eChapters

1. Introduction
Nanotechnology, which involves manipulating and controlling matter at the nanoscale, offers innovative solutions for enhancing soil quality, nutrient management, and remediation of contaminated soils (Lal et al., 2019). According to the Food and Agriculture Organization (FAO, 2015), approximately 33% of global soils are degraded due to erosion, nutrient depletion, and chemical contamination. This degradation poses a severe threat to food security and environmental sustainability. Moreover, the world population is projected to reach 9.7 billion by 2050, placing further pressure on agricultural systems to produce more food without degrading soil resources (Tripathi et al., 2019). In this context, nanotechnology presents a promising avenue for sustainable soil management. The unique properties exhibited by nanomaterials enable precise monitoring, targeted delivery of nutrients, and effective remediation of soil contaminants. Nanosensors equipped with nanomaterials and nanoparticles allow real-time monitoring of soil parameters, enabling farmers to make informed decisions regarding irrigation, fertilization, and pest management (Gogos et al., 2017). For instance, a case study conducted by Smith et al. (2021) demonstrated the effectiveness of nanosensors in monitoring soil moisture levels and optimizing irrigation practices, resulting in a 20% reduction in water usage while maintaining crop yield.
Furthermore, nanotechnology offers innovative solutions for nutrient management in agricultural soils. Nanoencapsulation techniques allow controlled release of fertilizers, minimizing nutrient leaching and improving nutrient uptake by plants (Lal et al., 2019). In a case study by Johnson et al. (2020), the application of nanoencapsulated fertilizers resulted in a 15% increase in nutrient use efficiency and a reduction in fertilizer runoff by 30%, thus improving soil health and reducing environmental pollution.
Moreover, nanomaterials exhibit high sorption and catalytic properties, enabling efficient removal of heavy metal contaminants and organic pollutants from soils, thus mitigating soil contamination risks (Wang et al., 2022). A case study conducted by Chen et al. (2019) demonstrated the successful remediation of a heavily contaminated soil site using iron oxide nanoparticles, resulting in a significant reduction in heavy metal concentrations and improvement in soil health indicators.

1. Introduction
Precision farming, often referred to as precision agriculture, is an advanced agricultural management approach that leverages technology and data-driven strategies to optimize various aspects of farming operations. At its core, precision farming involves the precise measurement and control of variables like soil conditions, crop growth and environmental factors to enhance productivity, sustainability and resource efficiency. This approach encompasses use of a wide range of automated technologies, including Global Positioning System (GPS), Geographic Information Systems (GIS), remote sensing, sensor networks and automated machinery.
The significance of precision farming lies in its potential to revolutionize agriculture by addressing sustainability and conservation of resources. Precision farming is able to tailor farming practices to specific field conditions. This sitespecific management approach minimizes resource wastage, incumbent costs and environmental hazards. It enhances crop management through real-time monitoring and data analysis. By the use of GPS fitted automated machinery, drones and robotic harvesters and advanced decision support system labour costs are reduced and efficiency is increased (Gebbers and Adamchuk, 2010).By employing soil sensors, and satellite imagery, farmers can gain valuable insights of soil moisture, nutrient levels and pest infestations which helps in irrigation scheduling and adequate fertilizer application (Srinivasan et al., 2019). In contemporary agriculture, the need for sensor-based approaches in soil management has become increasingly apparent. These approaches harness cuttingedge technology and data-driven solutions to optimize soil health, crop production and resource utilization. One of the primary advantages of sensor-based approaches is their ability to monitor soil moisture levels. Soil moisture sensors, such as those based on time-domain reflectometry (TDR) or capacitance technology, provide continuous data on soil moisture content at different depths (Dzikiti et al., 2012). In addition to monitoring moisture and nutrients, sensors can assess other vital soil parameters. Soil pH sensors help maintain the optimal pH level for crop growth, as soil pH can affect nutrient availability. Compaction sensors identify areas of soil compaction, allowing for targeted tillage practices to alleviate compaction and improve root growth. These are just a few examples of how physical parameter sensors can address soil health challenges, as documented in studies by Marchetti et al. (2018) which emphasizes the role of sensors in mitigating soil erosion. Chemical parameters sensors like soil nutrient sensors, such as ion-selective electrodes, can measure key nutrients like nitrogen, phosphorus and potassium. This information enables farmers to tailor their fertilization practices with pinpoint accuracy through variable rate technology (VRT) (Zhao et al., 2018). Moreover, sensor data can be integrated with Geographic Information Systems (GIS) and global positioning systems (GPS) to create detailed soil maps, aiding in sitespecific management strategies.

