eChapters

1. Introduction 
The growing demand for fossil fuels around the world and their extensive use, which degrades the environment, have been serious issues. More precisely, it is predicted that global energy consumption will rise by 49% between 2007 and 2035 due to social pressure, population growth, and economic expansion (Cheah et al., 2016; Prasad et al., 2016). In order to replace fossil fuels, alternative energy resources must be developed, according to energy security and environmental sustainability. The most promising bio-fuel among the alternatives to gasoline is bio-ethanol, which can be used as the only fuel in compatible automobile engines or blended with gasoline up to 30% without requiring engine changes (Safarian and Unnthorsson, 2018). Bio-ethanol has a higher-octane number, which enables engines to run at high compression ratios, and a high oxygen content, which improves combustion efficiency (Branco et al., 2019). Grain, sugar beets, corn, and sugarcane could all be used to make bio-ethanol. Together, Brazil and the United States account for over 90% of the world’s bio-ethanol production, with 59% and 27% coming from each country, respectively (Branco et al., 2019).The Policy Energy Act and Energy Independence and Security Act, which aim to consume 136 billion gallons of bio-ethanol by 2022, are the main drivers of the USA’s remarkably high production rate (Menon and Rao, 2012; Tran et al., 2019). Unfortunately, the world’s food security is now in jeopardy due to the manufacture of bio-ethanol from the aforementioned edible energy crops. For the synthesis of bio-ethanol, lignocellulosic biomass-such as agricultural residues, forest woody residues, micro-algae, and even municipal solid waste is consequently a more advantageous source. Cellulose, hemicellulose, and lignin make up the lignocellulosic complex structure. While hemicellulose is in charge of binding and 88 | Biofuels Production Using Sustainable Bioprocessing Technologies lignin guarantees the toughness of the entire stricture, cellulose, a polymer made of glucose, gives plants structural support (Kumar et al., 2016; Prasad et al., 2016). According to Tran et al. (2019), pre-treatment is a crucial step in order to: (i) increase the amorphous area to facilitate hydrolysis; (ii) improve the porosity of the porous matrix to facilitate enzymatic and chemical hydrolysis; and (iii) separate cellulose from lignin and hemicellulose. The majority of the most successful pre-treatment techniques now in use are physio-chemical and chemical techniques, which also produce harmful compounds such furfural (Liyamen and Ricke, 2012). Other techniques are also used, each with advantages and disadvantages. For example, biological pre-treatment is environmentally benign but does not generate large amounts of material. Compared to the thermochemical approach, the biochemical conversion process performed better and was more ecologically friendly (Mu et al., 2010; Liyamen and Ricke, 2012; Kumar et al., 2019). It should be mentioned that the pre-treatment cost should also include the expense of detoxifying harmful inhibitors, which are created as a result of the pre-treatment technique (Liyamen and Ricke, 2012). All things considered, a successful pre-treatment ought to be both financially and environmentally responsible. It’s also critical to make sure that other advantageous aspect like maximum energy savings, waste and wastewater recycling, material recovery, and the use of a biorefinery method are taken into account throughout the lignocellulosic bioethanol production process (Kumar et al., 2019). Figure 1. Overview of how ILs typically pre-treat lignocellulosic biomass for the manufacture of biofuel (cheah et al., 2020).

Precipitation indices convert rainfall data into standardized measures of wetness, dryness, intensity and duration. They allow climatic conditions to be compared across locations and time scales despite differences in normal rainfall. This chapter introduces major indices such as the Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index, Percent of Normal and percentile-based measures. It discusses data requirements, calculation, interpretation, strengths and limitations. These indices support meteorological, agricultural and hydrological drought assessment, wet-spell analysis, crop planning and water-resource management. By identifying the severity and persistence of rainfall deficits or surpluses, precipitation indices strengthen early warning, drought preparedness and climate-risk management in agriculture.

