eChapters

Artificial intelligence offers practical solutions to agricultural problems associated with population growth, food demand, climate change and limited natural resources. This chapter introduces machine learning, deep learning, computer vision and natural language processing, together with enabling technologies such as IoT, remote sensing and cloud computing. It explains their applications in crop monitoring, disease recognition, yield forecasting, irrigation, soil assessment and livestock management. AI can improve decision-making, resource efficiency, automation and precision farming while reducing reliance on manual labour. The chapter also examines adoption barriers, including high costs, limited digital literacy, data-security concerns and weak rural infrastructure, and considers future developments in autonomous and climate-resilient farming.

Introduction

A toxin (Greek: ôïîéêüí, toxikon, lit. (poison) for use on arrows) is a poisonous substance produced by living cells or organisms that is active at very low concentrations. Toxins can be small molecules, peptides, or proteins and are capable of causing disease on contact or absorption with body tissues by interacting with biological macromolecules such as enzymes or cellular receptors. Toxins vary greatly in their severity, ranging from usually minor and acute (as in a bee sting) to almost immediately deadly (as in botulinum toxin)

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.

ABSTRACT

Advances in biotechnology, particularly methods for culturing plant cells and tissues, can provide new means for the commercial processing of even rare plants and the chemicals they produce. These new technologies will extend and enhance the usefulness of plants as renewable resources of valuable chemicals. Aristolochic acid is mainly found in the genus Aristolochia. Aristolochic acid has been shown to have anti-tumour activity and has been recommended for its activity against various effects of snakebite and its capacity to inhibit phospholipase A₂ or other enzymes. Considering the importance of medicinal use of A. bracteolata and A. indica, in the present study, they were selected for in vitro development and rapid multiplication of plant through tissue culture. The study was undertaken to enhance the in vitro production of Aristolochic acid in different tissue cultures. Cell suspension culture was established from friable calli of both the species in MS liquid medium. Bergmann plating technique was carried out for cell line selection for more production of Aristolochic acid. Primary transformation was carried out by Agrobacterium tumefaciens strain LBA4404 / pTOK233 with Gus and hpt genes to confirm the transgenic potential of A. bracteolata and A. indica. In order to enhance the production of aristolochic acid in in vitro culture condition, the physical, chemical and biological conditions were altered. Aristolochic acid was isolated, identified and purified from naturally growing plants as well as from tissue culture plants and all other culture staes

Animal behaviour provides valuable information about health, welfare, reproduction, environmental adaptation and social interaction. Traditional observation is labour-intensive, time-consuming and vulnerable to observer bias. This chapter explains how machine learning automates behavioural analysis using video, sensor and bioacoustic data. It covers preprocessing, feature extraction, classification, clustering and recurrent neural networks for temporal analysis. Computer-vision techniques—including object detection, tracking and pose estimation—enable continuous and non-invasive monitoring. Case studies demonstrate applications in livestock management, wildlife conservation and laboratory research. The chapter also examines data labelling, model generalization, ethics, explainable AI, real-time processing and the integration of multiple behavioural data sources

Digital dairy farming integrates IoT, artificial intelligence, machine learning, big-data analytics, cloud computing, sensors and automation to improve herd management. This chapter explains how wearable devices and monitoring systems support early disease detection, welfare assessment, milk-yield analysis and precision feeding. Automated milking, feeding and other operations reduce manual labour, improve consistency and lower operating costs. The architecture of smart dairy systems—including sensors, wireless communication and decision algorithms—is examined alongside environmental and economic benefits. Barriers include high initial investment, limited digital skills, poor rural connectivity and cybersecurity risks. Future developments may include AI-enabled autonomous dairy farms and interconnected, sustainable agricultural ecosystems.

Introduction

Pollution is the introduction of contaminants into an environment that causes instability, disorder, harm or discomfort to the physical systems or living organisms they are in.
 
Pollution can take the form of chemical substances, or energy, such as noise, heat, or light energy.
 
Pollutants, the elements of pollution, can be foreign substances or energies, or naturally occurring; when naturally occurring, they are considered contaminants when they exceed natural levels. Sometimes the term pollution is extended to include any substance when it occurs at such unnaturally high concentration within a system that it endangers the stability of that system. For example, water is innocuous and essential for life, and yet at very high concentration, it could be considered a pollutant.