1. Introduction
Soil hydrothermal regime refers to the distribution and movement of water and heat within the soil. It describes the patterns of soil moisture and temperature variation over time and space. Understanding the hydrothermal regime is important in agriculture, ecology, and engineering, as it affects various soil processes, plant growth, and the behaviour of soil-related structures. The hydrothermal regime of soil is influenced by several factors, including climate, topography, vegetation, soil properties, and land use practices. The movement of water within the soil is driven by precipitation, irrigation, evaporation, and plant uptake (Aggarwal et al., 2009). It can be affected by soil physical properties such as soil texture, structure, porosity, and permeability. Water moves through the soil in response to hydraulic gradients, which are determined by the difference in soil water potential. Soil temperature is influenced by solar radiation, air temperature, and heat fluxes at the soil surface. It varies with depth and can be affected by factors such as soil color, moisture content, vegetation cover, and the thermal properties of the soil. The hydrothermal regime of soil has significant implications for plant growth and development. It affects processes such as seed germination, root growth, nutrient availability, and microbial activity. It also plays a crucial role in soil erosion, groundwater recharge, and the movement of pollutants within the soil profile. Soil’s thermal properties are pivotal in determining the microclimate, which subsequently influences critical stages like seed germination, the growth of seedlings, and the successful established of a crop stand. These properties are vital for modelling the movement of energy, water, and nutrients in soil. Soil thermal regimes are shaped by static properties such as texture and mineralogy and dynamic properties including water content, compaction, organic matter, and porosity. These dynamic properties including water content, compaction, organic matter, and porosity. These dynamic properties, significant to significant change during various farming operations can be adjusted with suitable soil and crop management strategies. Soil moisture content has a significant effect on the soil thermal conductivity. The relationship between soil moisture and thermal conductivity

1. Introduction
Water is the most crucial input for agricultural production. Globally, more than 80% of all freshwater used by humans are consumed in agriculture and most of it are for crop production (Morison et al., 2008). Currently about 60% of production in developing countries is derived from rainfall. Though irrigation provides only 10% of agricultural water use and covers just around 20% of the cropland, it can contribute about 40% of total food production since productivity of irrigated land is almost three times higher than that of rainfed land. The Food and Agriculture Organization (FAO) has predicted a net expansion of irrigated land of about 45 million is hectares in 93 developing countries (for a total of 242 million hectares in 2030). It projected that water withdrawals by the agriculture sector will increase by about 14% during 2000 – 2030 to meet the growing food demands (FAO, 2006). It is estimated that by 2050 in India, about 22 % of the geographic area and 17 % of the population will be under absolute water scarcity. The per capita availability of water which was about 1704 cubic meter in 2010 is projected to be 1235 cubic meter in 2050 (Anonymous, 2011). Infrastructure to increase the command area and minimize the gap of irrigation potential created and utilized is the dominant component of overall investment in agriculture (Anonymous, 2011). Besides this, judicious agricultural water management assists in ensuring good returns through efficient use of fertilizers and high yielding crop varieties. Above all, water will remain as a critical input for attaining sustainability in agricultural production. India’s current water supply is approximately 740 billion m3, which necessitates implementation of judicious water management technologies to enhance crop water productivity. In recent decades the world’s population has shown tremendous growth from an estimated 2.5 billion in the 1950’s to approximately 6.7 billion till date. It is expected that reduction in the average size of land holding, declining per capita water availability, deterioration of water quality etc. will seriously affect the sustainable use of water resources and will make it difficult to accomplish the target of producing 345 Mt of food grain in 2030 and 494 Mt in 2050 AD. To achieve the targeted food production, increase in the crop productivity from the present 2.3 to 4.0 t ha-1 under irrigated conditions and from 1.0 t ha-1 to 1.5 t ha-1 in rainfed area, productivity need to be increased at individual field level. This demands efficient use of water in agriculture, which can be achieved by reducing the losses of water and increasing the water uptake by the crops and it requires quantification of different water budgeting parameters like evapotranspiration and deep percolation.

1. Introduction
Ecosystem Services (ES) are actually the benefits human beings derive from their interaction with nature (Reyers et al., 2013; Ernstson, 2013). Around 400 BC, Plato observed how important service provided to Attica by the forests and subsequent loss of forests lead to drying spring and soil erosion (Mooney and Ehrlich, 1997). Thus, the work of Plato highlights that how the people were even then aware of the critical services provided by nature much before the industrial revolution (Rapidel et al., 2011). These ES are categorised into mainly provisioning, supporting, regulating and cultural services as per the Millennium Ecosystem Assessment (MEA, 2005). In fact, the increasing population warrants more and more set of ES (Biggs et al., 2015). A combination of factors responsible for delivery of ES like geology, climate, ecosystem, management scenarios, society, techniques, skills etc. that led to ES in an agricultural ecosystem (Kroll et al., 2012; Swinton et al., 2007). The ordinary nature largely comprised of agricultural lands (i.e., intensive crops, species poor, artificial pasture) that covers around 37.4% of total Earths’ land surface (FAOSTAT, 2016; Leff et al., 2004). Converting large tract of pristine lands into agricultural land and other competing uses considered to be one of the main reasons for biodiversity loss and land degradation (Tilman et al., 2001). Thus, meeting the challenge of working with limited arable land to feed our population in an eco-friendly manner requires our proper understanding of processes/mechanisms involved in nutrient availability/dynamics including fixation/ release pattern and refining the best management practices (Kumar et al., 2022a). The production of food has been targeted at the cost of other ES like supporting, cultural and non-marketable provisioning services (Muller et al., 2016; Foley et al., 2005; MEA, 2005). There is complex interaction between ecosystem and societal changes in an agricultural landscape. One is intermittently connected to other. Thus, it requires thorough understanding of the processes and biophysical factors that affect ES research, in particular and social-ecosystem, in general (Reed
et al., 2013; Carpenter et al., 2006; Plieninger et al., 2013). The ES that contribute to the agricultural production are mostly linked to aspect of soil fertility, biological control as well as pollination (Bommarco et al., 2013; Duru et al., 2015).

