eChapters

Green nanocomposites offer promising possibilities for biomedical applications because biodegradable matrices can be combined with nanoscale fillers to obtain specific functional properties. This chapter explores their applications in tissue engineering and drug delivery, with attention to materials such as chitosan, alginate, cellulose, and polylactic acid. It discusses the fundamentals of green nanocomposites, tissue-engineering applications, controlled drug delivery, toxicity, biocompatibility, regulatory considerations, and current limitations. Nanocomposite systems can provide useful mechanical, thermal, antimicrobial, antioxidant, and therapeutic characteristics while supporting more sustainable material development. The chapter highlights the importance of balancing biomedical functionality, safety, environmental responsibility, processing requirements, and long-term biological compatibility.

The demand for lightweight, high-performance, and environmentally responsible materials is particularly strong in automotive, aerospace, and defence applications. This chapter examines biomass-derived green nanocomposites as potential materials for lightweight systems and energy-storage-related applications. It discusses material fundamentals, design requirements, sector-specific performance expectations, material systems, and performance benchmarks. Green processing and circular economy integration are considered alongside current technological challenges and strategic research gaps. Biomass-derived resources provide opportunities to reduce dependence on non-renewable feedstocks while developing functional composite materials. The chapter emphasizes the need to balance lightweight design, mechanical and functional performance, manufacturing feasibility, sustainability, and end-of-life considerations for demanding engineering applications.

Graphene and related carbon nanomaterials have attracted considerable attention as reinforcements for high-performance green composites. This chapter examines graphene, graphene oxide, reduced graphene oxide, carbon nanotubes, and graphene nanoplatelets and their integration with bio-based polymers and natural fibres. It discusses synthesis and fabrication methods, characterization techniques, mechanical properties, electrical and thermal performance, barrier properties, and applications. The chapter also considers dispersion and compatibility challenges associated with incorporating nanomaterials into green matrices. Graphene-based composites are explored for energy storage, environmental treatment, biomedical applications, sensors, packaging, automotive, and aerospace uses. Emerging trends focus on multifunctionality, scalable production, cost reduction, and sustainable processing.

Two-dimensional nanomaterials have expanded the possibilities for designing advanced green composites with tailored multifunctional properties. This chapter focuses on MXenes and nanosilicates, including laponite, montmorillonite, and halloysite, as important alternatives and complements to graphene-based nanomaterials. Their integration into green composite matrices can influence mechanical strength, thermal stability, barrier characteristics, surface functionality, and other performance parameters. The chapter examines the development of advanced green composites based on these nanomaterials and considers their potential for sustainable material design. Particular attention is given to the relationship between nanoscale structure, matrix interaction, and composite performance, highlighting opportunities for multifunctional and environmentally responsible material systems.

Green composites provide opportunities to reduce dependence on petroleum-derived materials while maintaining useful mechanical and functional performance. This chapter examines the fundamentals of green composites and the major challenges limiting their wider adoption. Important issues include poor interfacial bonding, nanoparticle dispersion, variability of natural fibres, mechanical and thermal limitations, processing difficulties, cost, scalability, and commercialization barriers. Nanoparticle reinforcement is explored as a means of improving tensile strength, stiffness, thermal stability, electrical conductivity, and barrier properties. The chapter also discusses green processing, advanced characterization, emerging applications, sustainability assessment, life-cycle assessment, and future research directions for developing high-performance and commercially viable green composite systems.

The development of green nanocomposites requires more than replacing conventional materials with renewable alternatives; it also requires responsible sourcing, efficient resource utilization, and consideration of the complete material life cycle. This chapter examines green and eco-friendly nanocomposites from the perspective of sustainable material selection and circular economy principles. It discusses strategies for obtaining renewable raw materials, reducing environmental impacts, improving resource efficiency, and designing materials for reuse, recycling, and recovery. Applications of green nanocomposites are also considered in relation to sustainability objectives. By connecting material development with circular economy strategies, the chapter provides a framework for developing nanocomposites that combine functional performance with reduced environmental burden.

Natural-fibre-reinforced green composites represent an important alternative to conventional synthetic-fibre composites. This chapter examines renewable fibres such as hemp, jute, flax, and sisal and their integration with bio-based polymer matrices. The discussion covers material composition, processing methods, environmental considerations, sustainability, applications, advantages, limitations, and current research developments. Natural fibres offer low density, renewability, biodegradability, and cost advantages, although hydrophilicity, variability, and processing limitations can affect composite performance. Surface treatments and appropriate fabrication methods are therefore important for improving mechanical, thermal, and durability characteristics. Applications in automotive, construction, packaging, and biomedical sectors demonstrate the growing potential of these sustainable composite materials.

