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Full Description
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.
Contents
Preface. Drone Importance and their Necessity in Future Generation Agriculture. Advances in Wildfire Spread Detection and Prediction: Techniques, Challenges, and Applications. Leveraging Remote Sensing for Agriculture Mapping: Techniques, Applications, and Future Directions. Basil Crop Detection Using Computer Vision and Deep Learning Approach. Remote Sensing for Agriculture Mapping. Advances of Remote Sensing Technologies in Agriculture: Current Progress and Future Perspectives. Remote Sensing for Sustainable Agriculture: A Machine Learning Approach to Optimizing Farm Yield and Economic Returns. Leveraging AI and CV Technologies to Advance Water Quality Assessment in Ecological Informatics. Tracing Plant Growth Patterns: Employing Artificial Intelligence and Computer Vision for Explicit Mapping. AI-Driven Circular Economy: Innovations in Agro and Food Waste Management. Advancements in Machine Learning for Water Quality Assessment. Enhancing Agricultural Support with AI in the Farmer ChatBot Framework. Federated Learning: A Game-Changer in Agricultural Decision-Making and Precision Farming. Clean Streams, Clear Futures: AI Innovations in Water Quality Monitoring. Transforming Waste into Resources: AI's Impact on Wastewater Treatment. Soil Moisture Evaluation by Artificial Intelligence and Computer Vision. Crop Yield Estimation. Soil Fertility Evaluation. Advancements in Soil Moisture Evaluation: Sensors, Remote Sensing and Artificial Intelligence. Sustainable Agriculture: Economic Perspectives on AI and ML in Crop Yield Estimation. Index.
