eChapters

Abstract

Sequence alignment is the positioning of DNA, RNA, or protein sequences with each other to identify the regions of identity, which may be the result of functional, structural, or evolutionary relationships between the sequences. Therefore, sequence alignment is the process of aligning two sequences to achieve the maximum levels of identity and conservation. This chapter deals with the basic concept of sequence alignment and the tools associated with multiple sequence alignment. Further, it describes various matrices used in sequence alignment.

Series of experiments are conducted at different locations, times, or combinations of locations and years to evaluate the spatial and temporal performance of treatments. Such experiments are especially important in forestry and agricultural research when conclusions are intended to apply across wider geographical or environmental conditions. This chapter explains combined analysis of a series of experiments after individual experiments have first been analysed according to their respective designs. Particular attention is given to testing the homogeneity of error variances, including Bartlett’s test. When variances are homogeneous, pooled analysis can assess treatment effects, location or year effects, and treatment-by-location or treatment-by-year interactions, thereby evaluating consistency of treatment responses.

The earlier view was that the cultivated sesame originated from Ethiopia. But Bedigian and Harlen (1984) established evidence to indicate that sesame is native of India. In vedic period, liberal mention about Til (Gingelly seed) and Taila (Gingelly oil) are mentioned. The Charred seeds of Gingelly were extracted from Harappa (2500-1500 B.C).

Introduction

Research setting is basically a functional social ecology along with research objectives and condition wherein all possible socio-economic and communication processes are generated, and again gets regenerated. The researcher seeks to throw a spotlight on the implications and behavioral patterns of people who reside in the subject region and who are usual of the greater community. In this chapter, an in-depth review of the socio-demographic background of the local population in a rural setting has been provided.

4.1. Area of the study

The present research study was taken up in two states, Tamil Nadu and West Bengal.

Shelf life is a critical measure of ice cream quality and depends on formulation, processing, hardening, packaging, storage, and distribution conditions. This chapter explains the concept of shelf life and follows the movement of ice cream through the storage and distribution chain. It identifies temperature fluctuation and thermal abuse as major causes of quality deterioration, particularly through ice recrystallization and structural damage. The chapter examines hardening, formulation stability, cold-chain controls, shelf-life KPIs, and formal shelf-life studies. Common defects and their causes are also discussed. By understanding how temperature and handling influence product stability over time, manufacturers can establish better controls and protect texture, flavour, appearance, packaging integrity, and consumer acceptance.

A simple random sample (SRS) of size n consists of n individuals from the population chosen in such a way that every set of n individuals has an equal chance to be the sample actually selected.

A simple random sample is a subset of a statistical population in which each member of the subset has an equal probability of being chosen. An example of a simple random sample would be the names of 25 employees being chosen out of a hat from a company of 250 employees. In this case, the population is all 250 employees, and the sample is random because each employee has an equal chance of being chosen.

Learning objectives

1. Recognise inflammatory cell types

2. Recognise common skin pathogens

3. Diagnostic test are best for each case

4. Gain experience in interpretation

5. Recognise important artefacts

″The most crucial step towards healing is having the right diagnosis″. While approaching a case, a clinician should follow appropriate steps in a systematic way for accurate diagnosis of dermatological disorders. A careful history is a first step to choose appropriate diagnostic tests.

By 2050, the global population is projected to reach approximately 9.8 billion, requiring a 70% increase in food production to meet the growing dietary demands. The livestock sector will play a crucial role in addressing this rising need through sustainable animal production, contributing to food security, poverty alleviation, public health, and food safety. To tackle the challenges posed by population growth and to achieve the United Nations Sustainable Development Goals (SDGs), technological advancements and innovations will be essential in transforming the food and agriculture sectors.

In light of these challenges, Veterinary Services are more critical than ever. They are essential for enhancing livestock health and productivity, ensuring high-quality food that meets safety standards, and mitigating risks associated with animal diseases and public health. Additionally, Veterinary Services must contribute to global efforts in addressing issues such as food security, antimicrobial resistance, climate change, and the depletion of natural resources.

