eChapters

Introduction

Considering the accountable post-harvest losses which is approximately 30-35 percent, food processing becomes an extremely important sector for preventing the losses and feeding the growing population. The Indian food industry is poised for huge growth, as currently valued at US$ 1.3 billion with compound annual growth rate of 20 per cent per year, increasing its contribution to world food trade every year. In India, the food sector has emerged as a high-growth and high-profit sector due to its immense potential for value addition, particularly within the food processing industry. Increasing population of the country and high cost of fuels have also created a demand for new alternate sources of energies for post-harvest processing of foods. Solar food processing technologies may become a viable option that provides good quality foods at low or no additional fuel costs. The energy policy of the developing countries now focuses on the clean renewable energy to create a further reduction of the petroleum import and to alter the utilization of petroleum energy toward the utilization of solar energy. It is an interesting fact that most of the developing countries fall in the climatic zone where the insolation is considerably higher than the world average of 3.82 kWh m-2 (Majumdar 2015). The daily horizontal insolation of India is approximately 5.80 kWh m-2 (Visavale 2009, Visavale et al. 2011). Solar dyers are an alternative to traditional drying technique and a contribution towards the solution of the problems of the open sun drying from perspective of renewable and clean energy. The International Energy Agency (IEA) declared in its 2011 solar energy perspectives executive summary (SEP 2011): “Solar energy offers a clean, climate-friendly, very abundant and inexhaustible energy resource to mankind, relatively wellspread over the globe. Its availability is greater in warm and sunny countries that will experience most of the world’s population and economic growth over the next decades.

Introduction

The thermal performance of a building refers to the process of modeling the energy transfer between a building and its surroundings. For a conditioned building, it estimates the heating and cooling load and hence, the sizing and selection of HVAC equipment can be correctly made. For a non-conditioned building, it calculates temperature variation inside the building over a specified time and helps one to estimate the duration of uncomfortable periods. These quantifications enable one to determine the effectiveness of the design of a building and help in evolving improved designs for realizing energy efficient buildings with comfortable indoor conditions. The lack of proper quantification is one of the reasons why passive solar architecture is not popular among architects. Clients would like to know how much energy might be saved, or the temperature reduced to justify any additional expense or design change. Architects too need to know the relative performance of buildings to choose a suitable alternative. Thus, knowledge of the methods of estimating the performance of buildings is essential to the design of passive solar buildings.

Introduction

As the most of the conventional resources are depleting and can last only for about a few 30 to 50 years. Scientists worldwide are looking for alternative energy resources and they have come to an agreement that solar energy can provide much of our energy needs in the future. The major challenge before them is to store this energy. The radiation falling on the earth surface can be tapped and stored in no of ways. However, the most efficient way is done through the latent heat storage through PCM. The PCM has many added advantages i.e. high energy density, isothermal behavior, non-polluting and low cost. Nowadays, it gaining popularity worldwide and now seen as the most efficient way of thermal energy storage. There is a large number of PCM at use i.e. organic, inorganic, eutectic and salt hydrates. Their utilization depends on the user’s requirement technological constraints. They also serve the purpose of energy availability at night beside the uninterrupted supply during the day. They are seen as the materials for ensuring the environmental sustainability

Introduction

Solar energy could be converted to electricity both through thermal route and photovoltaic cells but the latter is more advantageous as it has no moving part, it doesn’t require water, a rare commodity specifically in arid regions. Moreover, the maintenance of PV systems is easy, it can be installed right at the place of utility, the system is modular and these are reliable having life more than twenty years and of course eco-friendly.

In photovoltaic cells, generally known as solar cells, charge carriers are generated by the incoming irradiance (energy greater than band gap) and these are separated and swept away by an internal electric field, which is created by either making metal semiconductors contact (Schottky barrier) or p-n junction or MIS (Metal Insulator Semiconductor) or SIS (Semiconductor Insulator Semiconductor) devices. The height of potential barrier at the junction determines the voltage and the flowing charge carriers contribute to the current when connected externally. The choice of material depends on its band gap, absorption coefficient, diffusion length, mobility of charge carriers, ease in contact formation, type of junction with thermal and electronic compatibility, availability of material, toxicity and several other fabricational opto-electronic and practical aspects. PV cells based on single crystal and polycrystalline silicon, amorphous silicon, CdS, CdTe, CuInGaSe, InP, GaAs etc. have been much studied. PV modules of efficiency above 16% on silicon and 10% on CdTe, CuInGaSe and 8-9 % on amorphous silicon are commercially available. Dye sensitized TiO₂ devices have also been receiving more attention with coloured panels for windows. Organic semiconductors are explored and more recently perovskite solar cell has been studied. Flexible solar cell panels based on thin film devices are in use. The applications have gone from solar lighting to operation of pumps, small and medium size equipment, stand alone and grid connected solar power plants, roof top and building integrated PV and now even energizing cars and aero planes. With technological advancement and enhanced production (233 GW in 2015) the cost of commercially available PV modules, which used to be high at one time has been brought down from 30 $/Wp in seventies to about $0.5/Wp at present.