1. Introduction
Soils stand out as an intricate and diverse ecosystem on our planet. Their significance extends beyond supplying 98.8% of the world’s food, encompassing a wide array of essential services such as carbon storage, regulation of greenhouse gas emissions, flood prevention, and offering support to burgeoning urban populations. However, it is crucial to recognize that soil is a finite natural resource. The unprecedented growth of the human population, escalating from 250 million in the year 1000 to 6.1 billion in 2000, with projections reaching 9.8 billion by 2050, along with the intensified practices of agricultural production, is exerting immense pressure on the sustainability of soils.
For long-term sustenance the concept of ‘Soil quality’ was propagated around three decades back from now in the United States of America, and further expanded to the scientists of European and Asian countries. Soil health and soil quality are two interweaving term that signifies the potentiality or capacity of soil towards mankind. These two words complement each other in such a way that one term automatically comes with another to determine both aspects i.e. agricultural sustainability and environmental quality in terms of plant, animal and human health (Haberern, 1992; Doran, 2002). Idowu et al. (2007) remarked that the term “soil quality” is more favoured by scientists, whereas “soil health” is a term favoured by farmers as it connotes a holistic approach to soil management. The inception and application of soil quality term was officially approved by Soil Science Society of America (SSSA). They defined soil quality as “The capacity of a specific kind of soil to function within natural or managed ecosystem boundaries, to sustain biological productivity, maintain environmental quality, and promote plant and animal health” as illustrated by Karlen et al. (1997). It is a complex composition of certain properties associated with physical (texture, bulk density, water holding capacity, infiltration capacity etc.), chemical (pH, EC, organic carbon, extractable macronutrient and micronutrient etc.) and biological (microbial activity and related parameters) conditions of soil (Prabha et al., 2020). There are primarily two key methodologies for assessing soil quality: the descriptive approach, which focuses on characterizing various aspects or attributes of soil quality, and the Indicative approach, which aims to identify the ability or capacity of an attribute in a desired manner. The comprehensive process of developing a soil quality index is guided by the 3-Is framework: the Identification of indicators, Interpretation, and Integration of selected soil quality indicators. It is important to note that the specific set of indicators utilized can vary between different soils, diverse agroecosystems, and from one crop to another. Additionally, climatic conditions and other abiotic factors may also significantly influence the assessment of soil quality.
The USDA has categorized soil quality indicators into four primary groups: visual, physical, chemical, and biological indicators. However, in the current context, the dominant indicators are physical, chemical, and biological. For a systematic assessment of soil quality, it is crucial to collect individual parameters and integrate them meaningfully. Consequently, integrated soil quality indicators

1. Introduction
In the realm of modern agriculture, achieving sustainability is paramount. The judicious use of resources and the maintenance of soil and crop health are vital components of this endeavor. Remote sensing is a powerful technique used to gather information about the Earth’s atmosphere and surface without direct physical contact (Lillesand et al., 2015). It involves the acquisition, measurement, and analysis of data from sensors aboard platforms such as satellites, airplanes, drones, or ground-based instruments. These sensors record electromagnetic radiation, providing valuable insights into various aspects of the Earth’s environment. Remote sensing technology has emerged as an invaluable tool for achieving these objectives, offering a range of benefits for sustainable agriculture (Jensen, 2009). With the advent of modern technologies such as hyperspectral remote sensing (HRS) that collects information in narrow contiguous bands, many new avenues of applications have come up. This detailed spectral information enables the precise identification and classification of objects based on their unique spectral signatures. Further, the narrow spectral reflectance provides significant information regarding the biophysical, biochemical, and physiological parameters of crops. These characteristics of HRS provides opportunities for improved feature identification, anomaly detection, and quantification. Often HRS data also complement to the remote sensing data, such as LiDAR or SAR (Synthetic Aperture Radar), allowing for a more comprehensive understanding of the Earth’s surface and its features. All these features of HRS enables precision agriculture, allowing the management practices as per the specific needs of the fields. By assessing soil and crop conditions remotely, one can apply resources like water, fertilizers, and pesticides precisely where and when they are needed. This minimizes waste and reduces the environmental impact of agricultural operations. Hyperspectral remote sensing can also assess critical soil parameters such as moisture content, pH levels, and nutrient concentrations. Monitoring these factors over time helps farmers make informed decisions about soil management and the choice of suitable crops. It also aids in preventing soil degradation and erosion. Early detection of stressors like pests, diseases, and nutrient deficiencies is crucial for maintaining crop health and maximizing yields. Remote sensing provides a timely and accurate means of monitoring crop conditions across large areas, enabling rapid responses to threats and reducing yield losses. In regions with water scarcity, efficient water management is essential. Remote sensing can track soil moisture levels and assess irrigation needs. By optimizing irrigation practices, farmers can conserve water resources while maintaining crop productivity. Remote sensing, particularly hyperspectral remote sensing can detect changes in