Smart farming, otherwise called smart agriculture, uses progressed agriculture advancements to improve ranch the farm management. By consolidating Industry 4.0 advancements, Precision farming achieves doable outcomes like extend production, resource insurance, and lessened petroleum derivative result. Internet of Things (IoT) accepts a fundamental part in Smart farming, by Precision agriculture to screen and deal with things, as a matter of fact. Information examination is in like manner essential for isolating huge encounters from the colossal proportion of data created by IoT devices. The reconciliation of present day modern digital technology has change into smart farming as information empowered and information driven activity. After affiliation, further examination of each IoT and data analytics frameworks in the agricultural region can be positive.
The total population is predicted to increase by 10 billion in 2050, by enhancing interest for agricultural goods, and notable food. This has brought about a developing interest supply befuddle. Issues including restricted assets, natural changes, and work deficiencies increment the jumble, endangering food security and agricultural production. To defeat these worries, farming should be feasible and utilize state of the art innovation. One important choice is “smart farming,” that attempt to increased food productivity by meeting the population demand in a sustainable manner. This is steady with the Assembled Countries’ Sustainable Development Goals (SDGs), which hold back nothing security utilizing reasonable ways.
Smart farming strategy for agriculture business had changed Ancient farming into smart agriculture. Ancient times, human cultivated the soil to provide food for livelihood. Such activity is known as agriculture, and it follow a long path between agriculture 1.0 to agriculture 4.0. Producers employed agricultural implements including pitchforks and sickles for cultivation throughout the 17th century, a period called as Agriculture 1.0. Farming practices, a huge number of workers are needed to conduct agricultural tasks. Moreover, agricultural

11.1 Introduction

The application of greenhouse in agriculture is vast. Greenhouses are used for growing flowers, vegetables, fruits, transplants and varieties of certain crops in a controlled environment which is appropriate for the plants. The edible flowers are served in some good restaurants to grow them precise calculation is required. Sensor network can help to monitor and automate the activities in greenhouse. The objective of this chapter is to discuss the development of sensor nodes to monitor the growth of flower plants through XBee and internet of things. The system comprises of three sections- sensor nodes, local server and main server. Sensor nodes communicate to local server with XBee and then local server communicates the same data to main server through IoT.Fig.11.1 shows the generalized block diagram, sensor node is to be implemented on each flower pot and communicating to local and main server.

Water system is considered an Arti?cial usage of the water on the dirt utilizing various strategies like siphons, cylinders and showers. Typically, the requirement for water system shows up where the precipitation is unpredictable, in dry times, or where lack of hydration is customary. An excessive number of water system frameworks are accessible with various sorts as indicated by the Climate of the dirt. The water utilized in water system has numerous assets, like underground water, through wells or springs; the surface water, from lakes, streams; or other a few sources, for instance, the treated wastewater or desalinated seawater. In this way, farmers need to save and safeguard their Agriculture water sources by limiting the capability of diseases. Since with any groundwater extraction, clients of water system water should be mindful so as not to deplete groundwater out with a rate more prominent than it is being recovered. There are two strategies of current water system systems, the conventional water system procedures and the Smart water system philosophies. Water system is like surface water system, trickle water system and sprinkler water system. Later on, a few extreme and confounded issues will be met by inundated agriculture. An illustration of a signi?cant issue is the low efficiency of the water assets for water system. An important safe guess is that over 40% of the diverted water system water is spent before at the level of the ranch, either through profound permeation or at the surface spillover. Nonetheless, frequently these misfortunes address botched open doors for water, as they keep water from showing up at the downstream redirections. One of the most noticeable coming issues is the expansion of various water prerequisites assets, for example, manufactural and metropolitan requirements. These are utilized to give water assets a higher worth, so as a result, researchers favor concentrating entirely on rehearses with a high inefficient rate. s worldwide populace is expanding quickly, rural efficiency in drylands needs improvement to fulfill food security needs. Subsequently, taking on Precision water system approaches is a suitable choice to more readily use the accessible water assets and further develop water efficiency in drylands. Water shortage has become one of the basic issues and undermines the supportable improvement

Efficient irrigation is essential for maintaining crop productivity while conserving increasingly limited water resources. This chapter explores the application of artificial intelligence to smart irrigation systems and irrigation scheduling. It considers water balance and evapotranspiration modelling and examines AI architectures used for irrigation decision-making. Sensor technologies and Internet of Things integration provide continuous information about soil, weather and crop conditions. Optimization algorithms can use these data to determine appropriate irrigation schedules and improve water-use efficiency. The chapter also discusses emerging AI-based irrigation approaches and global case studies. Challenges associated with implementation, data requirements, system limitations and ethical considerations are examined, followed by future directions for intelligent and sustainable irrigation management.

