Siddharth Singh Chouhan – författare

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.

Agriculture is going through a technology renaissance with deep learning leading the way. This volume addresses how advanced AI methods are transforming the future of farming, food systems, and environmental sustainability. It is forward-looking guide to integrating smart systems in agriculture across all scales - from soil to satellite. The volume comprises contributions from international leaders in AI, agronomy, genomics, remote sensing, and robotics. It addresses a broad array of emerging topics, such as autonomous farming systems, UAVs, and deep reinforcement learning for field operations; computer vision and hybrid models for crop, weed, and livestock detection; and advanced edge and federated learning approaches to real-time, privacy-conscious decision-making. It also discusses explainable AI for transparent agricultural models, deep learning in crop genomics and trait prediction, biodiversity monitoring, and time-series forecasting of pests and climate stress. Generative AI for generating synthetic agricultural data and simulating digital farms with AR/VR and digital twins are also covered. With a focus on climate-smart agriculture, the book addresses the role that deep learning can play in promoting resilient, adaptive, and sustainable food systems under climate change and food insecurity globally. It concludes with an examination of the ethical, regulatory, and policy issues, presenting a vision for inclusive, human-centric AI in agriculture.Key Features: Integrates autonomous agriculture, explainable artificial intelligence, edge/federated learning, genomics of crops, and biodiversity monitoring.Highlights climate-resilient agriculture and future-proof simulations.Incorporates real-world applications, case studies, and multidisciplinary viewpoints.Connects AI research, policy, and ethics with agricultural adoption.

This book explores how modern technologies are changing the way we farm and produce food. The book covers both the opportunities and challenges that come with using technology in farming and offers practical solutions.The chapters begin with an introduction to the AgriTech revolution and the basic concepts of agricultural technology.

This edited volume explores the integration of artificial intelligence to improve crop production.

This edited book focus on two most emerging areas and covers the different aspects of computer vision and drone technology in the field of agriculture. It comprises various applications including segmentation/classification of plant diseases, monitoring of crops, grade/quality estimation of fruits/flowers/vegetables/crops, surveillance, soil deficiency estimation, crop/plant growth estimation, canopy measurement, water stress management, vegetation indices calculation, weed detection, and spraying, among other. It has 17 chapters contributed by experts in the field of computer vision, drone technology, deep learning, machine learning, artificial intelligence, image processing, agriculturist, and plant pathologists.The recent development of high-end computing devices and the adaptation of unmanned aerial vehicles has provided a mechanism to automate traditional agriculture practices. The on-field or aerial images captured using cameras are processed with the help of intelligent algorithms, and an assessment is drawn for further recommendations. This practice is efficient in provisioning an accurate, timely, and economical decision-making system to overcome the problems of agricultural field experts and farmers. This process is advantageous in increasing the quality and quantity of crop yields.This book serves as an excellent guide to students, researchers, scientists, and field experts in directing their work toward this domain and developing/designing models. Further, this book is useful for pathologists, biotechnologists, seed production specialists, breeders, market managers, and other stakeholders associated with underlying technology or market development from the public and private sectors.