Artificial Intelligence And Computer Vision For Ecological Informatics
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Artificial Intelligence and Computer Vision Technologies for Ecological Informatics
This book explores AI, machine learning, deep learning, bio-inspired algorithms, and computer vision in ecological informatics. It covers remote sensing, water body evaluation, agriculture mapping, aquatic monitoring and terrestrial ecosystems. It provides insights to develop models and prototypes benefiting society and the environment.
Artificial Intelligence and Computer Vision for Ecological Informatics
Ecological informatics, more commonly known as Ecoinformatics, is the study of environmental sciences and ecological information. It is an emerging interdisciplinary framework for the management, analysis, and synthesis of ecological data with the help of advanced computational intelligence algorithms. Management in this context is data acquisition, preprocessing, and sharing the data. Analysis and synthesis are the process of extracting useful information and forecasting with the help of intelligent algorithms. The aim of this book is to encapsulate concepts and theories of artificial intelligence and computer vision algorithms used for the evaluation of various ecological informatics applications. It focuses on soft computing, machine learning, deep learning, artificial intelligence, bio-inspired algorithms, data analysis tools, data visualization tools, and computer vision algorithms used in ecological informatics. The book covers remote sensing applications, water bodies evaluation, agriculture mapping, aquatic mapping, forest management, and terrestrial ecosystems. The book will be useful to students, researchers, scientists, and field experts in directing their work towards this domain, to deliver and design models and prototypes for the benefit of society and the environment.
Computer Vision and Pattern Recognition in Environmental Informatics
Computer Vision and Pattern Recognition (CVPR) together play an important role in the processes involved in environmental informatics due to their pervasive, non-destructive, effective, and efficient natures. As a result, CVPR has made significant contributions to the field of environmental informatics by enabling multi-modal data fusion and feature extraction, supporting fast and reliable object detection and classification, and mining the intrinsic relationship between different aspects of environmental data. Computer Vision and Pattern Recognition in Environmental Informatics describes a number of methods and tools for image interpretation and analysis, which enables observation, modelling, and understanding of environmental targets. In addition to case studies on monitoring and modeling plant, soil, insect, and aquatic animals, this publication includes discussions on innovative new ideas related to environmental monitoring, automatic fish segmentation and recognition, real-time motion tracking systems, sparse coding and decision fusion, and cell phone image-based classification and provides useful references for professionals, researchers, engineers, and students with various backgrounds within a multitude of communities.