1. Introduction
Crop health valuation plays a vital part in modern agriculture as it positively stimulates food security, economic constancy, and ecological sustainability. This process involves monitoring and evaluating the growth and development of crops, finding possible stressors or diseases, and executing suitable management practices. Judicious and precise evaluation of crop health permits early exposure of diseases, pests, and other irregularities. Detecting these problems at their early growth stages can aid prevent extensive occurrences and decrease the need for unnecessary pesticide use, indorsing justifiable crop management practices. Assessing crop health on a regular basis help in enhancing yields by detecting factors that could inhibit crop productivity. Agricultural systems can be made resource-efficient by incorporating tools, modern technologies and information management systems that come under the precision agriculture concept. Modern scientific improvements, comprising Global Positioning System (GPS), grain yield monitoring system, Variable Rate Technologies (VRT), sensor networks, and Femote Sensing (RS), have assisted farmers to detect in-field heterogeneity of crop and soil and adopt site-specific farm management practices. Varying the application of inputs with VRT can decrease the cost of inputs and labor, increase productivity, and decrease the potential environmental influences from the overuse of farm inputs. However, the success rate of VRT needs precise and consistent information on the location and extent of crop and soil health conditions, such as crop nutrient shortages, soil and crop water deficiency, and injury caused by insect pests, weeds, or pathogens. So far, the dominant remote sensing technologies that the farming sector has used are visible (VIS), Near-Infrared (NIR), and short-wave infrared (SWIR) sensors. The use of thermal sensors in agriculture is to some extent narrow despite its wide use in the areas of medical science (Ring and Ammer, 2012), intelligence/military (Hinz and Stilla, 2006), and the food processing sector (Vadivambal and Jayas, 2011). By providing temperature variations of soil and crop canopy, thermal remote sensing has the prospective to be used for various

Introduction
With advancements in molecular biology, DNA-based technologies have shown great potential in enhancing the efficiency of crop breeding programs, preserving germplasm resources, improving the quality and yield of agricultural products, and protecting the environment. As a result, their role in modern agriculture is becoming increasingly important.
DNA is the main genetic materials of all cellular organisms; preserving DNA itself is one way of preserving germplasm resources (Geptset al.,2006). The advancement of biotechnology and molecular biology has made it possible to regulate or even control plant traits using DNA sequence information, including its structure, function, and mechanisms.DNA technologies based on DNA molecular markers, transgenic technology and gene expression have been widely used in agricultural production which have showed great potential in improving agricultural yields and quality, reducing the loss that various biotic and abiotic stress caused, promoting the utilization of germplasm resource, improving breeding efficiency and strengthening the regulation of plant growth. (Garciaet al.,2005)
These modern DNA technologies, with their high feasibility and necessity, play a crucial role in ensuring the sustainable development of agriculture. Although agriculture encompasses both plant and animal production, DNA technologies in these fields share similar technical purposes and types. Therefore, this study focuses on reviewing the agricultural applications of DNA technologies by exploring their utilization in plant production. Numerous reports have documented the application of various DNA technologies in agricultural production. However, as these technologies continue to evolve and advance, it becomes challenging to cover every DNA technique in detail..DNA technology holds immense promise for the future of crop identification, enabling precise and efficient breeding, accelerating the development of improved varieties, and contributing to sustainable food systems. Here’s a breakdown of the key future prospects:

Introduction
Identification of crop varieties is one of the major tasks required in modern agriculture, plant breeding, and seed certification systems. The correctness of identification ensures that the varietal purity is sustained, advocates the rights of plant breeders, and guarantees quality seeds that will perform expectedly under specified agro-climatic conditions to farmers (Yu and Chung, 2021). The growing number of crop varieties due to the intensive breeding programmes all over the world makes it equally important to be able to differentiate between cultivars. Variety identification serves several purposes, such as:
• Seed Quality Assurance: Used to prevent the sale of spurious or mislabeled seeds.
• Intellectual Property Rights (IPR): Legal protection of new plant varieties by acts like the Plant Variety Protection (PVP).
• Biodiversity Conservation: Tracking and documenting existing genetic resources.
• Regulatory Compliance: Aids in Distinctness, Uniformity and Stability (DUS) testing protocols.
• Traceability in Food Supply Chains: Ensures safety for consumers and compliance with labeling standards.
Thus, crop variety identification forms the basis of integrity in agricultural systems and is the coordination mechanism for protecting innovations in plant breeding with regulatory frameworks both at national and international levels (Jones et al., 2013; Kage et al., 2016; Cockram et al., 2012).

Introduction
The accurate identification of crop varieties is crucial for agriculture, plant breeding, and intellectual property protection. DNA-based techniques have emerged as powerful tools for distinguishing between crop varieties due to their precision and reliability. However, several challenges persist in the adoption and implementation of these techniques. Conventionally, morphological descriptors are routinely used for establishing the identity of varieties. But these morphological descriptors suffer from many drawbacks such as influence of environment on trait expression, epistatic interactions, pleiotrophic effects etc. Furthermore, the paucity of sufficient number of these descriptors for unequivocal identification of increasing number of reference collection of varieties enforces to look for alternatives. Electrophoresis of seed proteins and isozyme analysis have overcome these limitations to some extent but now many powerful DNA based techniques are available. Since all genetic differences between individuals are laid down in the primary sequence of their genomic DNA, the straightforward method of identification of crop varieties and parental lines would be to determine this sequence for the genome under comparison. Sequencing requires huge monitory investment and it is practically impossible to sequence the whole genomes of all varieties. But with the automation in sequencing now possible, short stretches of amplified DNA can be sequenced. However, a more practical strategy would be to limit the comparisons to specific regions of the genome, which frequently differ between individuals.

