eChapters

Human communication rarely depends on a single information modality. People simultaneously interpret language, images, sounds, gestures and contextual signals to understand their surroundings. Multimodal Large Language Models (MLLMs) seek to reproduce this capability by integrating text, vision and speech within unified computational frameworks. This chapter introduces the evolution and technological foundations of multimodal intelligence and explains how different data modalities can be aligned through shared representations. It examines the architectural components, training paradigms and evaluation methods used to develop multimodal systems. Applications are discussed across healthcare, autonomous systems, education, accessibility, conversational interfaces and intelligent assistants. For example, healthcare applications can combine electronic health records, spoken clinical interactions and medical images to provide richer contextual understanding. Similarly, autonomous vehicles can integrate visual road information, spoken instructions and textual navigation data. The chapter also considers challenges involving multimodal alignment, computational requirements, data quality, hallucination, interpretability and safety. It concludes by highlighting future research toward more natural, context-aware and human-centered AI systems capable of seeing, listening, reasoning and communicating

Large Language Models have moved beyond experimental research environments and now support conversational agents, coding assistants, recommendation systems and intelligent decision-making applications. However, delivering these services to millions of users requires extremely low latency, high throughput, scalability, security and cost efficiency. This chapter examines the engineering principles required for real-time LLM inference and large-scale deployment. It decomposes end-to-end latency into model execution, network and queuing components and discusses techniques for optimizing each stage. Model-level approaches such as quantization, knowledge distillation, LoRA, QLoRA and speculative decoding are examined for reducing computational requirements while maintaining model quality. At the infrastructure level, tensor and sequence parallelism, GPU and TPU acceleration, efficient batching, autoscaling and cloud-native deployment strategies are considered. Key-value caching and speculative decoding are discussed as important methods for reducing response time in interactive applications. The chapter also addresses privacy, confidential computing, real-time PII protection, energy consumption and monitoring of model performance. Practical deployment examples demonstrate how these techniques can support responsive, secure and efficient AI services at production scale.

The rapid growth of Large Language Models has demonstrated that increasing model size, training data and computational resources can significantly improve performance. This chapter examines the theoretical and empirical foundations of scaling laws and their relationship with optimization strategies used to train and deploy increasingly capable LLMs. It discusses the major scaling dimensions of model parameters, dataset size and computational budget and explains how predictable relationships between these factors influence model development. The chapter considers the foundational work on scaling laws and the subsequent movement toward compute-optimal training approaches. It also examines distributed training, advanced optimization algorithms, memory optimization, data curation, quantization, pruning and efficient inference. Particular attention is given to Mixture-of-Experts architectures, efficient attention mechanisms and inference-aware scaling. The interaction between scaling laws and optimization is presented as a synergistic process in which scaling principles guide resource allocation while optimization techniques improve computational efficiency. The chapter concludes by discussing emerging capabilities, sustainability, hardware requirements and responsible scaling of future foundation models.

Nitrogen is an essential plant nutrient and plays a particularly important role in intensively cultivated irrigated systems. This chapter examines nitrogen management in irrigated soils, where assured water availability and intensive cropping can increase nitrogen demand and create significant nutrient losses. The chapter begins with the functions of nitrogen and the forms in which nitrogen occurs in soil, including inorganic and organic forms. Mineralization and immobilization processes are discussed, along with factors affecting nitrogen mineralization and nitrogen transformations under waterlogged conditions. Major pathways of nitrogen loss, particularly volatilization, denitrification and leaching, are examined because they directly influence nitrogen-use efficiency and environmental quality. The chapter discusses different nitrogen fertilizers, including ammonium sulphate, ammonium chloride, ammonium nitrate and slow-release fertilizers. Nitrification inhibitors and strategies for efficient fertilizer use are also considered. Special attention is given to leaching of urea-N, nitrogen-use efficiency and nitrogen balance in the plant–soil system. The chapter therefore emphasizes balanced nitrogen management for sustaining productivity while reducing nutrient losses

Crop improvement is essential for developing horticultural varieties with superior yield, quality, resistance, and adaptation. This chapter introduces conventional breeding methods such as selection, hybridization, backcrossing, and mutation breeding, along with modern approaches involving molecular markers and biotechnology. Important breeding objectives include disease and pest resistance, abiotic stress tolerance, improved nutritional quality, shelf life, fruit characteristics, and market preference. Modern techniques such as marker-assisted selection, genomic approaches, and genome editing can accelerate the development of improved cultivars. The chapter connects traditional genetic principles with contemporary breeding technologies and explains how integrated breeding strategies can address changing production environments, consumer requirements, and emerging horticultural challenges.

