eChapters

THE AGRO-ECOSYSTEM CONCEPT

An agro-ecosystem is a site of agricultural production farm, for example- understood as an ecosystem. The agro-ecosystem concept provides a framework with which to analyze food production systems as wholes, including their complex sets of inputs and outputs and the interconnection of their component parts.

Because the concept of the agro-ecosystem is based on ecological principles and our understanding of natural ecosystems, the first topic of discussion in this chapter is the ecosystem. We examine the structural aspects of ecosystems their parts and the relationships among the parts and then turn to their functional aspects how ecosystems work. Agro-ecosystems are then described in terms of how they compare, structurally and functionally, with natural ecosystems.

Structure of Natural Ecosystem

An ecosystem can be defined as a functional system of complementary relations between living organisms and their environment, delimited by arbitrarily chosen boundaries, which in space and time appear to maintain a steady yet dynamic equilibrium. An ecosystem thus has physical parts with particular relationshipsthe structure of the system that together take part in dynamic processes the function of the system.

The most basic structural components of ecosystems are biotic factors, living organisms that interact in the environment, and abiotic factors, nonliving physical and chemical components of the environment such as soil, light, moisture and temperature.

1. The Fraction of incident radiation which is reflected back is called as.................

   a) Short wave radiation

   b) Long wave radiation

   c) Albedo

   d) U.V radiation

2. Hulling percent in Rice crop.........

   a) 60-70%

   b) 40-50%

   c) 80-90%

   d) 20-25%

1.Agronomy is derived from Greek word.
2.Indigenous plough cuts ‘V-shape furrow’.
3.Indigenous plough is a primary tillage implement.
4.The ploughing depth of chisel plough is 60-70 cm.
5.Hoe is an example of secondary tillage implement.
6.Tillage improves soil aeration.

Important Points to be Remember

• Total number of essential nutrient elements required for plant growth -17
• Man made cereal – Triticale
• Father of India’s green revolution – Dr M.S. Swaminathan
• Na, Ve, Ni, Si, Co, Se and I - Beneficial nutrients
• Is added to prevent nitrogen losses from FYM decomposition-SSP

11.1 Agroterrorism

‘Agroterrorism’, also known as ‘agriterrorism and agricultural terrorism’, is a malicious attempt to disrupt or destroy the agricultural industry and/or food supply system of a population through “the malicious use of plant or animal pathogens to cause devastating disease in the agricultural sectors”. It is closely related to the concepts of biological warfare, chemical warfare and entomological warfare, except carried out by non-state parties. It refers to a hostile attack towards an agricultural environment including infrastructures and processes in order to significantly damage national or international political interests.

The terms ‘agroterrorism, agroterror and agrosecurity’, were coined by veterinarian pathologist Corrie Brown and writer Esmond Choueke in September 1999 as a means to spread the importance of this topic.

Introduction

The finance sector is undergoing a profound transformation driven by technological advancements, with artificial intelligence (AI) and blockchain technology at the lead of innovation. AI, characterized by its capacity to analyze vast datasets, recognize patterns, and take data-driven decisions has revolutionized various industries, including finance. On the other hand blockchain technology, initially popularized by cryptocurrencies like Bitcoin has arose as a disruptive force in finance, offering decentralized, transparent and secure transactional systems.

The integration of AI and blockchain technology holds immense promise for the finance sector, offering new avenues for efficiency, transparency, and risk management. By combining the analytical power of AI with the security and transparency of blockchain, financial institutions can streamline operations improve customer experiences, and mitigate risks more effectively. However realizing the full potential of this integration requires a nuanced understanding of its complexities, challenges, and opportunities.