2.1 Introduction

Precipitation is the general term for all forms of moisture emanating from clouds and then ultimately falling on the land surface. It is the total supply of water derived from the atmosphere in the forms of rain, snow, hail, mist, dew, sleet, fog etc. The liquid form of precipitation is called as rainfall whereas the frozen forms of precipitations are snow, hail and sleet. Of all the different forms of precipitation, rain and snow are the two dominant factors that contribute maximum amount of water. In Indian condition, rainfall is the most important form of precipitation that contributes the highest amount of water causing stream flow. Unless otherwise stated, the term rainfall will be used in this book synonymously with precipitation. It is the primary input vector of the hydrologic cycle. Generally it is expressed as the depth water on a horizontal surface over any specified time interval. If the specified time interval is day, then the amount of precipitation is called as daily precipitation. Similarly, the precipitation being contributed over time interval of a month, season and year is termed as monthly, seasonal or annual precipitation. Its form and quantity are influenced by the action of different climatic factors such as wind, temperature and atmospheric pressure. The study of precipitation forms a major portion of the subject of the hydrometeorology.

Precipitation is water in liquid or solid forms, falling on to the earth. In other words, precipitation is the deposition of atmospheric moisture on earth. Formation of clouds does not mean precipitation. Precipitation involves condensation, growth of hygroscopic nuclei, cooling of nuclei at different stages and their fall on to the earth’s surface. There may be several clouds but no precipitation. If the cloud particles are too small and remain suspended in the sky, they may not fall on the earth. Under certain conditions, water particles formed tend to fall but do not reach the surface since they get evaporated on the way. Only when the cloud droplets or ice crystals grow to certain size and can no more remain floating due to buoyancy of air, do they fall as precipitation.

Precipitation is the major source of water to all the living organisms on the earth. All the water sources on the earth depend on precipitation only.

Forms of precipitation

Precipitation form is the state that the moisture is in, i.e., liquid, freezing, or frozen (solid). Different forms of precipitation may occur together, such as mixed rain and snow; but such an occurrence is simply a mixture of forms, not a separate form of precipitation. Because atmospheric conditions vary greatly both geographically and seasonally, several different forms of precipitation are possible. All forms of precipitation are collectively termed hydrometeors. Hydrometeors have been classified into 50 specific types. Out of these precipitation forms, only rain (liquid form) and snow (solid form) make significant contribution to total precipitation. In many parts of the world, the term rainfall is used interchangeably with precipitation.

The major source of water supply on the earth is precipitation. The water received at the earth’s surface from the atmosphere either in solid or liquid form is called precipitation. The water received in solid form may be snow or hail. The precipitation is seasonal in character in many parts of the world. The precipitation may occur due to –
• Conventional physical process
• Orographic effect and
• Convergence of air masses
3.1. Conventional Physical Process
It occurs when the air on the surface of the earth and few metres above the surface of the earth is heated by absorbing long wave radiation emitted by the earth’s surface. As the air is heated, it becomes lighter. The water vapour formed due to evaporation from the soil surface or water bodies will mix with the air and the air will be moist. When the moist air gets heated it becomes lighter and rises vertically upwards. After reaching certain height, the air gets cooled enough to get saturated with water vapour. The moisture in the saturated air will condense on small solid particles to form tiny drops. These solid particles may be dust or smoke or solid particles suspended in the atmosphere and are called aerosols.

Abstract

The social and economic development of any state is interlaced with the manner of its natural resource management. The unplanned use and overexploitation of resources are results in various kinds of land degradation, biomass deterioration and siltation of tanks. In addition, the low per capita availability of land, erratic and uneven distribution of rains, undulating topography, improper management, traditional cropping programmes and recurrence of droughts having cumulative effect leading to low productivity and high risk particularly in dryland farming. Further, developing dry land agriculture through sectoral approach did not bring in the desired results, as the efforts were not integrated. However, in nature all the resources are inter-linked and thus, the integrated developmental approach is the best solution for optimal development of any area. Further, development based on administrative units such as district, taluk, block and village have resulted in imbalance and sometimes, no overall development in some areas. Considering this, natural resources management through Geo- informatics tools like, remote sensing, GIS, GPS and other sensor based techniques are the possible ways for making agriculture sustainable. The paper mainly highlights theimportance of application of latest technologies in agriculture along with some field level case studies.