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 breeding is a revered custom that proceeds with the most common way of creating further developed plant cultivars, which traces all the way back to the beginning of agriculture . People started to observe levels of greatness among the plants in their fields not long after the underlying regulations of cereal grains, from which they put away the seeds of the best to develop new harvests. Early plant-reproducing advancements were the trailblazers to such simple choice methodologies. Individuals from the whole way across the world investigated and developed almost 7000 assortments of food plants during Breeding 1.0, which started quite a while back. Breeding stage 2.0 began in the late nineteenth and mid 20th hundreds of years when an inbreeding discouragement was found. During this time, many advances in plant breeding were made in the study of reproducing itself, like duplicated field preliminaries, controlled intersections, factual examinations, formal exploratory plans, crossover breeding, family-based appraisals of reproducing values, and exact yield estimation at scale (e.g., with Muldrow joins). Breeding 3.0 came to around a long time back, when sub-atomic markers and genomic information started to enhance phenotypic information. Breeding 4.0 is quickly drawing nearer, with immense omics information and the fast advancement of informatics innovations. Starting from the start of the plant breeding cycle, beginning from crop training, new methodologies interceded by various mechanical upheavals are being advanced in the science consistently to build the speed, exactness, and Precision in plant reproducing. In the previous 10 years, this science has proactively brought the green upheaval by creating semi-predominate, supplement responsive, and crossover cultivars. In any case, taking into account the populace development, diminishing arable land, and Climate changes, these have requested more exact, high throughput moves toward that can intervene to foster harvest cultivars at a more prominent speed, with higher exactness, and accuracy. In such manner, the field of Artificial brainpower has as of late arisen, which has been recommended to have unprecedented potential to help with breeding Climate versatile Precisionyields. Climate strong harvests keep up with or increment crop yields

The adoption of artificial intelligence is transforming agricultural production, management and value chains while creating significant economic and social implications. This chapter examines the socio-economic dimensions of AI adoption in agriculture. It considers economic impacts, changes in labour and employment, rural livelihoods and the accessibility of digital technologies. Particular attention is given to the digital divide and barriers that may limit adoption among different agricultural communities. The chapter also addresses policy, governance and ethical considerations surrounding AI deployment. AI applications across agricultural value chains and regional and global perspectives are discussed. Finally, the chapter considers future socio-economic transformations, research gaps and the requirements for responsible and inclusive agricultural digitalization.

Introduction

The natural resources of arid regions particularly soil and water are limited and is often in a delicate environmental balance. Desert encroachment due to lack of conservation planning, and the dangers of destroying or depleting beyond recovery these productive resources, are evident at present time and may be disastrous if development is based on short term expediency rather than long term stability.

The arid zone of India is spread over 38.7 million ha area, out of which 31.7 m ha in under hot arid region and 7 m ha under cold region. The hot arid region occupies major part of north-western India (28.57 m ha) and occurs in small pockets (3.13 m ha) in south India. The north-western arid region occurs between 22o30' and 32o05' N latitudes and from 68o05' to 75o45' E longitudes, covering western part of Rajasthan (19.6 m ha, 69%), north-western Gujarat (6.22 m ha, 21%) and 2.75 m ha (10%) in south-western part of Haryana and Punjab (Faroda et al. 1999). Rainfall distribution is highly uneven over space and time (CV>60%). The region receives low rainfall (<100 mm to 500 mm), has high evapotranspiration and high temperature regime (Rao and Singh 1998). Groundwater is deep and often brackish. The western-central area is devoid of drainage system and surface water resources are meager (Fig. 20.1). Due to low and erratic rainfall, replenishment of water resources is also very poor. The entire Rajasthan state is being categorized as the driest state and water scarce (having per capita water availability below 1000 m3 year-1) since 1991 in the country. Increasing pollution by industrial units, big and small, unregulated mining and even over-extraction of water from deep wells also add to the water quality problem in number of districts. Rapid urbanization and industrialization make such existing differences even more

