crop yield prediction. Featuring coverage on a wide range of topics such as soil and crop sensors, swarm robotics, and weed detection, this book is ideally designed for environmentalists, farmers, botanists, agricultural engineers, computer engineers, scientists, researchers, practitioners, and students seeking current research on technology and techniques for agricultural diseases and predictive trends.

Since agriculture is one of the key parameters in assessing the gross domestic product (GDP) of any country, it has become crucial to transition from traditional agricultural practices to smart agriculture. New agricultural technologies provide numerous opportunities to maximize crop yield by recognizing and analyzing diseases and other natural variables that may affect it. Therefore, it is necessary to understand how computer-assisted technologies can best be utilized and adopted in the conversion to smart agriculture. Modern Techniques for Agricultural Disease Management and Crop Yield Prediction is an essential publication that widens the spectrum of computational methods that can aid in agriculture disease management, weed detection, and crop yield prediction. Featuring coverage on a wide range of topics such as soil and crop sensors, swarm robotics, and weed detection, this book is ideally designed for environmentalists, farmers, botanists, agricultural engineers, computer engineers, scientists, researchers, practitioners, and students seeking current research on technology and techniques for agricultural diseases and predictive trends.