Keywords: Remote sensing, GIS, Watershed, CAPE, Precision agriculture

Precision agriculture is yet to be popularized for taking its enormous advantages. In laser land levelling and micro irrigation/ fertigation it must have made its prominence but others it is with research institutes where research is focussed on the spatial variability of production environments, development of efficient and suitable data management systems, efficiency of various types of image analyses and optical sensing, efficiency of sensors and related technologies, designs of precision agriculture equipment, optimal inputs and service uses, and their spatial allocations, potentials of unmanned aerial vehicles (UAVs), nanotechnologies and several others.The adoptions of precision-agriculture technologies have, so far, mostly been limited to parts of developed countries. Major contributions to the profitability of precision agriculture is reduced costs of plant protection inputs and higher output revenues.

Precision agriculture includes information driven administration choices that further develop asset use productivity, bringing about decreased Agriculture expenses while bringing down the natural effects from agriculture. Consequently, information and information assortment frameworks, choice help devices, and information driven hardware and info changes are significant parts of Precision agriculture, participating in three vital farming advances: determination, direction, and performing separately. Before the coordination of shrewd innovations, ICT (data and correspondence innovation) was incorporated into farming devices and mechanical assembly to get certifiable data. Here, remote detecting, mechanized hardware and programming, telemetric, drones, autonomous vehicles, GPSs, and computerized developments were incorporated into cultivating rehearses. For example, the agro-tech association John Deere introduced GPSs for ranch farm haulers, anticipating extended yield and reduced input wastage. The previous status of accuracy agribusiness before brilliant cultivating can be summarized as follows.
Data Collection and Acquisition
Data, data collection, and decision support devices are critical for the distinctive verification and finish of alternate points of view in cultivating. In Accuracy agribusiness, data on individual fields and gathers are collected by seeing, assessing, and distinguishing with different sorts of sensors, yield and soil checking, and remote-detecting mechanical assemblies, such as imaging from drones, groups, airplane, or satellites. As such, “detecting” is a fundamental organization gadget of accuracy agribusiness, which is seeing distinct information and giving data on climatic circumstances, soil conditions, fertilizer necessities, water openness, nuisance and infection stresses, and other field limits. An extent of sensors is used in Accuracy farming. Biomass limits are huge in making decisions to screen the readiness and truly zeroing in on crops. Sensors for mass stream and dampness content are portions of yield screens, along with a differential global positioning system (DGPS) recipient. Appropriately aligned yield screens can produce precise constant

Introduction

Agriculture is a vital sector in the world economy, as it helps to reduce poverty and promote sustainable development, especially in developing countries. However, agriculture also faces challenges such as increasing productivity and reducing environmental impacts. Therefore, new technologies and methods are needed to improve agricultural practices. One of these methods is precision agriculture (PA), which is based on the principle of sustainable agriculture and healthy food production. PA is a science that uses high technology sensors and analysis tools to improve crop yields and support management decisions. PA is a new concept that aims to increase production, reduce labor time, and optimize the management of fertilizers and irrigation processes.

4.1 Introduction

In the modern era, agricultural productivity seems to have reached a maximum production due to the easy availability of fertilizers and pesticides/insecticide, which are used to improve crop productivity. Excess uses of these products and unawareness about field constraints can decrease the crop productivity and endanger the environmental balance in the cultivation area. Therefore, there is an urgent need to improve agricultural system and efficient utilization of these resources for profitable and environmentally sustainable agriculture.

Government has also an emphasis on effective and efficient use of available resources and farmers are also adopting more scientific approach during crop production such as remote sensing, global positioning system, and new equipment to make their agricultural operation more precise.