Robotics is transforming livestock production by automating repetitive, labour-intensive and precision-dependent operations. This chapter examines automated milking, robotic feeding, manure removal, health monitoring, drones and autonomous herd-management systems across dairy, poultry, swine and sheep production. Integration with AI, IoT and sensors creates connected environments capable of continuous monitoring and data-driven action. Benefits include higher productivity, lower labour requirements, timely animal care and more efficient resource use. Challenges include substantial investment, technical complexity, maintenance and farmer acceptance. Emerging developments such as autonomous farms, swarm robotics, solar-powered equipment and climate-smart robotic systems may further strengthen animal welfare, environmental sustainability and global food security.

Introduction 
Natural product scaffolds and heterocyclic ring-based organic compounds are crucial for developing bioactive substances and therapeutic drugs, serving as essential structural units for bioactive molecules and pharmacophores. Numerous experts have worked hard to design and develop organic synthesis, which could make it easier to obtain these crucial organic chemicals. Due to the multistep reactions involving several hazardous chemicals, conventional organic synthesis is neither commercially feasible nor environmentally benign. To overcome these issues, improvisation or developing a new synthetic approach with a superior atom and step economic procedure is always desirable. The biggest challenge in organic synthesis is functionalizing a carbon-hydrogen (C-H) bond in a molecule. This is generally achieved through radical intermediates and catalysis by enzymes or transition metals. The transition metal-catalyzed process is the most popular due to its high chemoselectivity, site-selectivity, and efficiency (Yamaguchi et al., 2012). Inert C-H bonds can be directly functionalized, offering appealing synthetic strategies that enhance both atom and step economy and provide an overall efficiency of multistep synthetic sequences, leading to a sustainable and greener approach. These optimizations provide atom and step-economical methods for synthesizing structurally complicated organic molecules with broad substrate scope and good to exceptional product yields (Ackermann, 2011; Gensch et al., 2016). Direct conversion from the substrate to the desired organic compound without pre-functionalization is attractive and cost-effective. However, it can be quite challenging to selectively functionalize particular C-H bonds due to the inertness of those C-H bonds having similar electronic and bonding properties and the ubiquity of C-H bonds in organic molecules (Sambiagio et al., 2018). Two main techniques have been explored to obtain selectivity in C-H bond functionalization (Brückl et al., 2012). The first is based only on the natural reactivity pattern of an organic molecule. This strategy is highly successful for some heterocycles with at least one specific reactive site due to the presence of heteroatoms. However, it produces inadequate selectivity for most aliphatic and aromatic hydrocarbons. The latter is to direct further functionalization at a particular site using directing groups already present in the molecule (Kuhl et al., 2012). The development of directing groups that effectively control the regioselectivity of C-H functionalization has lately transformed this field, enabling the incorporation of desired groups in unfunctionalized sites (Docherty et al., 2023; Labinger & Bercaw, 2002). Employing directing groups, various functional groups are added to a molecule using this method. Directing groups are crucial for breaking a C-H bond in a transition metal catalysis reaction, where the metal center in the active catalyst complex is usually coordinated with the substrate through a weak interaction with the nearby C-H bond. This interaction is typically not selective; thus, the metal centre may activate numerous C-H bonds on the substrate. However, when a directing group is incorporated into a substrate, the metal center can interact more strongly and regioselectively with a specific region on the substrate as directed by the directing group. This causes selective cleavage