Introduction

In India, hot arid ecosystem covering an area of 31.7 million ha lies at north western part of the country and 62% of this area falls in western Rajasthan covering 12 districts i.e. Jodhpur, Jaisalmer, Jhunjhunu, Sikar, Hanumangarh, Sri Ganganagar, Bikaner, Barmer, Pali and Sirohi. The major landforms in arid western Rajasthan are dune and inter-dune plains having sandy to sandy-loam textured soils. Temperature remains high for most part of the year reaching up to 42-45°C during prolonged summer months (April to July), whereas annual rainfall is very low and highly sporadic (100-400 mm) mainly occurs during August and September. Annual potential evapotranspiration ranges from 1500-2000 mm. Loss of fertile top soil from denuded soil surface through wind erosion is frequent during summer months. The region is subjected to frequent occurrence of droughts, once in every three years, which further intensifies the process of desertification (Samra et al. 2006). Shorter growing period, sandy soils, very low available water capacity, inadequate nitrogen and phosphorus availability are the major constraints. Soils are prone to severe wind erosion, terrain deformation and nutrient depletion. The region is mostly mono cropped and double cropping is being practiced extensively in canal and tubes well command area. Extensive fallowing in the past even in very good rainfall years because of uneven undulating topography and severe windstorm are the cause of concern. Ground water is deep and is usually brackish, declining at alarming rate due to excessive withdrawal for irrigation and other purposes. The region, however, sustains on perennial vegetation of Prosopis cineraria, Tecomella undulata, arid shrubs and grasses, which are rich resources of fodder for animal because cropping is affected by vagaries of monsoon. During last two to three decades, a 2 to 3-fold increase in irrigated areas with cultivation of both kharif and rabi crops has been recorded and most of these increments are resulted through conversions from rainfed areas, grazing lands and sand dunes into irrigated areas.

Soil health determines agricultural productivity, resource efficiency and long-term sustainability. Conventional laboratory and field-assessment methods provide valuable information but can be time-consuming and difficult to scale. This chapter presents a computational approach combining soil sensors, remote sensing and historical agronomic records. Indicators such as moisture, nutrients, pH and organic matter are processed using data-cleaning and feature-extraction techniques. Random Forest, Support Vector Machine and neural-network models estimate soil-quality parameters and identify degradation patterns. Explainable artificial intelligence improves the transparency and usefulness of predictions. Integration with edge computing enables real-time soil evaluation and supports targeted fertilization, efficient irrigation and sustainable soil-management decisions.

Introduction

Microorganisms generally present in all the environments are the most abundant and diverse living organisms present on earth. Microorganisms have great impact on the biosphere due to their inbuilt ability to transform the key elements of life into forms usable by other organisms and plants. Because of heterogeneity in nature of soils from variations in physical, chemical and biological properties the various natural habitat are microbially most diverse environments on the earth maintaining the stability between complex microbial communities and their function (Daniel 2005). One gram of soil contains about 10 billion microorganisms of possibly thousands of different species but microbial diversity in general, and particularly in extreme environments, and its role in ecology is poorly understood (Rossello-Mora and Amann 2001).

Deserts present a unique environment with very little precipitation and large variations in day and night temperatures, making it inhospitable environment for living organisms. Still a number of microbial species do survive in those harsh environments. The populations of aerobic bacteria in deserts across the world are reported to vary from < 10 in Atacama desert to 1.6 × 10⁷ cfu g^–1 in soils of Nevada (Skujnis 1984), and India is not exception to that with the bacterial population in the range of 1.0 × 10³ to 8.35 × 10⁶ cfu g^–1 sediment or ml^–1 water in the samples collected from cold deserts of north western Himalayas (Yadav et al. 2015).

Introduction

Over the past few decades, soil resources have witnessed many changes driven by demographic pressure and climate change. It is necessary to have up to date soil information to monitor the state of soils health with time. Soil resource inventory provides information about the potentialities and limitation of soils for its proper utilization. Soil survey offers an accurate and scientific inventory of different soils, their properties and extent. An in-depth knowledge of the type of soils and their spatial distribution is a prerequisite for developing rational land use planning. Remote sensing is the science of obtaining information about the earth’s surface without actually being in contact with it. On the other hand, proximal sensing refers to remotely sensed measurements that are taken at the field or laboratory level. Both these processes involve an interaction between incident radiation and the targets of interest. Fundamentally, remote sensing data products are a representation of the functional response of objects to energy sources. These are typically spectra, which are acquired as a discrete series of reflectance measurements taken at different wavelength intervals, or bands. In the shorter wavelengths of the electromagnetic spectrum (visible part), features can be detected by virtue of reflected solar energy, while in the longer wavelengths (microwave, thermal parts), sensing of emitted energy works. Conventional soil sampling, and subsequent laboratory analysis are generally time consuming and costly. In this context, remote sensing plays a crucial role for studying soil resources. The term remote sensing is generally used for airborne and space borne acquisitions, whereas proximal sensing refers to ground-based laboratory and field measurements. Advances in the quantitative disciplines like remote sensing and proximal sensing have laid the foundations for a spatial exploration of soil-system dynamics within a landscape context (Pennock and Veldkamp 2006).