Life cycle cost analysis

Economics of solar energy devices can be calculated through life cycle cost (LCC) analysis. Total life cycle cost of a device is comprised of capital cost, maintenance cost, replacement costs for damagaed components and operational cost. Before adding these above costs, all future costs (C) are converted to present worth considering the relative rate of inflation (i) and discount rate (d).

Where, PW is the present worth of any future cost, i is the relative rate of inflation and d is the discount rate per year and n is the time period in years. Relative rate of inflation accounts for the escalted increase or decrease in prices of a commodity in comparison to general inflation rate. For any commodity, if the price escalation is expected as per the general inflation rate then relative rate of inflation is considered zero. In general case, relative rate of inflation is considered zero. Discount rate accounts the real value of money in future and in most of the economies of the world it is about 8-12%, and therefore 10% is considered in most analyis. The discount rate also refers to the interest rate used in discounted cash flow (DCF) analysis to determine the present value of future cash flows. The discount rate in DCF analysis takes into account not just the time value of money, but also the risk or uncertainty of future cash flows; the greater the uncertainty of future cash flows, the higher the discount rate. A discount rate of 10 % per year would mean that in real terms it makes no difference to a farmer whether he has ? 100 now or ? 110 in one year’s time. Conversely, a cost of ? 110 one year from now has a present worth of ? 100. For a future single cost in n^th year, the present worth of that cost is calculated using Eq (1). However, for future multiple payments, costs are to be converted to present worth for each year and then needs to be cumulated. For calculation of annualized life cycle cost (ALCC) of solar devices, annuity factor (AF) needs to be calculated for a period of life cycle of the device as follows:

Introduction

Cultivation of crops in greenhouse or protected structure has been increased tremendously in recent times and also has been spread from temperate regions to the warmer regions of tropics and subtropics. Plentiful technologies have been developed for heating the inside environment of greenhouse for optimum plant growth (Sethi et al. 2013). However, in hot arid areas, cultivation in greenhouse is very difficult as the temperature reaches to a very high level to sustain plant growth (Pek and Hayles 2004, Saran et al. 2010). Under such situation requirement for cooling the greenhouse microclimate environment is the necessity, but is very energy and water intensive. Hence, in such regions, reduction of air temperature inside the greenhouse or the regulation of temperature closer to the ambient temperature during summer is necessary for successful crop production. A typical diurnal variation of temperature and global solar radiation during late winter months inside a shade net structure has been plotted in Fig. 18.1. It has been found that global solar radiation has been decreased to almost 1/3 of the radiation received outside by using 75% shade net cover. The temperature inside structure during morning time has also been found higher than outside temperature and in summer such increase is expected to be much higher.

Therefore, cooling is considered as the basic necessity for greenhouse crop production in tropical and subtropical regions to overcome the problems of high temperatures during summer months. Development of suitable cooling system that provides congenial microclimate for crop growth is a difficult task as the design is closely related to the local environmental conditions. Different cooling techniques for greenhouse crop cultivation may be found in Kumar et al. (2009). Fan pad cooling system has been widely used for greenhouse cultivation however, requires large amount of water during evaporation based cooling. Since water resources are becoming scarce in future such fan-pad cooling system may not be viable in future specifically in hot arid region.

Introduction

Agricultural produce are being sun dried since ages as the first step to preserve the product for future consumption. It is still practised being most inexpensive over mechanical and other advanced drying systems. The most prevailing disadvantage of open sun drying is the quality losses due to insect infestation, enzymatic reactions, microorganism growth, and mycotoxin development. On the other side, sun drying is also a highly labor intensive and time consuming process prone to theft and damage by birds. It also suffers with the lack of process control and treatment uniformity (Bansal and Garg 1987, Bansal, 1987).

To overcome these disadvantages several types of solar dryers have been developed over the years (Ekechukwu and Norton 1999). These dryer may be identified into three group namely active, passive and hybrid. As regards their structural arrangement three generic subclasses were also identified: direct-modes (the solar-energy collection unit is an integral part of the entire drying system), indirect-modes (the solar collector and the drying chamber are separate units) and mixed-modes solar dryers. Natural convection solar dryers have become more suitable for rural sector and remote areas, as they work on single energy option and do not require any other external energy source (Pangavhane et al. 2002).