Introduction to Molecular Markers in Vegetable Breeding
As global demand for food continues to rise, the agricultural sector faces mounting challenges from climate change and resource limitations. These pressures necessitate an urgent transformation in farming practices, making molecular markers vital for enhancing crop improvement strategies (Tester and Langridge, 2010). Traditional breeding techniques, which often rely on phenotypic selection and protracted breeding cycles, find it increasingly difficult to produce resilient, high-yield cultivars (Bernardo, 2016). Molecular markers, including Single Nucleotide Polymorphisms (SNPs), Simple Sequence Repeats (SSRs), and Amplified Fragment Length Polymorphisms (AFLPs), provide direct access to plant genomes, allowing for the precise identification and manipulation of desirable traits regardless of environmental variability (Varshney et al., 2005). This approach is essential for expediting breeding programs and enhancing genetic gains, ultimately leading to crops better suited for diverse and evolving climates (Jannink et al., 2010).Additionally, the integration of genome-wide association studies (GWAS) and genomic selection (GS) harnesses high-density marker data to deepen our understanding of complex trait genetics, fostering sustainable agricultural practices and securing future food supplies (Hickey et al., 2019).

Introduction
In the realm of modern agriculture and plant breeding, the integration of advanced biotechnological tools has revolutionized the traditional methods of crop improvement. Traditional approaches to genetically improving horticultural crops that are primarily propagated asexually encounter multiple challenges. These include an extended juvenile phase, the absence of seeds, a high level of intraand interspecific incompatibility, frequent heterozygosity, seed sterility, and, most importantly, the restriction of certain valuable traits to wild species (Mehlenbacher, 1995). Conventional or classical breeding techniques rely on natural processes and traditional tools to develop new plant varieties or cultivars. However, these methods are often time-consuming, labor-intensive, and less efficient. The lengthy process, coupled with the challenges of maintaining desirable traits, makes it difficult to achieve rapid genetic improvements. As a result, there is a growing need for more advanced and precise breeding methods to enhance crop development (Jain and Kharkwal, 2004). A molecular marker is a specific DNA segment that signifies genetic variations at the genome level, aiding in genetic analysis and breeding (Dhutmal et al., 2018). These are composed of nucleic acids as well as proteins and used on basis of naturally occurring polymorphism (Thottappilly et al., 2000; Ahmad et al., 2020). These markers are closely related with the target gene and they act as sign or flags (Nadeem et al., 2018), aiding in the identification of specific regions associated with advantageous characteristics such as disease resistance, yield potential, nutritional content, and environmental adaptability (Meena et al., 2023).The advancement of high-throughput molecular markers and genetic maps has enabled the precise identification of genes responsible for agronomically important traits, potentially enhancing breeding through markerassisted selection (MAS). Compared to traditional breeding methods, MAS overcomes the challenges of phenotypic selection and significantly improves selection efficiency. This approach accelerates crop improvement by ensuring the effective transfer of desirable traits with greater accuracy and reliability (Wani et al., 2022). The incorporation of next-generation sequencing (NGS) technologies and high-throughput genotyping platforms has also facilitated genome-wide association studies (GWAS) and the discovery of single nucleotide polymorphisms (SNPs) linked to important traits, further enhancing the resolution of genetic analysis. In recent years, molecular markers have been extensively used to characterize and enhance genotypic and phenotypic traits in temperate fruit and nut crops. Studies have focused on assessing genetic diversity in almonds, identifying genes responsible for red skin color in apples, developing sharka disease-resistant apricots, and sequencing the almond genome. These markers have been successfully used for genetic studies in many horticultural crops including vegetables (Gulsen et al., 2007; Kumar et al., 2008 a and b; Schafleitner et al., 2013; Yadav et al., 2015; Sharma et al., 2016; Kumar et al., 2017 and Kumar et al., 2019) and flowering crops (Kumar et al., 2019; Kumar et al., 2016 and Kumar et al., 2018a and b). These advancements highlight the crucial role of molecular markers in improving breeding strategies, accelerating genetic improvements