Bipolar plates (BPPs) are essential components of proton exchange membrane fuel cells, performing functions related to gas distribution, current collection, thermal management, water management, and cell separation. This chapter examines conventional and advanced materials used for BPPs, with particular emphasis on nanoparticle-enhanced composite systems. The incorporation of nanoparticles can improve electrical and thermal conductivity while reducing weight and supporting environmental sustainability. Materials, synthesis and fabrication techniques, and additive manufacturing approaches are discussed. The chapter also considers cost and scalability because these factors influence the wider commercialization of fuel-cell technologies. Nanoparticle-reinforced composites are presented as promising lightweight alternatives for sustainable energy applications.

The growing demand for sustainable materials has encouraged the development of green composites based on bio-based polymers and renewable reinforcements. This chapter explores advanced carbon nanomaterials, including graphene, graphene oxide, carbon nanotubes, carbon nanofibers, and carbon quantum dots, as functional reinforcements for green composites. It discusses their synthesis, functionalization, incorporation into biopolymer and bio-based matrices, and influence on mechanical, electrical, thermal, and barrier properties. Particular attention is given to green synthesis approaches using biomass-derived resources, surface modification, dispersion, environmental considerations, scalability, cost, and life-cycle assessment. The chapter also considers future opportunities for multifunctional and commercially viable sustainable nanocomposites.

The successful transformation of agriculture through artificial intelligence and digital technologies requires supportive policies, institutions, infrastructure, and farmer-oriented programmes. This chapter examines the role of government policies and institutional support in promoting smart farming and digital agricultural innovation. Effective policies can facilitate investment in research and development, digital infrastructure, farmer training, technology access, rural connectivity, data governance, and sustainable agricultural practices. Institutional mechanisms are particularly important for overcoming barriers faced by smallholder farmers, including high technology costs, limited digital literacy, and inadequate infrastructure. The chapter emphasizes collaboration among governments, research institutions, universities, private enterprises, and farming communities to promote inclusive technology adoption, strengthen resilience, and achieve sustainable food security.

Increasing crop productivity while reducing resource consumption is a central objective of modern agriculture. This chapter examines how artificial intelligence can support crop yield improvement through data-driven analysis, prediction, and decision-making. AI models can integrate information from soil, weather, crop growth, remote sensing, IoT sensors, and historical yield records to identify important factors influencing productivity. Machine learning and deep learning approaches can support yield forecasting, crop selection, fertilizer and water optimization, and precision management. The chapter also considers AI-enabled genomic selection, market intelligence, and intelligent farm management. Challenges such as data availability, implementation cost, connectivity, farmer training, and model reliability are discussed alongside future opportunities including edge AI and explainable AI.

Agricultural robotics is transforming farming by introducing automated machinery capable of performing agricultural operations with greater precision and efficiency. This chapter explores the role of robotics in planting, irrigation, spraying, crop monitoring, weed management, harvesting, and other farm activities. Integration of artificial intelligence, machine learning, sensors, cameras, and IoT enables robots to collect information, analyse field conditions, and perform tasks with limited human intervention. The chapter discusses the major applications, benefits, and limitations of agricultural robotics. Robotics can address labour shortages, improve resource-use efficiency, reduce operational time, and support sustainable farming. However, high investment costs, technical complexity, infrastructure requirements, and skill shortages remain important adoption barriers

Agricultural drones have emerged as important tools for precision farming, crop surveillance, and field-level data acquisition. This chapter introduces drones as aerial intelligence platforms capable of collecting high-resolution spatial and spectral information about crops, soil, and environmental conditions. It discusses drone architecture, sensing technologies, remote sensing, crop monitoring workflows, and crop health assessment. Artificial intelligence and machine learning enable efficient analysis of aerial imagery for disease detection, irrigation management, precision spraying, crop stress assessment, and yield prediction. The chapter also examines autonomous drones, drone swarms, IoT integration, edge computing, and digital twins. Operational limitations, regulatory considerations, data management, and sustainability implications are also addressed.

