eChapters

Accurate crop-yield forecasting supports food-security planning, resource allocation and farm decision-making. Yield is influenced by interacting factors such as weather, soil, irrigation, fertilizer and pesticide use, making reliable prediction challenging. This chapter presents an algorithmic framework using historical agricultural and environmental data. It covers cleaning, missing-value treatment, categorical encoding, normalization and feature extraction. Decision Tree, Gradient Boosting, AdaBoost and XGBoost regression models are evaluated individually and combined through soft voting. Performance is measured using standard prediction-error metrics. The case study demonstrates that ensemble modelling can improve accuracy and robustness, helping farmers and planners optimize inputs, anticipate production and strengthen agricultural productivity.

Punjab, known as the “breadbasket of India,” relies heavily on livestock, particularly cattle and buffalo, for its agricultural prosperity. These animals play a crucial role in the region’s dairy industry, contributing significantly to milk production and rural livelihoods. However, like all livestock, cattle and buffalo in Punjab are susceptible to a range of diseases that can impact their health, productivity, and overall well-being. Timely and accurate diagnosis is essential for effective treatment and prevention. This article explores some of the most common diseases affecting cattle and buffalo in Punjab, along with the diagnostic procedures used to detect and manage these health issues, ensuring the sustainability and growth of the dairy sector.

Hydatidosis

Hydatidosis is a zoonotic disease caused by metacestode stages of parasite Echinococcus granulosus and is recognized as a major public health problem worldwide. This tapeworm is found in small intestine of the canid (definitive host) where it produce proglottids containing eggs. Upon shedding through faeces the later are ingested by the cattle, buffaloes and other intermediate hosts including man. These hatch in their intestine and the oncosphere invade circulatory system and lodge in various organs (liver, lung, brain etc.) where they develop into large thick walled unilocular cavity called hydatid cyst, which expands and can reach very large size and buds hundreds and thousands of protoscoleces endogenously. In a survey on hydatid cysts, a prevalence rate of around 5% has been recorded in more than 4000 meat samples of different animals in Punjab.

Introduction

Canine epilepsy, a prevalent neurological disorder, significantly impacts the quality of life for both dogs and their owners. Traditional anticonvulsant therapies often yield inconsistent outcomes, necessitating the exploration of alternative treatments such as homeopathy [1][2]. Homeopathy’s individualized approach aligns with the principles of unicism, focusing on holistic care tailored to each patient’s unique symptom profile [3]. However, the subjective nature of remedy selection and response assessment poses challenges for practitioners.

The integration of Artificial Intelligence (AI) and Machine Learning (ML) offers a promising avenue to address these challenges [4]. Predictive models can analyze complex datasets to uncover patterns that guide remedy selection and treatment strategies. This study evaluates the comparative performance of Logistic Regression, Random Forest, and XGBoost in predicting treatment outcomes for canine epilepsy managed with homeopathic remedies.

8.1 Introduction

The objective of this chapter is to develop a smart control for site-specific management of fixed irrigation system with Blynk app. Mobile app is designed to control the water management in field. The complete system comprises of two sections field device and mobile app. Field device comprises of Arduino, Node MCU, Power supply, LCD, Relay board, soil moisture sensor, temperature & humidity sensor, water level sensor, motor1, motor2. The system is designed to establish control and communication with specific agricultural field to take sensory data from the sensors and control the PUMP IN motor and PUMP OUT motor with the help of mobile app. Fig.8.1 shows a block diagram for a system.

The increasing capabilities of Large Language Models have created strong demand for real-time AI services, but their computational complexity makes low-latency deployment difficult on conventional systems. Transformer models require substantial memory, high computational throughput and efficient data movement, creating challenges for mobile devices, embedded systems, edge platforms and latency-sensitive applications. This chapter introduces hardware–software co-design as an integrated strategy for addressing these challenges. Rather than optimizing hardware and software independently, co-design aligns model algorithms with the characteristics of processors, memory hierarchies, accelerators and communication systems. The chapter examines hardware platforms including GPUs, TPUs, FPGAs and ASICs, together with software techniques such as quantization, pruning, knowledge distillation, operator fusion and memory scheduling. Hybrid architectures are discussed for reducing latency, improving throughput, lowering energy consumption and increasing scalability. Applications in healthcare, finance, autonomous driving, robotics, industrial IoT and real-time translation illustrate the importance of efficient inference. The chapter demonstrates that coordinated optimization across the full computing stack can bridge the gap between LLM capability and practical real-time deployment while maintaining acceptable accuracy and reliability.