1. Introduction: Setting the Stage for GLP in Artificial Insemination
Artificial Insemination (AI) stands as a cornerstone technology in modern animal agriculture, playing a pivotal role in the genetic enhancement of livestock across a diverse range of species. This technique, particularly prevalent in cattle and swine, allows for the widespread dissemination of superior genetic traits from carefully selected sires, leading to significant improvements in productivity, such as increased milk yield in dairy cattle and enhanced growth rates in beef cattle. Beyond genetic advancement, AI offers considerable advantages in disease management by mitigating the risks associated with natural mating, thereby controlling the transmission of reproductive tract infections. Furthermore, AI provides economic benefits to livestock producers by reducing the need to maintain live bulls, which require substantial resources, and by offering access to a broader spectrum of genetics than would be feasible through natural service alone. The application of AI is not limited to traditional livestock; it is increasingly employed in the breeding of horses and smaller ruminants, as well as in the management of pet populations, indicating its growing importance in animal reproduction across various sectors. The expanding scope of AI underscores the necessity for robust quality control measures to ensure its efficacy and safety across different animal species and production systems. The success of any AI program is intrinsically linked to the quality of the semen used, making the operational standards of AI laboratories paramount. Optimal conception rates rely on semen with high viability, motility, and morphological integrity, which can be compromised by inadequate handling and unsanitary 130 Concepts of Good Laboratory Practices (GLP) conditions during collection, processing, storage, and insemination. Poor semen management can lead to reduced fertility and the potential spread of diseases, thereby undermining the benefits of AI. To ensure consistent and successful outcomes, standardized procedures throughout the AI process are essential, providing a framework for optimizing techniques and minimizing the risk of errors. In this context, Good Laboratory Practices (GLP) emerges as a critical quality paradigm. Defined as a managerial quality control system, GLP encompasses the organizational processes and the conditions under which non-clinical health and environmental safety studies are planned, performed, monitored, recorded, archived, and reported. While traditionally applied to the safety testing of chemicals and pharmaceuticals, the core principles of GLP, with their emphasis on systematic processes, controlled conditions, and rigorous data management, hold significant relevance for ensuring the quality and reliability of operations within animal AI laboratories. The genesis of GLP regulations in response to concerns about the integrity and consistency of scientific data further underscores its fundamental importance in any laboratory setting where the trustworthiness of results is paramount. This report aims to provide a comprehensive analysis of the GLP paradigm within the context of animal AI laboratories. Its objectives include defining GLP and its core principles, investigating the specific application of these principles in the various stages of animal AI, exploring the benefits and challenges associated with GLP implementation, reviewing relevant regulatory guidelines and standards across different regions, analyzing the impact of GLP adherence on AI techniques and outcomes, identifying effective data representation strategies, comparing the operational differences between GLP and non-GLP compliant laboratories, and discussing emerging trends and future perspectives on the role of GLP in advancing the field.

PART-A

1. Indicate True/False for the following

1. Super infection phenomenon is common with antibiotics that are given as depot preparation.______
2. A combination of penicillin and chlortetracycline leads to antagonism of antimicrobial activity.______
3. Bactericidal drugs act most effectively on toxins librated by organism.
   1. Most of the laboratory sensitivity tests are conducted at a pH of 4-5.______

Introduction
Infection prevention and control refers to prevention and control of infections and their transmission in healthcare settings. Healthcare Associated Infections (HAI) are one of the most common adverse events in delivery of care and a major public health problem with an impact on morbidity, mortality and quality of life. Up to 7% of patients in developed and 10% in developing countries acquire at least one HAI. These infections also present a significant economic burden for the health system.1It is evident that HAIs result in prolonged hospital stays, long-term disability, increased resistance of microorganisms to antimicrobials, additional cost on health systems, high cost for patients and their family, and preventable deaths.
The core components of IPC2
• IPC programme at the national and health facility level
• National and facility level evidence-based guidelines on IPC
• Education and training
• HAI surveillance
• Multimodal strategies
• Monitoring and audit of IPC practices and feedback
• Workload, staffing and bed occupancy
• Built environment, materials and equipment for IPC
HAI Burden
HAI are one of the most common adverse events in healthcare delivery and a major public health problem with an impact on morbidity, mortality and

All separation procedures depend mainly on some physical characteristics of the compounds. It is relatively easy to separate compounds that have significantly different physical characteristics by simple techniques such as solvent extraction. If the various compounds are similar to each other in their molecular size, any slight difference in any one of their physical properties is exploited to achieve separation. 

Ultrasonography or sonography is a diagnostic medical procedure that uses echoes of ultrasound pulses to produce images, of the objects or of the areas of different densities in the body, on the screen. This diagnostic procedure is performed through ultrasound machine. Sonogram is a visual image produced from ultrasonography.