Precision agriculture is one of the many advanced farming approaches that make production more efficient. It is basically an approach of farm management with the help of information technology to confirm that the cultivated crops and soil receive desire input for optimum health and productivity. In other words, precision agriculture utilizes different tools and technologies to identify infield soil and crop variability for improving farming practices and optimizing agronomic inputs. To reduce dependency on excessive use of chemical inputs in agriculture, several technologies have been applied to make agricultural products safer and to lower their adverse impacts on the environment and human beings, a goal that is consistent with sustainable agriculture. Precision agriculture has emerged as a valuable component of the framework to achieve this goal. Further, Pierce and Nowak define the precision farming that, the application of technologies and principles to manage spatial and temporal variability associated with all aspects of agricultural production for improving crop performance and environmental quality. Precision agriculture is the combination of many different elements working together in a synchronised way with the help of Geographic Information System (GIS) as shown in Fig 4.1. Precision agriculture is basically an information technology based agricultural management system. In this modern management system, Geography information system (GIS) based analysis is extensively used to get land and crop related data accurately and timely, which help farming community to take correct decision .

Precision farming is also known as precision agriculture. A farmer is not only expected to produce more for the increasing population and his family but save and protect the fragile environment while practicing it. Advanced technologies with appropriate and exact inputs such as precision farming can take care better of environment. Precision farming is right farming operations at right time leading to maximize profit. Every farming activity performed with precision by doing the right thing, in the right place, in the right way, at the right time and with right technology leading to maximize profit is known as precision farming. It is an efficient, eco-friendly and accurate management of agriculture inputs such as water, seed, fertilizers, pesticides and harvesting tools and machinery to maximize profit and reduce waste. The basic concept of precision farming is to match agricultural inputs and operations as per crop (variety/hybrid/cultivar) and agroclimatic conditions with accuracy to their better use efficiency. Perfection (nothing requisite is wanting) in farming is the key to precision farming.

Precision farming is based on global positioning system (GPS). The unique character of GPS is precision in time and space. Its use is spreading to all farm operations. Precision farming is an integrated information and production based farming system that is designed to increase long term, site specific and whole farm production efficiency, productivity and profitability while minimizing, waste and unintended impacts on the environment. This concept of precision agriculture is based on observing, measuring and responding to inter and intra field variability in crops.

Farm management strategy is the key to precision agriculture that uses integrated smart technologies such as sensors, GIS, GPS, satellite radars, drones, IoT (Internet of things) and data analytics to develop informed crop production and management decisions. The field variations are precisely measured with the help of technologies to optimize applications of inputs such as manures, fertilizers, pesticides, bio-agents and water use to increase crop yield at minimum cost. The aim of precision farming is to increase productivity, decrease production costs and minimize the environmental impact of farming.

In this Chapter the precision farming/ Agriculture is discussed along with its steps of implementation. It also includes how artificial Intelligence plays role to perform precision farming/ agriculture in various way along with its scope, limitation and challenges.

Introduction

The primary source of livelihood is agriculture in India, where two thirds of India’s population, services and the private sector account for the remainder. Agricultural land accounts for about 43 percent of the geographical area of India. In earlier days, India was largely dependent in terms of food imports, but over the years, the country’s production of grain and seeds was self-sufficient, and this effort led to the formation of the Green Revolution. In fact, there was strong effort to achieve food self-sufficiency. This trend has continued up to now and continuous improvements have occurred. Precision agriculture or precision farming means the right thing to do, in the right way, on the right place and at the right time. Precision agriculture is expected to suit the agro-climate activities to improve application precision. Farming land has declined a bit in the last 40 years but the number of farmers has increased. According to the 2010–11 Agriculture Census, a total of 138.35 million operating holdings were estimated (single farmers) and 159,59 million hectares were covered by operations.