Introduction

Accuracy and uncertainty analysis is an important step in preparation of surface map through geostatistical techniques or digital soil mapping approach. It is because that we rely on a map based on how much accurate it is and the degree of uncertainty associated with the estimates while the map was prepared. Any stakeholder for a soil map especially policy makers is interested in accuracy and uncertainty of the map before taking suitable land management decisions involving soil map as an input. Again the level of accuracy and uncertainty depends on the properties for which the map has been developed. If the property of interest is sensitive to environment or human consumption, then the accuracy level should be very high while the uncertainty level should be very low e.g. heavy metal concentration in soil, quality parameters of water etc.

Additive manufacturing is increasingly important for developing resource-efficient and customized products while reducing material waste associated with conventional manufacturing. This chapter examines the role of additive manufacturing in supporting green and sustainable production systems. It covers environmental benefits, economic and operational advantages, additive manufacturing technologies, sustainable applications, life-cycle assessment, environmental impact analysis, challenges, and design strategies. Industry trends, case studies, sustainability standards, and future research directions are also considered. In the context of green composites, additive manufacturing can support flexible fabrication and potentially improve material utilization and product customization. The chapter emphasizes the need to evaluate manufacturing technologies across their complete environmental and economic life cycle.

9.1 Introduction
Building upon our foundational understanding of statistical methods in meteorology, Chapter 9, “Advanced Analytic Techniques,” propels us into the forefront of data science applications within the atmospheric sciences. The imperative for these advanced methodologies stems directly from the evolving nature of meteorological data itself. Modern observations, numerical weather prediction (NWP) model outputs, and climate simulations generate an unprecedented volume of information, often arriving with incredible velocity and exhibiting immense variety in format and structure. Traditional statistical approaches, while robust for many tasks, often struggle to efficiently process such vast, high-dimensional datasets, or to fully capture the intricate, often non-linear, relationships inherent in atmospheric processes.
This chapter is dedicated to exploring why and how these sophisticated techniques are not merely supplementary, but increasingly essential. We will delve into how machine learning and deep learning algorithms excel at identifying subtle, complex patterns within massive datasets, enabling breakthroughs in areas like severe weather detection, nowcasting, and the bias correction of model outputs. We will also examine advanced spatial statistics, which provide powerful frameworks for explicitly modeling the intricate geographical dependencies of meteorological variables, crucial for accurate interpolation, downscaling, and understanding regional climate impacts. Furthermore, the chapter will introduce functional data analysis, a method particularly adept at treating continuous meteorological fields (like temperature profiles or pressure maps) as functions, allowing for a more holistic analysis of their evolution and variability. By embracing these advanced analytical techniques, meteorologists and climate scientists can unlock deeper insights, enhance predictive skill, more rigorously quantify uncertainties, and ultimately contribute to a more resilient society in the face of a changing climate. Prepare to expand your analytical toolkit and discover new frontiers in meteorological understanding.

Biomedical signals provide valuable information about physiological conditions and support continuous health monitoring. This chapter introduces ECG, EEG, EMG, PPG, respiratory and blood-pressure signals and examines the challenges associated with their acquisition and interpretation. It discusses noise removal, filtering, normalization, resampling and artifact correction before presenting time-domain, frequency-domain, time–frequency, wavelet and nonlinear feature-extraction methods. Feature selection and dimensionality reduction are considered in relation to model accuracy and clinical interpretability. The chapter also explores machine learning, deep learning and hybrid approaches through applications involving arrhythmia detection, seizure prediction and wearable cardiovascular monitoring, highlighting their importance in intelligent and personalized healthcare.

Renal failure is one of the most commonly presented conditions in veterinary clinics world over. Although the potential for recovery is favorable, most animals die due to multisystemic effects induced by azotemia. Hence, extracorporeal removal of endogenous and exogenous toxins is very essential for expanding the window of recovery. Hemodialysis has been used in treatment of veterinary patients for the last 50 years and is still one of the most expensive and technically demanding treatment modalities in veterinary medicine. It is an advanced extracorporeal renal replacement therapy which has recently transitioned from a clinical curiosity to a more advanced standard for management of acute kidney injury (AKI) and chronic kidney disease (CKD) in companion animals particularly dogs and cats.

In addition to eliminating selective toxins that cause AKI, regardless of renal function, hemodialysis is also effective in the removal of pharmacokinetically active soluble uremic substances in CKD to enhance life expectancy and quality of life (Singh, 2021). Other common indications of hemodialysis in veterinary medicine include fluid overload, dysregulated body fluid composition, drug overdose or intoxications (Foster, 2020; Singh et al?, 2024). Medical management alone is often insufficient to control the clinical signs and metabolic abnormalities associated with uremia when AKI is severe or when there is involvement of acute on chronic kidney disease. Consequently, animals are at risk of death within a short therapeutic window, despite the potential reversibility of AKI and the improvement of life expectancy in CKD.

Tissue Doppler Imaging

Doppler echocardiography relies on detection of the shift in frequency of ultrasound signals reflected from moving objects. With this principle, conventional Doppler techniques assess the velocity of blood flow by measuring high-frequency, low amplitude signals from small, fast-moving blood cells. In TDI, the same Doppler principles are used to quantify the higher-amplitude, lower-velocity signals of myocardial tissue motion.

