M. M. Sufyan Beg – författare

This book offers insights into how modern technologies are transforming agriculture with a focus on their effects in aiding sustainability. It explores the current applications of technologies such as AI, IoT, and machine learning, and their impact on improving crop yield, resource efficiency, and environmental impact. Through practical examples and case studies, it demonstrates how these technologies can address challenges in agriculture and enhance operational efficiency. Key Features:Analysis of how IoT devices and sensor networks are used for real-time monitoring of farm operations, soil conditions, and weather patterns.Studies emerging technologies like machine vision, predictive modelling, and natural language processing, for tasks such as crop monitoring and pest detection.Detailed discussion on machine learning algorithms, including supervised, unsupervised, and reinforcement learning, tailored for agricultural applications.Insights into how AI, IoT, and machine learning optimize resource use, improve crop yield and quality, and enhance sustainability.Case studies to highlight real-life examples from crop management to livestock and water conservation.Smart Agriculture for Sustainable Practices is for researchers, professionals, and policymakers interested in leveraging technology for sustainable agricultural practices.

This book offers insights into how modern technologies are transforming agriculture with a focus on their effects in aiding sustainability. It explores the current applications of technologies such as AI, IoT, and machine learning, and their impact on improving crop yield, resource efficiency, and environmental impact. Through practical examples and case studies, it demonstrates how these technologies can address challenges in agriculture and enhance operational efficiency. Key Features: Analysis of how IoT devices and sensor networks are used for real-time monitoring of farm operations, soil conditions, and weather patterns. Studies emerging technologies like machine vision, predictive modelling, and natural language processing, for tasks such as crop monitoring and pest detection. Detailed discussion on machine learning algorithms, including supervised, unsupervised, and reinforcement learning, tailored for agricultural applications. Insights into how AI, IoT, and machine learning optimize resource use, improve crop yield and quality, and enhance sustainability. Case studies to highlight real-life examples from crop management to livestock and water conservation. Smart Agriculture for Sustainable Practices is for researchers, professionals, and policymakers interested in leveraging technology for sustainable agricultural practices.