Precision agriculture is a management technique that collects, processes and analyzes temporary, spatial and individual data and integrates this with other information in order to support management decisions based on an estimated variability to enhance efficiency of use of agricultural production resources, productivity, quality, profitability and sustainability. Precision Agriculture manages every input of crop production (fertilizer, specifically found calestone, herbicide, seeds, insecticide, etc. Reduce waste, boost income and maintain efficiency Place. Precision Agriculture adapts soil and crop management carefully to suit the different field conditions.

Agriculture sector alone uses nearly seventy per cent of fresh water in the world. Therefore, water use economy in the agriculture sector is becoming a necessity. Increasing flood production is possible reducing water used for irrigation if we can adopt precision irrigation (Fig.11.1).
The precision irrigation has to be practiced by providing water to the crop plants in its root zone to meet the water requirements. The precise irrigation can result in-
• Water saving
• Avoid weed control
• Preventing loss of water due to infiltration
• Preventing loss of water due to surface evaporation from the soil
• Minimise salinity problems
• Improving water use efficiency of crops
Traditionally farmers were practicing flood irrigation. It is a method of watering crops delivering to a field and allowing it to flow over the ground among the crops. It is a simple and inexpensive technique followed in less developed areas. Flood irrigation is not very efficient way as nearly 50 per cent of the water applied in the field will be lost through run-off, soil evaporation and other factors. Flood irrigation is only suitable in wet climates and not effective in sandy soils. It leads to water logging in clay soils. There are several types of flood irrigation such as basin, border, furrow and contour irrigation. Although flood irrigation is not very efficient, the water wastage can be avoided to some extent using some techniques

Precision farming involves the collection of data by observation and measurement, and the subsequent response. Sensing is key to the collection of data; whether this is remote from space by satellites, by aircraft or drones, or by proximal sensors used on the ground, mounted on vehicles or otherwise. Responses are increasingly automated and data are often integrated into wholefarm management systems. Early commercial applications were directed towards improving crop nutrition, however, utilising precision technologies to control weeds, pests and diseases is now a reality.

Precision crop protection requires detailed data on the occurrence and distribution of problems at the earliest possible stage of detection. An early detection is the key for successful management of pests in precision farming. Some of the key aspects are:

Identification: Identifications of crops, targets and beneficial; temporal and spatial distribution: presence of the target before, during and after the crop growth period;

Sampling techniques: statistically valid sampling regimes must be used to permit an accurate view of any problem

Thresholds: under particular circumstances, the point at which action to ensure levels of infestation or damage are kept to those deemed to be acceptable;

Prediction: the speed and pattern of development of a problem must be capable of being modelled, e.g. spread and severity of insect pests and fungal diseases; weed emergence according to species, dormancy and environmental conditions.

The domestication of oxen in Mesopotamia and the Indus Valley Civilization, perhaps as early as the 6th millennium B.C. provided mankind with the draft power necessary to develop the early plough into the larger, animal-drawn true ard (or scratch plough). Some of the earliest evidence of ploughing from around 2800 B.C.was found at the Indus Valley site of Kalibangan. Subsequent archaeological finds in Prague, Czech Republic, push the oldest known ploughed field even further back to 3500-3800 B.C. All food produce was planted and harvested by hand: seeds were scattered by hand and planted grains were brought from the field to be threshed on the dooryard. Milking a small herd of cows or goats was also done by hand. This continued for centuries. Then man discovered electricity.