Abstract

Global consumption of wheat during 2021/2022 marketing year reached to 787.4 million metric tons (https://www.statista.com/aboutus/our-researchcommitment/1239/m-shahbandeh). It shows about 4.4 million metric tons increase from the previous year. With the increase in population a significant increase in wheat production is needed to feed 9.7 billion mouths by 2050. A very limited effort can be directed to increase area under wheat production. Diseases and pests of wheat cause significant reduction (>30%) in production and 11-15% of total post-harvest losses of food grains were reported in India, which equates to about 20 million tonne (Basavaraja et al. 2007) and more than AUD10 million per year (Singh 2010). Thus, saving wheat from diseases can significantly contribute to the 2050 food security goal.

Horticultural crops comprise a diverse group of plants differing in botanical characteristics, growth habits, climatic requirements, economic uses, and production systems. This chapter presents systematic approaches for classifying horticultural crops into fruits, vegetables, flowers, plantation crops, spices, medicinal and aromatic plants, and other specialized groups. Classification based on botanical relationships, life cycle, climate, growth habit, edible parts, and commercial utilization is discussed to provide a practical understanding of crop diversity. Knowledge of classification helps students identify crops, understand their biological relationships, select appropriate production practices, and organize horticultural resources. The chapter establishes an essential foundation for crop-specific management and improvement

Sustainability

Sustainability is a complicated concept. The most often cited definition comes from the U.N. World Commission on Environment and Development (WCED): “Sustainable development is the development that fulfills the needs of the present without compromising the ability of future generations to meet their requirements.” Hence, sustainability has been defined as meeting our own needs without compromising the ability of future generations to meet their own needs.

Introduction

Rice straw is produced as a by-product of rice production at harvest. Agricultural Waste Burning (AWB) is a major contributor of air pollution in many countries, particularly in South Asia. Exposure to fine particulate matter less than 2.5 μm (micrometer), also known as PM 2.5, released by AWB can cause premature mortality due to respiratory and cardiovascular diseases (Brauer et al.,2012). Combine Harvesters used on the spring rice crop leave behind stubble and straw on the field (Sidhu et al., 2007). Farmers burn the rice residue while preparing the field for wheat sowing season that follows. Further, migrant labour from neighbouring states of Bihar and Uttar Pradesh has decreased (Sirhindi, 2019), and resulted in an increase in the cost of agricultural labour during the rice harvest season (Sharma,2018). Farmers find it both convenient and economical to burn residue to clear and prepare their fields for the next crop. In 2018, over 20 million metric tons of rice residue was generated in Punjab, of which almost 10 million metric tons was subject to open-air burning (MoA, 2019). The time window between rice harvesting and wheat sowing is just 20 to 30 days towards the conclusion of the Kharif season, which is one of the primary reasons of crop waste burning (Balwinder et al., 2016).

Introduction

In the evolving landscape of agriculture, where feeding a rapidly growing global population is a pressing concern, the role of data analytics has emerged as a beacon of hope, igniting revolutionary transformations in optimizing crop yields and resource utilization. Data analytics, the science of examining raw data to uncover insights and make informed decisions, has transcended conventional farming practices, propelling the industry towards a new era of efficiency, sustainability, and productivity.

Introduction

"Research is the systematic approach to obtaining and confirming new and reliable knowledge." It is a systematic and orderly (following a series of steps). Purpose is new knowledge, which must be reliable. Thus research is:

1. Searching for explanation of events, phenomena, relationships and causes

• What, how and why things occur
• Are there interactions?

2. A process

• Planned and managed - to make the information generated credible
• The process is creative
• It is circular - always leads to more questions

Commercial Building

Commercial buildings consist of government buildings, banks, hospitals, schools, malls, retail, restaurants, and office spaces. Among these, office and retail occupy the major percentage of energy consumption. 

More than 40% of the total final energy consumption and about 30% of greenhouse gas (GHG) emissions are in connection with the civil and residential sector.

Maximum energy consumption in commercial buildings by HVAC system (51% followed by appliances and food service equipment (20%), lighting(12%), electronics and information technology(9%) and water (8%).