Introduction
DNA fingerprinting envisages a very significant molecular methodology employed for identification and characterization of wheat varieties and germplasm. It is of utmost importance in varietal authentication, rights protection of breeders, and analysis of genetic diversity. Currently employed molecular markers like SSRs, SNPs, and DArT are applied extensively for wheat fingerprinting. Such markers precisely differentiate closely related varieties and aid in breeding programs (Babu et al., 2020; Kiss et al., 2021).
The current standing of wheat DNA fingerprinting shows a trend toward high-throughput genotyping and genomic selection strategies to accelerate crop improvement. Future prospects would entail the integration of advanced sequencing technologies such as Next-Generation Sequencing (NGS) and machine learning for genetic analysis. These technologies will ease wheat breeding and dramatically contribute to food security and climate-resilient agriculture. Expanding the global DNA fingerprinting database would further enhance global wheat germplasm conservation and utilization (Sandhu et al., 2021; Ahmadi et al., 2020). Wheat (Triticum spp.) is one of the major staple crops of the world, supporting the sustenance of over one-third of the total world population. It is the main source of carbohydrates, proteins, fibers, and other important micronutrients, making it very important for human nutrition. Wheat is cultivated mainly in all types of agro-climatic zones, with major producers being China, India, Russia, United States, and the European Union.
Wheat is highly valuable not only in terms of food security, but also economically with millions of farmers involved in its production and trading worldwide. Wheat is an important raw material for many food products, such as bread, pasta, and baked goods. Wheat production faces challenges from climate change, pests, and emerging pathogens. Therefore, continuous research on wheat, genetic improvement for desirable traits, and sustainable agricultural practices are needed to enhance yield, resilience, and in turn global food security. Genetic diversity in wheat is pertinent for fulfilling food security at the global level and for the success of breeding programs, apart from giving diverse traits for resistance against pests, diseases, and environmental stresses like droughts

Introduction
Plant genetic resources (PGRS) encompasses everything that is on the earth related to the plants which are beneficial to mankind (Appazov,2008). So, it is the basic raw material for crop improvement (Somsenet al., 2002). In the present regime of transgenic crops, you can have ‘designer crops’- crops of your willingness (Wisner et al., 2001). From the heritage of mankind to the sovereign rights of the states this is the path that PGRs have traversed, earlier before CBD (Conventional Biological Diversity)(Chakraborty et al., 2018) PGRs were considered as a heritage of mankind freely available to everybody but after signing that they became the sovereign rights of the states with sovereign rights of the nation (Brahmiet al., 2015). In such a scenario where states have their rights on those plants’ genetic wealth, protection becomes very important (Clevelandet al., 1997). DNA fingerprinting is one of the technologies which can facilitate that protection (Wanget al., 2019).Rapeseed-mustard (Brassica spp.) is a vital oilseed crop cultivated extensively in India and other parts of the world (Kumaret al., 2008). It plays a crucial role in ensuring edible oil security and contributes significantly to the agricultural economy(Pahariyaet al., 2007). The genetic diversity within rapeseed-mustard species is a key factor in improving yield potential, disease resistance, and oil quality through breeding programs (Chandet al., 2021). However, accurately identifying and characterizing varieties is a challenge due to phenotypic similarities, environmental influences, and potential genetic erosion(Chhikaraet al., 2024). To address these challenges, DNA fingerprinting has emerged as a powerful molecular tool for precise varietal identification, genetic purity assessment, and germplasm conservation (Hosseinpouret al., 2024).DNA fingerprinting involves the use of molecular markers to generate unique genetic profiles of plant varieties, enabling breeders and researchers to differentiate between closely related cultivars (Singhet al., 2022; Singhet al., 2014). This technology is instrumental in breeding programs, ensuring the registration and protection of plant varieties under legal frameworks such as the Protection of Plant Varieties and Farmers’ Rights Act (PPV&FRA) in India (Williamet al., 2024). Additionally, DNA-based markers provide valuable insights into genetic relationships, aiding in the efficient selection of parent lines for hybridization and improving crop resilience to biotic and abiotic stresses (Nadeemet al., 2018; Haqueetal., 2021).Over the past two decades, several DNA fingerprinting techniques, including Random Amplified Polymorphic DNA (RAPD), Amplified Fragment Length Polymorphism (AFLP), Simple Sequence Repeats (SSR), and Single Nucleotide Polymorphisms (SNPs), have been widely applied to characterize rapeseed-mustard varieties (Nybomet al., 2014; Panjabietal.,2019) Advances in high-throughput genotyping and nextgeneration sequencing (NGS) technologies have further enhanced the resolution and accuracy of genetic profiling, paving the way for marker-assisted selection (MAS) and genomic-assisted breeding strategies (Sinhaet al., 2023).

Introduction
Legumes that produce dry seed suitable for human consumption are known as pulses, and they are agronomically valuable plants in the field and in the food chain. In the past ten years, grain legumes—particularly pulses—have become more popular as a means of addressing global agricultural concerns. (Bohra et al. 2015, Varshney et al. 2015).The best plant-based sources of dietary protein, as well as other minerals including iron, zinc, magnesium, and dietary fiber, are pulses. (Bohra et al.2014, Kouris-Blazos and Belski 2016, Maphosa and Jideani 2017). According to Simpson and Campbell (2015), a plant-based agricultural diet high in fruit or legume fiber helps to improve microbial diversity and has a beneficial effect on short-chain fatty acid levels, both of which are critical for preserving gut health. Because of their unique ability to fix nitrogen symbiotically with Rhizobium spp. in their root nodules, legumes not only improve nutritional health but also increase soil fertility (Graham and Vance 2003, Stagnariet al. 2017). The Leguminosae, often known as the Fabaceae, are the third largest family of flowering plants, behind the orchid (Orchidaceae) and sunflower (Asteraceae) families (Walters 1960), with 750 genera and 20,000 species (Polhill 1981).With a productivity of 1016 kg ha-1, 93.23 million tons (m t) of pulses are harvested worldwide from 91.77 million (m ha) of land (http://faostat.fao.org/). With a cumulative average productivity of 1030 kg ha-1, the major pulse crops—dry bean (primarily common bean), chickpea, dry pea (pea), cowpea, pigeonpea, lentil, and faba bean—share 92.58% (84.05 million ha) of the world’s total pulse production, or 92.82 percent (86.53 m t) (http://faostat.fao.org/). According to their ability to adapt to tropical and temperate agroclimatic conditions, pulse crops can be broadly divided into two groups: 1) warm season crops, such as common bean, pigeonpea, and cowpea, and 2) cool season crops, such as pea, chickpea, lentil, and faba bean (Cannon et al. 2009, Young et al. 2003, Zhu et al. 2005). Chickpea, pea, and lentil are regarded as one of the founding grain crops and helped start modern agriculture because of its early domestication (around 11,000 years ago) (Zohary and Hopf 2000).Over the past 60 years, the global area under pulses has increased from 64 to 91.77 million hectares (http://faostat.fao.org/), and numerous high-yielding varieties have been developed and released as a result of extensive research on conventional breeding for pulse improvement due to their high agricultural value (Singh 2005, Saxena 2008, Pérez de la Vega et al. 2011, Torres et al. 2011, Gaur et al. 2012).Pulses are less productive than other crops, particularly cereals, due to factors such growing in risky settings, unpredictable rainfall, extended dry spells, and susceptibility to a range of pests and diseases (Borlaug 1973, Varshney et al. 2013a).