Introduction 
In the rapidly evolving landscape of agricultural sciences, the integration of artificial intelligence (AI) and digital technologies is heralding a new era of innovation, efficiency, and sustainability. As the global population continues to grow and the impacts of climate change become increasingly pronounced, the demand for sustainable food production systems has never been more critical. AI and digital technologies are emerging as powerful tools to address these challenges, offering unprecedented opportunities to enhance food security, improve resource management, and build resilience against climate-related disruptions. AI-driven solutions, such as predictive analytics, machine learning, and precision agriculture, are transforming the way farmers cultivate crops, manage livestock, and optimize supply chains. These technologies enable real-time decision-making, allowing for more efficient use of resources such as water, fertilizers, and pesticides, while also reducing the environmental footprint of agricultural practices. The application of AI extends beyond the farm level, influencing every aspect of the agricultural value chain, from seed selection to post-harvest processing, distribution, and market access. The advent of the Internet of Things (IoT), coupled with advanced sensors and robotics, has further revolutionized agriculture by enabling the continuous monitoring of environmental conditions, soil health, and crop performance. IoT devices collect vast amounts of data, which, when analyzed using AI algorithms, provide actionable insights that empower farmers to make informed decisions. This data-driven approach not only enhances productivity but also contributes to sustainable farming practices by minimizing waste and optimizing resource allocation. Moreover, digital platforms and mobile technologies are bridging the gap between farmers and critical information, making it easier to access weather forecasts, market trends, and expert advice. These technologies are particularly transformative in developing regions, where they can provide smallholder farmers with the tools and knowledge needed to improve their livelihoods and contribute to food security. Additionally, blockchain technology is emerging as a solution for enhancing transparency and traceability in the food supply chain, ensuring that consumers have access to safe and ethically produced food.

Artificial intelligence is providing new opportunities to accelerate plant breeding and develop improved crop varieties capable of meeting changing agricultural demands. This chapter examines AI applications in genomic selection, phenotypic prediction, stress tolerance modelling and crop improvement. Machine learning, deep learning and predictive analytics enable researchers to analyze large genomic and phenotypic datasets and identify desirable traits. High-throughput phenotyping, image analysis, sensors, remote sensing and IoT technologies further support accurate monitoring of plant performance. The chapter also discusses AI-enabled precision agriculture, decision-support systems and sustainable crop management. Challenges involving data quality, computational requirements and model interpretability are addressed, alongside emerging opportunities involving genomics, biotechnology, robotics and digital twins.

Plant physiological processes determine growth, development, productivity and adaptation to changing environmental conditions. This chapter examines how artificial intelligence can improve understanding of plant physiology and stress responses. It considers physiological processes including photosynthesis, transpiration, nutrient dynamics and hormonal regulation and explores how AI can analyze complex biological, environmental and phenotypic datasets. Machine learning, deep learning, computer vision and predictive analytics support interpretation of information obtained through sensors, imaging, remote sensing and omics technologies. The chapter emphasizes early detection and prediction of stresses such as drought, salinity, temperature extremes, nutrient imbalance and pathogen pressure. It also discusses explainable AI, digital twins and federated learning as emerging approaches for plant stress research.

Introduction 
The evolution of health care since 1950s has been attributed to artificial intelligence (AI). Remarkable advances in the health sector are being made particularly after the years, following 2010, thanks to AI technology. The significance of AI as a game-changer in healthcare has tremendously grown together, with its accompanying range of challenges, opportunities, and reward. There is increasing utilization of AI across public health activities including biomedical research with its capacityto reduce medical errors and improveaccess to care. The efficiency and ability of AI to analyse large volumes of data in different medical fields so as to identify diseases and guide treatment plans has turned it into an area of significant impact in clinical decision-making. Machine learning as well as data analytics are examples of AI technologies employed in monitoring, diagnosis, treatment and measurement of risks/benefits thereby transforming various healthcare processes. It is revolutionizing medical practices in several areas such as diagnosis, treatment, rehabilitation and surgical operations [Tagliaferri et al., 2020]. Studies have found that, the use of AI has improved patient outcomes and simplified management duties, resulting to an efficient health service delivery system. As the influence of AI continues to grow, understanding its implications, benefits, and challenges becomes essential for healthcare professionals and policymakers alike.