Introduction

India has vast livestock resources. Livestock sector includes animal husbandry, dairy and fisheries sector. Livestock provides livelihood to two-third of rural community and employment to about 8.8 % of the population in India. Livestock sector contributes 4.11% GDP and 25.6% of total Agriculture GDP. It provides milk and milk products, meat and meat products, and also constitutes a major supplier of food and food articles, raw materials, and finished products.

Abstract

Non-structural carbohydrates are the primary product of photosynthesis in plants. Glucose, fructose, sucrose, and so on are examples of Non-structural carbohydrates (NSC). These are free carbohydrates having a low molecular weight that are involved in the creation of structures. Plants’ principal photosynthetic product, the NSC, is involved in metabolic processes and plays a critical role in plant functional characteristics. In a recent laboratory inter-comparison, NSC concentration measurements had very minor uncertainty due to different extraction and quantification techniques. Different techniques such as High-Performance Liquid Chromatography (HPLC), Gas Chromatography-Mass Spectroscopy (GC-MS), Near Infra-red spectroscopy (NIRS), anion exchange chromatography, chemical approaches such as the Anthorn assay approach, and certain kits are used to detect NSC in plants. This review will cover the methods for measuring NSC in plant tissues and attempting to infer its dynamics, as well as how to apply these tools to go beyond simply monitoring plant carbon connections such as “how much NSC is there and where it is located” to “under what conditions and to what extent is NSC used” to support plant function.

Introduction

To address the escalating food demands of the global population, a transformation in the agricultural sector is imperative (Velten et al., 2015; Lampridi et al., 2019). The current overdependence on finite resources is leading to their overexploitation, largely due to irresponsible usage (Velten et al., 2015; Lampridi et al., 2019). By adopting a site-specific approach, farmers can apply inputs only where they are necessary in the field (Velten et al., 2015; Lampridi et al., 2019). This method can enhance output, decrease labor costs, and mitigate the environmental damage caused by excessive input use (Velten et al., 2015; Lampridi et al., 2019).

The motive behind conducting the workshop on Capacity Need Assessment and Development is that the existing Extension Advisory Services (EAS) needs new capacities to respond effectively to the emerging challenges in agricultural development.

Floriculture and landscape architecture combine plant production with aesthetics, environmental improvement, recreation, and urban development. This chapter introduces the production, management, post-harvest handling, and marketing of flowers and ornamental plants. It also explains principles of landscape design, including balance, rhythm, harmony, colour, texture, form, space, and focal points. Softscape and hardscape components are considered in the planning of gardens, parks, institutional landscapes, roadsides, and urban green spaces. The chapter highlights the increasing importance of floriculture and landscape plants for urban biodiversity, pollution reduction, temperature moderation, recreation, and human well-being. Modern applications include turf management, green infrastructure, indoor plants, and peri-urban floriculture.

Introduction Since about 10,000 years ago, men and women have been rearing livestock and producing crops. Information about agricultural practices can be found in documents dating back over 3,000 years from ancient Egypt, Mesopotamia, and China. Agricultural extension services have evolved and developed in many parts of the world to address agricultural systems’ unique challenges and requirements. These initiatives aimed to provide farmers with scientific knowledge, technological advancements, and improved farming practices to enhance agricultural productivity.

Climate change impacts

• Climate change is a crucial factor behind cyclone Tauktae

• Global ocean has absorbed 90% of the excess heat generated by greenhouse gas emissions since 1970

• Led to anomalous ocean warming in Arabian Sea and Bay of Bengal, which in turn makes cyclones intensify rapidly

• India will see more frequent cyclones every year due to global warming

Introduction

Farming has undergone significant changes since the Green Revolution in the late 1960s and economic liberalisation in the early 1990s. These changes have led to a shift in focus towards a few specific enterprises due to factors such as a decrease in farm size, fluctuating commodity prices, the need for livelihood diversification, and a shortage of labor. (Ponnusamy and Kousalya, 2017). The current scenario in the country indicates that the agricultural area under cultivation is decreasing and more than 20 percent of the current cultivable land will be converted into non-agricultural purposes by 2030 (Gill et al., 2005). 80 per cent of farm families belong to the small and marginal farmer categories. Hence India is putting more emphasis on agriculture in research as well as in technology transfer through many extension activities.