This chapter focuses on the statistical methods used for method development. These are categories as follow :

1.SUPAC- Scale-Up and Post-Approval Changes

2.PAT -Process Analytical Technique

1. SUPAC - The acronym “ SUPAC “stands for “Scale-Up and Post- Approval Changes” . It refers to the FDA-recommended testing and filing actions to be taken by a pharmaceutical firm when it changes the manufacturing processes of a drug product that has been approved via a New Drug Application (NDA), an Abbreviated New Drug Application (ANDA), or an Abbreviated Antibiotic Drug Application (AADA). FDA has provided its recommendations to industry in the form of SUPAC Guidances or supac guidelines.

Liver is situated in cranial abdomen. Scanning of abdomen is started with the examination of liver. Hepatic ultrasonography is a non-invasive diagnostic modality to evaluate liver parenchyma. Differentiation of focal disease from diffuse disease, cystic lesions from solid masses, and obstructive jaundice from non-obstructive jaundice is possible by hepatic ultrasonography.

In the area of pharmaceutical discovery, screening methods continue to play a major role in drug discovery. The screening process is simply testing molecular entities (compounds/natural products) for activity to advance those that exhibit the desired activity. This process has evolved over time in both the testing methods and in the degree of testing. Originally, the screening was done entirely by dosing animals and studying their responses. Then came test tube experiments and now, testing is conducted either with cell lines or with purified proteins in miniaturized test tubes called microplate wells. Screening by microplate has allowed the degree of testing to increase from a very small selected set of compounds to libraries, which contain thousands of test substances. Technological improvements in many areas of science and information technology have led to the evolution of screening methods from time consuming, animal dosing studies to high throughput screening (HTS) methods that have the potential to generate very large datasets of information in a very short time. Today, developments in bioinformatics have opened up the possibility of virtual (in silico) screens for targets where there is sufficient knowledge of the target or of its ligand. High throughput screening has become a pivotal part of drug discovery for identifying potential lead molecules for specific disease targets. The success of screening is measured by several factors including the throughput and costs of the assays, but ultimately by how many of the HTS hits progress forward through the drug discovery pipeline. Miniaturization of assays by conversion from 96- to 384- to 1536- wells to well-less platforms, such as microarrays and the utilization of automated equipments are methodologies implemented to increase the throughput and reduce the cost of the screen.

Carnivora is an order of placental mammals that have specialized in primarily eating flesh, whose members are formally referred to as carnivorans. The order Carnivora is the fifth largest order of mammals, comprising at least 279 species. They come in a very large array of different body plans with a wide diversity of shapes and sizes. The common characteristics of carnivores are given below:

• They are meat eaters.

• There is the presence of canine teeth.

• The intestinal tract is short and is adapted to rapid digestion as well as the assimilation of meat.

• All species have anal glands. Some species, like the striped skunk (Mephitis mephitis), may eject the contents of the anal gland as a defensive maneuver.

• Os penis is present in males.

• There is a lack of a clavicle which helps in the freedom of movement of the forelimb.

• Ulna is well developed.

• Toes end in claws.

Reliable artificial intelligence depends on accurate, timely and multidimensional livestock data. This chapter examines the transition from manual record-keeping to sensor-based systems that capture physiological, behavioural, production and environmental information. Data sources include wearable devices, IoT monitoring networks, computer-vision systems and environmental sensors. Their applications include monitoring health, welfare, movement, feeding and productivity. The chapter also considers the integration of heterogeneous datasets and persistent challenges involving missing values, noise, interoperability, scalability and system reliability. By connecting data generation with intelligent analysis, it emphasizes that robust acquisition protocols and dependable infrastructure are essential for developing effective, evidence-based and responsive livestock-management systems.

Cloud computing and Edge AI provide complementary infrastructure for intelligent livestock management. Cloud platforms offer scalable storage, centralized analytics, model training and integration of data from sensors, cameras, automated feeders and farm-information systems. Edge AI processes information close to the farm, reducing latency, bandwidth consumption and response time. This chapter explains their architectures, workflows and applications in dairy, poultry, swine and small-ruminant production. Uses include disease detection, estrus prediction, behavioural monitoring, feeding and environmental control. Hybrid cloud-edge systems balance centralized intelligence with local autonomy. Challenges involving connectivity, interoperability, cybersecurity, cost, model reliability and ethical animal surveillance are also critically examined.