Introduction

AI plays a key role in management effectiveness and adaptation, especially in small and medium enterprises (SMEs) facing evolving markets (Lemos S. I. C., et al., 2022). Designing AI-based management frameworks enhances companies’ economic activities through data analysis and informed decision making (Emine, Kalender., 2023). Academic research shows a growing interest in AI in management, with a focus on effective adoption practices and potential areas for development (Jbid, Arsenyan., Anke, Piepenbrink. 2023). Artificial intelligence (AI) significantly influences decision-making processes in management by enhancing rationality, efficiency, and fairness. AI, through machine learning and big data analysis, reduces managerial bounded rationality, enabling a shift from satisficing to optimizing decision making modes (Shtepa, S., et. al., 2023). In human resource management AI-enabled interview analysis improves candidate selection by uncovering hidden patterns and mitigating biases, leading to more objective decisions (Rajneesh, Panwar. 2023). Additionally, by increasing productivity, efficacy and creativity, the incorporation of AI into business strategy transforms decision-making; nonetheless, ethical considerations and human oversight are essential for responsible implementation (Gonesh, C., et. al., 2023).

Artificial Intelligence (AI) tools have rapidly transformed the way we work, learn, communicate, and solve problems in the 21st century. From automating routine tasks to assisting in complex decision-making, AI tools are becoming an essential part of modern life and professional environments. These tools use technologies such as machine learning, natural language processing, computer vision, and data analytics to perform tasks that traditionally required human intelligence. As a result, AI is not only increasing productivity but also changing the nature of work across industries. AI tools are software applications or platforms that use artificial intelligence to perform specific functions such as content creation, data analysis, customer support, language translation, image generation, video editing, coding assistance, and workflow automation. Today, AI tools are used in education, healthcare, business, marketing, finance, engineering, journalism, and many other fields. For example, students use AI tools for research, writing, and learning new concepts; businesses use AI for customer service chatbots, data insights, and marketing automation; and designers use AI for image and video creation.

AI-based content creation refers to the use of Artificial Intelligence technologies to generate, edit, optimize, and publish content automatically or semi-automatically. Content can include text, images, videos, audio, presentations, reports, emails, and social media posts. AI content creation tools use technologies such as Machine Learning, Natural Language Processing (NLP), Computer Vision, and Deep Learning to create content that looks like it was created by humans. In the modern digital world, content plays a very important role in education, business, marketing, communication, and entertainment. Earlier, content creation was completely manual and required a lot of time, effort, and human resources. Writers, designers, editors, and analysts had to work for many hours to create a single report, article, or presentation. But now, with the help of AI tools, content can be created in minutes instead of hours

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 can detect subtle changes in animal health before they become apparent through conventional observation. This chapter examines machine learning, deep learning and predictive analytics for livestock disease prediction and diagnosis. Models use physiological signals, diagnostic images, behaviour patterns, environmental conditions and historical records to recognize abnormalities and estimate disease risk. Wearable sensors and IoT devices enable continuous monitoring and early-warning systems, supporting preventive veterinary care and outbreak management. AI-based detection can reduce mortality, treatment costs and production losses while improving decision-making. The chapter also evaluates challenges involving inconsistent data, model interpretability, rural infrastructure, technical accessibility and the practical implementation of diagnostic systems.

Early and accurate detection of plant diseases is essential for protecting crop productivity and reducing agricultural losses. This chapter explores artificial intelligence-based plant disease detection using image processing and deep learning. It introduces major plant diseases and the fundamentals of image processing before examining deep learning approaches for identifying disease symptoms from plant images. AI models can automate disease recognition and support timely interventions in smart agricultural systems. The chapter presents a proposed methodology and discusses results and analysis, practical applications and limitations. It also considers challenges associated with data quality, environmental conditions and model performance, followed by future directions for developing more accurate, scalable and intelligent plant disease diagnostic systems