Uday Pratap Singh – författare

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:

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:

Neural Networks is an integral part in machine learning and a known tool for controlling nonlinear processes. The area is under rapid development and provides a tool for modelling and controlling of advanced processes. This book provides a comprehensive overview for modelling, simulation, measurement and control strategies for reactive distillations using neural networks.

Neural Networks is an integral part in machine learning and a known tool for controlling nonlinear processes. The area is under rapid development and provides a tool for modelling and controlling of advanced processes. This book provides a comprehensive overview for modelling, simulation, measurement and control strategies for reactive distillations using neural networks.

This edited volume explores the integration of artificial intelligence to improve crop production. It addresses the critical need for intelligent crop management in light of the world''s escalating population. Encompassing a spectrum of technologies, including computer vision, image processing, soft computing, machine learning, and deep learning, the book explores advancements in decision-making systems. It integrates data science methodologies, Internet of Things, wireless communications, and a range of sensors and actuators to provide precise, timely, and cost-effective solutions to agricultural challenges, ultimately enhancing both the quality and quantity of crop yields. The book empowers its audience to direct their efforts towards designing models and prototypes that benefit society and the environment, making it an indispensable resource for those eager to shape the future of intelligent agriculture.

It serves as a comprehensive guide for students, scholars, and academicians keen on delving into the transformative field of artificial intelligence in agriculture. Researchers, scientists, and field experts will find invaluable insights to guide their exploration and contribution to this domain.

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.