In 1746, just a few years before Benjamin Franklin of Philadelphia sent a kite to catch lightning in a storm, a Doctor von Maimbray gave talk to the Royal Society of London where he described his experiment of electrifying the myrtle bush (Morella cerifera) which then put forth new branches in October, something which had never happened before. This news inspired others in Europe to carry out similar experiments. In 1749, Abbot Jean Antoine Nollet of the Royal Academy of Sciences in Paris France, inventor of the electroscope, published a pamphlet on “The Harmful Effects or Advantageous of Electrical Phenomena on Plants.”¹Isolated experiments continued until in 1783 Father Pierre Bertholon de Saint-Lazare of Montpellier published “About the Electricity of Plants”, a work in which he dealt with the effect of atmospheric electricity on plants, taking in their medicinal and nutritive-electrical virtues and but in particular the practical means of applying it usefully to agriculture.²He even built an electro-vegetometer, a device for collecting atmospheric electricity to distribute it in the soil and then to the plants, but he met with little success. Bertholon also conceived of subjecting plants to “electric rain” by connecting a large syringe to an electric source. A gardener, standing on a slab of insulating material in a wagon, carried a sprinkling can electrified by means of a wire leading to a static machine. “By means of this process, strange for the times, the good Abbé Bertholon, who was considered something of a sorcerer, obtained lettuces of an extraordinary size.”3

Abstract
 
The definition of what drought is and what drought is not, has profound implications for the environment and all segments of society, yet it may be different for each. Many attempts have been made to develop a comprehensive and meaningful definition. A generic definition provides a starting point:  Drought is a persistent and abnormal moisture deficiency having adverse impacts on vegetation, animals, and people. The public perceives “drought” as a serious departure from normal water conditions, a departure that requires a public response to reduce negative impacts. About two thirds of the geographic area of India receives low rainfall (less than 1000 mm), which is also characterized by uneven and erratic distributions.

Predictive analytics estimates future health risks and outcomes, while prescriptive analytics recommends appropriate interventions and resource-allocation strategies. This chapter explains how statistical models, machine learning and healthcare data can support disease-risk assessment, prognosis and personalized treatment. Applications involving cardiovascular disease, diabetes, cancer, chronic-disease management and early-warning systems are examined. The integration of clinical, genomic, lifestyle and electronic health-record data is discussed alongside survival analysis, disease-progression modelling and treatment-response prediction. The chapter also addresses bias, fairness, transparency, privacy, regulation and interoperability. Future developments involving real-time analytics, IoT-enabled monitoring and precision medicine illustrate the transition toward proactive, preventive and data-driven healthcare.

Predictive modelling of plant growth can help farmers and researchers improve crop management, resource utilization and production planning. This chapter examines machine learning approaches for predicting important plant growth characteristics. It introduces plant growth fundamentals and the environmental and management factors that influence development. The chapter covers the machine learning workflow, including data acquisition, preprocessing, feature engineering, model development and evaluation. Supervised learning, unsupervised learning and deep learning approaches are considered for predicting plant height, biomass, canopy development, leaf area and yield-related characteristics. Data from IoT devices, drones and satellite imagery can strengthen prediction models. The chapter also discusses precision agriculture applications, limitations and emerging technologies such as explainable AI, digital twins and edge AI.

Preparation and processing of the ice cream mix determine the functionality, safety, and consistency of the final product. This chapter describes the complete mix-processing sequence, beginning with ingredient measurement and blending and continuing through pasteurization, homogenization, cooling, and aging. It explains the purpose of each operation and its effect on proteins, fat globules, stabilizers, viscosity, microbial safety, and structure. Aging is particularly important because it supports fat crystallization and hydration of proteins and stabilizers, improving whipping and texture. The chapter also discusses common processing defects, corrective actions, and practical standard operating procedures, providing an industry-oriented framework for producing consistent and high-quality ice cream mixes.

Aim

To prepare a contour map by direct method using dumpy level.

Materials required

Dumpy level

Leveling staff

Measuring tape

Wooden pegs/chain pins

Field book

Theory

For locating a point on the ground on a contour, its position and elevation in a horizontal plane should be known. In direct method, the contours to be plotted are located on the ground with a level by making various points on each contour, and these points are surveyed and plotted on plan. This method is more accurate but slow and tedious, and is suitable for contouring small areas where great accuracy is required. For locating the points of the same elevation, the instrument is set in such a commanding position from where maximum number of points can be covered. The bench mark is established near the site of survey. The height of the instrument (H.I) is determined after taking a back sight (B.S) on the established bench mark.