Introduction
Maize (Zea mays L.) is one of the most widely cultivated cereal crops globally and holds significant economic and food security value across diverse agroclimatic regions. In India, its multifaceted utility as food, feed, and industrial raw material underscores its relevance in national agriculture. With the expansion of hybrid cultivation, climate-resilient breeding, and commercial seed markets, the need for precise and dependable varietal identification has become increasingly critical. Traditional methods of cultivar identification, which relied primarily on morphological characteristics evaluated under the Distinctness, Uniformity, and Stability (DUS) framework, have shown considerable limitations due to their environmental dependence, subjectivity, and limited resolution among closely related genotypes (Verma et al., 2023; Jamil et al., 2021). Morphological traits, although historically pivotal in classification, are often confounded by genotype-environment interactions, multigenic inheritance, and developmental plasticity. Consequently, accurate varietal authentication using such traits becomes impractical, particularly in crops like maize where morphological similarity among hybrids is frequent (Poets et al., 2020; Verma et al., 2023). This challenge has driven the development and adoption of molecular techniques such as DNA fingerprinting, which offers a robust, reproducible, and environmentindependent alternative for cultivar and germplasm identification (Jamil et al., 2021).
DNA fingerprinting involves the use of specific DNA markers to distinguish and characterize plant genotypes based on their genetic makeup. The concept, introduced by Paul Hebert in 2003, was originally proposed as a supplement to morphological taxonomy and has since evolved into a cornerstone of modern plant breeding and germplasm management. This technique has been instrumental in multiple domains of crop improvement, including genetic diversity analysis, heterotic grouping, marker-assisted selection, and varietal purity testing. Moreover, in the context of intellectual property rights and seed certification, DNA-based identification plays a vital role in enforcing breeder rights and ensuring seed quality in both public and private breeding sectors (Poets et al., 2020). Its utility is further underscored by its ability to detect essentially derived varieties (EDVs), resolve disputes in seed authenticity, and support biodiversity conservation efforts through accurate germplasm cataloging (Jamil et al., 2021; Verma et al., 2023).
Given these wide-ranging applications and advantages, the present chapter aims to comprehensively assess the evolution and current status of DNA fingerprinting technologies, with specific emphasis on their application in maize variety and germplasm identification.

Introduction
Rice is a key crop that directly feeds about half of the global population, with most of it consumed in developing countries (Wang et al., 2018). It is cultivated in many different types of environments and is always being enhanced by the creation of new varieties and the assessment and conservation of germplasm collections. To improve rice diversity and increase its genetic range, numerous researchers and organizations have been attempting to bring long-forgotten types back into production.This variation among varieties is essential for breeders as it provides genetic traits that can be incorporated into commercial varieties, such as resistance to diseases and pests, tolerance to abiotic stresses and improvements in grain nutritional quality, among others. In some cases, specific new traits areintroduced through crosses made with distantly-related genotypes. As a result of these efforts, despite being one of the first domesticated crops, cultivated rice continues to tap into a vast gene pool (Vieira et al., 2022).
There are over 500,000 rice accessions stored in gene banks around the world (Genesys 2021). To further explore the molecular diversity of this germplasm International Rice Research Institute (IRRI), Chinese Academy of Agricultural Sciences and BGI-Shenzheninitiated the 3K Rice Genome Project. This project focused on the most important rice species, Oryza sativa, and involved resequencing 3,000 rice genomes selected from the available genetic diversity, creating an invaluable resource for large-scale genetic variation discovery in this species (Vieira et al., 2022).
Varietal identification and classification in breeding programs are typically assessed using the distinctness, uniformity, and stability (DUS) approach, which relies on morphological descriptors (Tiwari et al., 2013). However, using morphological data/markers to determine genetic variation has limitations due to the labor-intensive, time-consuming nature of the process, as well as theagronomical traits and season-specific that are heavily influenced by ecologicalaspects. The development of genetically and morphologically similar varieties has made the task of clearly distinguishing between them increasingly complex. These environmental challenges necessitate the use of new techniques that are fast, reliable and environmentally independent.Biochemical and chemical tests fail to accurately estimate genetic distances among closely related genotypes. PCRbased molecular markers, however, are fast, reliable, and effective for determining the genetic purity of crop varieties (Rana, 2006). Hypervariable microsatellites (Teymnykhet al., 2011) offer a valuable source of polymorphic DNA markers, connecting genetic maps to genomic sequences and, ultimately, to phenotypic variation. SSR-based “DNA barcodes” are unique patterns of SSR polymorphism that assist in the precise identification of varieties, providing stability and resistance to environmental influences, epistatic interactions, and pleiotropic effects.

