eChapters

Cloud-based software is used for the management of financial and field activities of farms. Prior to computers, farmers maintained data manually by keeping lengthy records on papers. This method was prone to human calculation errors. After the computer boom in the 1980s, it was not long before finance software such as Money Counts came to market. 

Introduction 
The importance of climate change, sustainable development, environmental concerns, and energy needs are gaining attention more than ever. The conventional energy procedures, such as solely dependent on fossil fuels are ended with greenhouse emissions, contributing to pollution and deficiency in natural resources. To overcome these straits, bioenergy has become the sustainable and promising alternative, also offering the declining nature of dependency on non-renewable resources and parallelly providing cleaner energy options (Khalid, 2024). Mere the production of bioenergy is insufficient to address the huge energy needs of the current situation. If sustainability is to be ensured means, then effective energy management is to be ensured. The newly developed smart energy management systems improve energy production, distribution, and consumption by utilizing cutting-edge technology like artificial intelligence (AI), data analytics, and Internet of Things (IoT) devices. Smart energy management is a notion that limelight on lifting the reliability and efficiency of energy systems. With the usage of current technologies, smart energy systems can audit real-time energy use, and energy demands and manipulate the power distribution accordingly. This required to improvising the potential of renewable energy (Hossain et al., 2024). Energy recovery is defined as the reusing of energy, that has the possibility to get waste in the following forms, heat, waste materials, gas and so on. In the bioenergy industry organic waste is converted into a suitable form of energy, which gives hybrid benefits like waste reduction and energy generation. Furthermore, a highly resilient and efficient energy system that meets sustainable development goals can be created by combining these recovery systems with intelligent energy management tools (Mariana et al., 2021). The investigation of how energy recovery technologies and intelligent energy management can improve the efficiency and sustainability of bioenergy systems is covered in this chapter. The potential of smart energy solutions to meet the increasing need for sustainable and renewable energy sources will be highlighted in this research through a thorough analysis of case studies, emerging trends, and existing technologies. A roadmap for the future of bioenergy will be provided, together with an identification of the main obstacles and possibilities that players in this quickly changing industry must contend with (Naeem, et al., 2024).

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.

Introduction

Recent advances in food technology have made our lives virtually easier and efficient. Changing lifestyle has led to the emergence of packaged food that provides convenience to the consumers. The authentic packaging of food implements consumers with the role of communication, containment, convenience, and protection. Any typical food package provides us with the information about the product and its nutritional fact, thus an efficient way of marketing/ branding. It protects the food product from the external environment and adulteration, thus keeps it safe. It allows the consumers to relish the food as per their convenience (reheating and direct consumption) and holds the product of any size, shape and restrains during handling and transportation (Robertson, 2006).

Food packages can be altered or modified according to one’s requirement with features like single-serving dishes and portability. However, traditional packaging is lacking adequacy to meet unending consumer demands and product intricacies. Therefore, a contemporary concept of packaging with higher proficiency is needed to meet various consumer requirements. For instance, a packaging that can - enhance product’s shelf-life with lesser or no preservatives, that maintains freshness, that meets permissible regulatory requisites and enables tracking throughout the product life-cycle. Emerging technologies like smart packaging have the expertise to invigilate a product and its milieu and pursue any modifications into these. Moreover, smart packaging helps in gaining global market recognition, and meets international food safety standards. In recent years, there has been rapid development in food packaging that includes active packaging, intelligent packaging, or smart packaging. These are the technologies that deal with the packaging of food, beverages, cosmetics, and pharmaceutical products. Moreover, these terminologies are apparently identical but, each has a different function to deal with and is as follows:

Inclusive education requires learning environments that recognize and respond to the diverse sensory, physical, cognitive, emotional, and learning needs of students with disabilities. The integration of Generative AI with Assistive Educational Technology and digital pedagogy creates new possibilities for removing longstanding barriers to participation and achievement. This chapter explores how intelligent technologies can provide flexible, adaptive, and personalized learning experiences for disabled students. It examines applications involving text-to-speech, speech-to-text conversion, screen readers, captions, intelligent interfaces, eye tracking, gesture-based controls, AR/VR, multimodal content, real-time feedback, and AI-supported personalization. Generative AI can further enhance conventional assistive technologies by producing customized written, visual, and audio learning materials according to individual requirements. Universal Design for Learning provides an important framework for ensuring that these technologies support accessibility and inclusion rather than creating additional barriers. The chapter also highlights the importance of individualized learning plans, teacher preparation, family and community engagement, emotional support, and stakeholder collaboration. By combining digital pedagogy, assistive technologies, and GenAI, educational institutions can move toward more equitable, accessible, learner-centered environments in which students with disabilities can participate meaningfully and develop their full potential.

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 fate of smoke from a wild land fire depends in large part on the airflow carrying it away from the fire which can involve a complex interaction of eddies that may occur near the ground or expand beyond the atmospheric mixed layer (Fig.11.1). The buoyancy of smoke and ambient atmospheric conditions determines how high and how quickly the smoke rises, and thus where it travels.
The smoke plume distribution is affected by number of factors as indicated below:
• Elevation: The smoke plumes at higher elevations disperse the smoke particles more vertically while plumes from lover elevations disperse more horizontally.
• Temperature: The large difference between the smoke and surrounding air affects the Plume dynamics. The buoyancy force increases the vertical velocity of the particles, while heat diffusion increases surrounding air temperature.
• Moisture: The presence of moisture can modify the distribution of smoke and chemistry of smoke particles.
• Topography: Topography can influence emissions that remain within the mixing zone.
• Atmospheric stability: Atmospheric stability can affect the distribution of smoke.

