Bhabashankar Sahu

Precision agriculture, which combines modern technology with conventional farming techniques, has significantly transformed the efficient use of water, soil and farm resources. This chapter examines the dynamic relationship between technology and security within this particular industry, with a specific emphasis on emerging technologies such as IoT, IDS, authentication mechanisms, AI, and cyber security. The integration of IoT enables real-time environmental sensing, while Intrusion Detection Systems (IDS) provide protection for agricultural networks against cyber threats. Authentication mechanisms play a crucial role in safeguarding essential agricultural equipment and control systems. The purpose of this chapter is to provide guidance to researchers, practitioners, and policymakers regarding the complex connection between technology and security within the context of precision agriculture. The article adds to the ongoing dialogue surrounding the preservation of agricultural practices, presenting a vision of a sustainable future in which innovation and resilience are complementary.

The swift advancement of machine learning (ML) has altered several industries, including agriculture, by providing innovative ways of addressing complex challenges related to modern farming. This chapter discusses the use of ML in precision agriculture, emphasizing its capacity to maximize crop output and improve agricultural practices. It studies the use of supervised, unsupervised, reinforcement, and deep learning methodologies to evaluate extensive datasets derived from remote sensing technologies, soil sensors, climate data, and agricultural equipment. Principal applications include predictive modeling for agricultural yield estimation, pest and disease identification, soil health assessment, irrigation optimization, and precision fertilization. The chapter also examines the problems and limits related to the implementation of machine learning in agriculture, including data quality and farmer acceptance.

The Internet of Things (IoT) significantly influences the advancement of smart infrastructure systems in civil and agricultural engineering. IoT technologies have reshaped the management of urban infrastructure and agricultural operations through real-time monitoring, predictive maintenance, and automated control. In civil engineering, IoT sensors help monitor structural integrity, environmental conditions, and urban utilities, enhancing safety and efficiency in areas like traffic and waste management. IoT enhances agriculture through precision farming methods, including automatic irrigation, monitoring of crop and soil health, and resource optimization, leading to sustainable practices and increasing yields. This study analyzes IoT applications in rural and remote areas, focusing on the integration and challenges of IoT deployment. This also employs a case study approach to analyze IoT applications, highlighting key technological, economic, and policy challenges in remote and rural regions where connectivity and integration are critical. The findings suggest that IoT-driven infrastructure improves efficiency, sustainability, and resilience, but require more comprehensive analysis and integration in remote and rural areas. Future possibilities for IoT are discussed, focusing on advancements in edge computing, the use of renewable energy, and the development of smart rural infrastructure that meets both urban and agricultural needs. The paper urges for ongoing research and supportive policies that optimize the potential of IoT-integrated infrastructure systems, which are expected to improve efficiency, sustainability, and resilience in both sectors.

Amidst the escalating global challenges of climate change, limited resources, and population growth, the adoption of sustainable land and resource management has become imperative to ensure food security and environmental conservation. Precision agriculture enhances process efficiency, reduces environmental impact, and improves agricultural productivity through the integration of artificial intelligence technologies, including machine learning, deep learning, and computer vision. Key findings indicate a reduction of 10–20% in input costs and an increase of 15–25% in crop yields through efficient resource utilisation. Furthermore, precision irrigation systems can achieve water savings of up to 50%, while targeted pesticide treatments reduce chemical usage by 30–40%. This chapter examines the economic and environmental benefits, highlighting a 20% reduction in CO2 emissions. Recent advancements underscore the potential of AI to foster sustainable agriculture, promoting environmental conservation and economic viability.

Automated technology has transformed agriculture by improving processes from tillage to supply chain management. This chapter provides an in-depth exploration of automated decision-making (ADM) applications within the agricultural sector, including tillage, planting, irrigation, crop selection, fertilization, pest management, harvesting, storage, and supply chain management. It begins by discussing automated technology concepts and how they enhance efficiency, productivity, and sustainability in farming practices. Real-world examples and case studies demonstrate successful ADM implementations, showing how it is applied and its results. It also discusses challenges and future directions in adopting automated technology in agriculture, such as scalability, data privacy, regulatory frameworks, and insights for stakeholders. The chapter aims to assist farmers, agronomists, policymakers, and industry professionals in utilizing automated technology for innovation, enhancing agricultural processes, and tackling global food security challenges in modern agriculture.