Introduction
As the seed market has become more globalized, seed trade has seen a rise in competitiveness as well as stricter quality requirements. Throughout the entire seed manufacturing process, thorough management is necessary to ensure high seed quality, from initial planning to final distribution. Among the various seed quality parameters, genetic purity is particularly critical in determining the authenticity and planting value of a seed lot. Genetic purity refers to the degree of purity within a seed lot, the genetic integrity of an F1 hybrid in a hybrid seed lot, or the difference between a submitted seed sample and the stated variety. During manufacturing, achieving complete (100%) varietal purity is challenging, even when recommended protocols are strictly followed. Outcrossing, partial off-type removal, and physical mixing during harvest, storage, or seed handling are a few examples of these factors can result in unintended varietal impurities (Bradford, 2006). Seed firms, producers, and suppliers conduct laboratory-based evaluations for internal quality control or post-control grow-out testing on seed lots to guarantee the necessary levels of purity. Assessments for genetically modified trait purity and the accidental inclusion of genetically modified (GM) seeds in non- GM seed lots are now necessary due to the introduction of transgenic varieties into the worldwide market, which has further broadened the scope of seed purity testing. Thus, seed purity testing is a cornerstone of agricultural production, ensuring that the seeds sown in the fields conform to their designated genetic identity. This process is essential for maintaining varietal integrity, meeting regulatory requirements, and ensuring high agricultural productivity (Sandra et al., 2023). The quality and genetic purity of seeds all directly impact crop productivity, pest and disease resistance, and overall farm profitability. Traditional seed purity assessment methods rely heavily on morphological, physiological, and biochemical traits. These methods include visual examination of seed characteristics, seedling grow-out tests, electrophoresis-based protein profiling, and chemical marker-based identification. However, these approaches often suffer from several limitations, such as the influence of environmental conditions on phenotypic traits, subjective assessments, and the need for significant time and labour. The accuracy and reproducibility of such conventional techniques can be further affected by variations in experimental conditions and the expertise of personnel conducting the tests.The advent of molecular biology has brought a paradigm shift in seed purity testing. DNA-based technologies provide precise, reliable, and high-throughput solutions that overcome the limitations of traditional methods. These molecular approaches allow for direct examination of genetic material, offering greater accuracy in varietal identification, hybrid seed purity assessment, and detection of genetic contamination. DNA markers can distinguish even closely related varieties, enabling seed companies, regulatory bodies, and farmers to ensure the authenticity of seed lots with high confidence. This chapter provides an overview of various DNA-based techniques employed in seed purity testing, including polymerase chain reaction (PCR)-based markers, single nucleotide polymorphism (SNP) markers, and high-throughput sequencing (HTS) technologies.

Introduction
Information on genetics is also crucial in agriculture research-related advancements in crop and livestock improvement, sustainability, and food security. Genetic information is analyzed by scientists so that important traits such as disease resistance, drought tolerance, and improved yield can be bred into plants and animals with many attributes. Selective breeding programs and subsequent genetic manipulation to improve agricultural production while reducing dependence on chemical inputs like pesticides and fertilizers are then developed (Bohra et al., 2020; Li & Yan, 2020).
Precision agriculture also boasts genetic data for informed decisions on the genetic composition of crops and livestock. Genetic data are thus used to optimize resource use as global demand for food resources continues to grow with increasing population needs against the backdrop of climate change resilience of crops and nutritional quality of agricultural products. Genetic studies also serve purposes of biodiversity conservation through ex-situ preservation of native and rare species presumed to have important genetic assets worthy for future breeding programs (Chaudhary et al., 2023; Varshney et al., 2021; Thudi et al., 2021).
Overview of Challenges in Analyzing Genetic Data
It may prove difficult for agricultural research to analyze genetic data because of its wide and varied capabilities:
Data Complexity and Volume
Advances in genomic sequencing technologies produce huge amounts of data that necessitate specialized computational machinery for processing. The most complex aspect of large-scale data is reliable storage, retrieval, and analysis.
Data Integration and Standardization
Genetic data comes from different sources and different sequencing techniques and institutes of research. Variations in format, methods, and terminologies complicate comparison of findings across studies. There are standardized protocols for collection and analysis of genetic data that ensure its consistency and reliability (Lassoued et al., 2021; Mulder et al., 2017).
Computational and Bioinformatics Challenges
Genetic data processing needs very high-performance computational resources and sophisticated bioinformatics tools. Most agricultural research institutions, particularly in developing countries, are poorly equipped with such technologies,