ABSTRACT

Hyperspectral data are generally noisier compared to broadband multispectral data. Therefore, it is desirable to smoothen the reflectance spectra. Smoothing needs to preserve an absorption feature while simultaneously removing noise. It is very essential to optimize the smoothing filter to minimize disturbances to the statistical properties of the original data. This study was carried out to see the effect of Fast-Fourier Transform (FFT) based smoothing technique on the statistical properties of vegetation spectral response and to see the effect of the filter size. The data used for the present study is the reflectance spectra data obtained from space borne EO-1 onboard Hyperion sensor. Filter sizes were varied between 3 and 15 with the increment of 2, i.e. 3, 5, 7..........15. Paired t-test was carried out between the original and the smoothed data (for the selective bands) for all the filter sizes in order to see the extent of distortion with changing filter sizes. Our study revealed that the distortion in the spectra increases with the increase in the filter size. Beyond filter size 11, the number of locations within the spectra where the smooth spectra were statistically different from its original counterpart increased to 14 and reaches 21 at the filter size 15.

11.1. Definition

Jones: Variations or modifications of any aspect of social process, social patterns, social interaction or social organization.

Gillin and Gillin: Variations from the accepted modes of life; whether due to alteration in geographical conditions, in cultural equipment, composition on the population or ideologies and whether brought about by diffusion or inventions within the group.

Davis and Mac Iver: Change in the relationships.

10.1. Social value

Ideas which help an individual to discriminate whether a particular object or behaviour is good or bad, desirable or undesirable.

Norms: Times rules which govern the action

Normative behavior: People are expected to behave in accordance with the values held by the individual.

S.C. Dood defined the value as “desiderata” i.e., anything desired or chosen by someone, sometime.

Value may be considered as attitude-related attribute that are projected upon people, objects and situations.

4.1 Demographic Profile of Watershed Communities

• Population Distribution: Examining the spatial distribution and density of population within watershed areas, including rural settlements, urban centers, and peri-urban zones. Analyzing population distribution within watershed areas involves understanding how people are distributed across various types of settlements, including rural, urban, and peri urban zones. Here's a breakdown of each:

a. Rural Settlements: These are typically smaller communities dispersed throughout the watershed area, often engaged in agriculture, forestry, or other primary industries. Population density in rural areas tends to be lower compared to urban centers. The distribution of rural settlements can be influenced by factors such as terrain, availability of natural resources, and historical land use patterns.

b. Urban Centers: Urban areas within watershed boundaries consist of cities and towns with higher population densities and more developed infrastructure. These areas are characterized by a concentration of economic activities, services, and amenities. Population distribution within urban centers can vary widely, with some areas experiencing high population density in downtown cores and others having more spread-out suburbs.

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.

Existing positive as well as negative interactions among the members of the society determine the communication structure, leadership and technology diffusion pattern within the given social system. Sociometry is a method for gathering and analyzing data on communication choice and interaction pattern of individuals, within a group in order to identify communication structure and pattern.

Introduction

For many years it has been an elusive task to provide a precise definition as to what constitutes quality in wheat. Each country has its own traditions in bread and other bakery products. Quality will mean different things to a miller, a baker, a grain handler or a plant breeder. As the baking industry becomes increasingly specialized, specific qualities are sought. Even among those wheat varieties generally recognized as having desirable quality traits, the influence of environment can be substantial, and this necessitates a high level of skill on the part of grain buyers to survey the current year’s crop and blend the wheat for a specific end-use. This genotype X environment effect on quality also limits the capacity of end-users to source identity-preserved wheat from a narrow geographic area. Those of us engaged in the field of plant breeding are concerned primarily with those traits that can be manipulated genetically, and we study their stability under a range of climatic conditions. Constraints in time, cost, and sample availability usually necessitate the use of predictive tests during early generations, followed by milling and baking tests on advanced lines that have met a number of agronomic, disease and predictive quality criteria.

8.1 Moisture extraction pattern

The moisture extraction pattern reveals about how the moisture is extracted and how much quantity is extracted at different depth level in the root zone. The moisture extraction patter shows the relative amount of moisture extracted from different depths within the crop root zone. The moisture extraction pattern of plant growing in a uniform soil without a restrictive layer and with adequate supply of available soil moisture throughout the zone is shown in Figure. 8.1.

3.1 Concepts of Soil Sampling

Samplinginvolvesthe selection fromthe total populationof a subset ofindividuals upon which measurements will be made. The measurements made on this subset (or sample) will then be used to estimate the properties (or parameters) of the total population.Samplingisinherent toanyfield research programinsoilscience because the measurement of the total population is impossible for any realistic study.

Samplingdesign involves theselection of the mostefficient method forchoosing the samples thatwill be used to estimatethe properties of the population. The definition of the population to be sampled is central to the initial formulation of the research study. The sampling design defines how specificelementswill be selected from the population, and these sampled elements form the sample population.

Pedogenetic and soilgeomorphic studies focus on determining the processes thatformed the soilproperties or landscapes under study and the environments that controlled the rates of these processes. Pedonscale studies are closely associated with thedevelopmentof soil taxonomic systems andfocus onvertical, intrapedon processes. Soil geomorphic studies are the interface between quaternary geologyand soil science and soil geomorphologistsfocus on lateral transferprocesses and the historical landscape evolution. Hence sampling is difficult for a particular population and is differentfor differentstudy.

Out of 329 m ha geographical area in the country, 187.8 m ha or 53% is affected by various forms of degradation process which include 148.9 m ha affected by water erosion, 13.5 m ha by wind erosion, 13.8 m ha by chemical deterioration and 11.6 m ha by physical deterioration. The annual loss of topsoil is nearly 16.0 t/ha/year resulted in to 8.4 m t/year total nutrient loss.