There are important limitations to TD interrogation. As with all Doppler techniques, TDI measures only the vector of motion that is parallel to the direction of the ultrasound beam. In addition, TDI measures absolute tissue velocity and is unable to discriminate passive motion (related to translation or tethering) from active motion (fiber shortening or lengthening). The emerging technology of Doppler strain imaging provides a means to differentiate true contractility from passive myocardial motion by looking at relative changes in tissue velocity.

Age old practice of photography was taken with still camera in parallel direction, not aerially scan or snapping which really present wider prospective of any object or places in rearranging or planning skilfully in two dimensional working. Earlier aeroplane played a important role in over head condition to capture snaps but again accuracy and angle of camera limited the works because these photographs were diagonally or trigonally not matched the reality of ground.

Precision agriculture depends on information collected through ground sensors, satellites, IoT systems and other connected devices. However, these data sources differ in spatial resolution, timing, quality and technical compatibility. This chapter introduces a computational framework for acquiring, harmonizing and analysing heterogeneous agricultural data. Preprocessing methods address noise, missing values and inconsistencies, while machine learning, deep learning and ensemble models identify useful environmental, spectral and temporal patterns. Applications include crop-health assessment, soil-moisture estimation, yield prediction and climate-resilience planning. Edge and cloud computing support scalability and real-time operation. Integrating multiple data sources can improve model accuracy, robustness and the practical usefulness of intelligent farming systems.

Agriculture addresses the essential food wellspring of the world, and it has been confronting serious difficulties because of the rising interest for food items, sanitation, and security worries as well as calls for ecological insurance, water conservation, and maintainability. This improvement is anticipated to go on since the total populace is assessed to reach 9.7 billion by 2050. Since farming comprises the most noticeable illustration of water utilization around the world, it is normal that food interest and water utilization will decisively increment within a reasonable timeframe. Moreover, the rising utilization of composts and pesticides combined with the increase of cultivating exercises could prompt future ecological difficulties. Essentially, arable land is restricted, and the quantity of farmers is diminishing around the world. These difficulties highlight the requirement for imaginative and practical cultivating arrangements.
Consolidating novel innovations has been distinguished as a promising answer for address these difficulties. Brilliant cultivating and Precision farming have arisen because of such discussions. The previous is an overall idea for information communication technologies (ICT) and other state of the art developments in cultivating exercises to build productivity and viability. The last option centers around site-explicit administration in which the land is separated into homogeneous parts, and each part gets the specific measure of agricultural contribution for crop yield enhancement through original advances. Unmistakable advances that certainly stand out in this field incorporate Wireless Sensor Networks (WSNs), the Internet of Things (IoT), artificial intelligence (AI) techniques, including machine learning and deep learning.
Notwithstanding the previously mentioned innovations, remote detecting has been viewed as a mechanical device with high potential to work on savvy and Precision farming. Satellites, human-maintained airplane, and robots are well known remote-detecting advances. Drones, popularly known as Unmanned

In an era defined by increasing climate variability and the urgent need for sustainable food production, the science of Agricultural Meteorology has risen to unprecedented importance. This chapter, “Agricultural Meteorology: An Essential Associate for Natural Farming,” synthesizes the principles and applications we have explored, highlighting their critical role by providing timely, location-specific insights into weather and climate - factors that directly influence crop growth, soil health, and ecological balance. Unlike conventional farming, which often relies on synthetic inputs to manage uncertainty, natural farming depends heavily on understanding and adapting to natural rhythms. Here, agrometeorology becomes an indispensable ally.
Natural farming systems are sensitive to changes in temperature, rainfall, humidity, wind, and solar radiation. Agrometeorology helps farmers monitor and interpret these parameters, enabling them to make informed decisions such as when to sow, irrigate, mulch, or harvest — all without chemical interventions. For instance, knowing the onset of monsoon or forecasted dry spells helps in planning sowing operations or preparing natural plant protection measures like cow dung-based formulations.

Introduction

Agriculture contributes to 29.93% of India's GDP with a share of 27% of the world's agriculture production. India is ranked 6th in the world. In Rajasthan significant portion of economy of is agrarian. The agriculture sector of the state accounts for 22.5% share of agriculture in GSDP which is estimated to 18.39% in 2009-10. About 80% of the total population resides in rural area and largely dependent on agriculture as the source of their livelihood. The hot arid region of western Rajasthan occupies 61% of the total geographical area and 39.51% population of the state. During the last four decades the burgeoning human and livestock population and increased societal demands for land based products led to  significant expansion and intensification of agriculture by putting even marginal lands under plough, excessive exploitation  of ground water and importing of surface water for irrigation.

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

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.