Weather conditions strongly influence crop growth, irrigation requirements, pest and disease development, and final agricultural productivity. This chapter examines the application of artificial intelligence, machine learning, and deep learning for agricultural weather forecasting and predictive analytics. It explains how historical meteorological records, satellite observations, and IoT sensor data can be integrated to develop more accurate forecasting systems. Techniques such as Long Short-Term Memory networks, attention mechanisms, ensemble learning, and probabilistic forecasting support improved prediction of complex weather patterns. The chapter connects forecasting with agricultural decision-support systems for irrigation, pest management, and crop planning. It also considers data heterogeneity, missing observations, uncertainty, and future developments such as hybrid models, federated learning, and digital twins.

Water scarcity and inefficient irrigation are major constraints to sustainable agricultural production. This chapter presents smart irrigation as an AI-enabled approach for optimizing water application according to crop and environmental requirements. Smart irrigation systems integrate real-time information from soil-moisture sensors, weather forecasts, remote sensing, and crop growth models. Machine learning and predictive analytics can determine appropriate irrigation timing and quantities while reducing unnecessary water use. The chapter discusses the principles, components, technologies, and applications of AI-driven irrigation systems. It also highlights their contribution to improved crop productivity, nutrient-use efficiency, soil health, and climate resilience, while considering challenges such as investment costs, data accessibility, connectivity, and technological literacy.

Plant diseases significantly reduce crop productivity, quality, and economic returns, making early and accurate detection essential for effective crop protection. This chapter examines the application of machine learning and computer vision for identifying diseases at an early stage. It discusses agricultural image acquisition using RGB, hyperspectral, and sensor-based technologies, followed by image preprocessing, segmentation, feature extraction, and classification. Machine learning algorithms such as support vector machines, random forests, and convolutional neural networks are considered for disease recognition. The chapter also explores the integration of AI with IoT and mobile technologies and presents practical applications while addressing challenges involving datasets, environmental variability, model generalization, and computational requirements.

Effective crop monitoring is essential for maintaining plant health, optimizing farm operations, and improving agricultural productivity. This chapter explores the application of artificial intelligence for real-time crop monitoring and farm management. AI systems integrate information from sensors, satellites, drones, mobile devices, and other sources to assess crop conditions and identify emerging problems. Machine learning and deep learning techniques can analyse crop images, environmental parameters, growth patterns, and field conditions to provide actionable insights. The chapter examines real-time monitoring, image analysis, crop health assessment, anomaly detection, and decision-support applications. It also highlights the role of IoT and multimodal agricultural data in developing intelligent and responsive farming systems.

Artificial intelligence is becoming an important technology for transforming agricultural decision-making and farm management. This chapter introduces the fundamental concepts, tools, and applications of AI in agriculture. It explains how machine learning, deep learning, computer vision, predictive analytics, and intelligent systems can process large volumes of agricultural data. AI applications include crop monitoring, disease detection, yield prediction, precision input management, and automated decision-making. The chapter also discusses the major drivers of AI adoption, including increasing food demand, resource constraints, climate change, and labour shortages. Examples of AI-based agricultural applications demonstrate its potential to improve productivity, sustainability, resource efficiency, and resilience.

Agriculture faces increasing pressure from population growth, climate change, resource scarcity, land degradation, water shortages, and growing food demand. This chapter examines the major global challenges affecting agricultural productivity and food security. Climate variability, extreme weather events, declining natural resources, and changing pest and disease patterns create substantial risks for farming systems. The chapter discusses sustainable and climate-smart agricultural approaches for improving resilience and resource-use efficiency. It also highlights the contribution of artificial intelligence, precision agriculture, biotechnology, remote sensing, and digital technologies in addressing these challenges. Policy support, research investment, institutional cooperation, and equitable access to technology are emphasized for sustainable agricultural development

Agriculture is undergoing a major transformation driven by digital technologies, artificial intelligence, and data-based decision-making. This chapter introduces the concept of smart farming and explains the transition from traditional agriculture to precision and digital agriculture. It discusses enabling technologies such as the Internet of Things, remote sensing, cloud computing, machine learning, and data analytics. These technologies facilitate real-time monitoring, precision irrigation, crop surveillance, yield forecasting, and efficient resource management. The chapter also examines the development of cyber-physical agricultural systems and the potential of automation. Challenges including data quality, infrastructure, cost, technology adoption, and the rural digital divide are also highlighted.