Introduction

For optimum use of harvested rain water in surface reservoir like farm ponds or tankas, small sized solar PV pumping systems with 1 hp AC and DC motor were experimentally tested at research farms of ICAR-Central Arid Zone Research Institute, Jodhpur (Fig. 16.1). Total suction head in both pumps was about 5 m. Among two installed pumps, the system with AC pump consisted of 1400 Wp (200 Wp × 7) whereas the DC pump consisted of PV array of 920 Wp (230Wp × 4). Each PV panel of AC solar pump was connected in series, which was further connected with an inverter to generate AC output of about 220-240 volt and 4-4.2 ampere. In case of DC solar pump, panels are connected in parallel to generate DC output of 40-50 volt and 15-20 ampere.

Introduction

Agriculture has been the back bone of Indian economy and culture and it will be continued to remain as such for a long time in future. Parallel to this, energy security of a country is also very important and efforts have been given on renewable energy utilization since the fossil fuel based energy is depleting at a very fast rate. In agricultural fields, considerable amount of energy is used to do different field activities e.g. ploughing, irrigation through pumps, intercultural operations, spraying of agricultural chemicals for plant protection, harvesting, post-harvest processing etc. Therefore, there is also need to replace the conventional energy source with renewable sources to operate above mentioned agricultural activities.

Approximately, 35% of the crop production is damaged if pest and diseases are not controlled at right time. Uniform spraying of liquid formulations throughout the crop field is very important for effective control of pest and diseases. Using sprayer, liquid pesticide formulations are generally broken down to minute droplets of effective size for uniform distribution over a large surface area. Different types of sprayers are used in agricultural field based on different requirements.

PV clad structures and hybrid devices for rural and agricultural applications have been developed after considering different design aspects, climatic parameters, solar radiation availability on different planes, heat transfer coefficients, performance of PV in arid region and inter phasing of different components while keeping in view, practicality, application and ease of operation. Performance of the developed systems was studied, mathematical model developed and energy savings worked out for techno-economic considerations. The chronology and details of the developed PV structures and hybrid devices are as follows:

Green structure for environmental control

First of all a Quonset type environment control structure having a length of 5.3 m and width 4 m was fabricated with angle iron and iron rods and it was covered by agro-net (75%) having surface area of about 55 m². The structure having a volume of 33 m³ was fixed with long side along east west direction and had provisions for incorporation of misting unit on west side and a door on east.

Introduction

Water is the primary source of life for mankind and one of the most basic necessities for crop production. The demand for water to irrigate the crops is increasing. For sustainable production from agricultural farms, irrigating the crops at right stages is highly important. Even in rainfed situation, lifesaving irrigation during long dry spell has also been found beneficial for crop survival and to obtain the targeted yield. Considering the depletion of groundwater below the critical zone in most part of the country, energy intensive pumping for irrigation is not a viable option. Therefore utilization of available runoff water through surface storage systems followed by pumping may be a potential solution to achieve the set goal of ‘crop per drop’ mission. In this connection, micro-irrigation system including drippers and sprinklers is of great importance. However, ensured power supply is essential to operate the micro-irrigation system even in remote areas. Reliable solar photovoltaic (PV) pumps are now emerging in the market and are rapidly becoming more attractive than the traditional power sources. These technologies, powered by renewable energy sources, are especially useful in remote locations where a steady fuel supply or electricity supply is problematic. About 16 million electric pumps and 7 million diesel pumps are in operations in the country for irrigation purpose; however they are highly energy intensive. Moreover, diesel operated and electrified pumps directly or indirectly emit large amount of CO₂ gas in atmosphere and hence are not environment friendly. To meet the energy demand for irrigation, solar PV pumps have been introduced under the off-grid power generation category of National Solar Mission (NSM) with a target of 1000 MW by the end of phase II (2013-2017). The target has been revised in 2015 to a total grid connected solar power generation of 1,00,000 MW comprising 40,000 MW roof top generation and 60,000 MW grid connected solar power plants (Resolution of MNRE, Govt of India, No. 30/80/2014-15/ NSM dated 1^st July 2015).

Introduction

A solar collector, the special energy exchanger, converts solar irradiation energy either to the thermal energy of the working fluid in solar thermal applications, or to the electric energy directly in Photovoltaic (PV) applications. For solar thermal applications, solar irradiation is absorbed by a solar collector as heat which is then transferred to its working fluid (air, water or oil). The heat carried by the working fluid can be used to either provide domestic hot water/heating, or to charge a thermal energy storage tank from which the heat can be drawn for use later (at night or cloudy days). Solar collectors are usually classified into two categories according to concentration ratios: non-concentrating collectors and concentrating collectors. A non-concentrating collector has the same intercepting area as its absorbing area, whilst a sun-tracking concentrating solar collector usually has concave reflecting surfaces to intercept and focus the solar irradiation to a much smaller receiving area, resulting in an increased heat flux so that the thermodynamic cycle can achieve higher Carnot efficiency when working under higher temperatures.

Introduction

The sun is an abundant source of solar energy (average value on horizontal surface 6 kW m^-2 day^-1), which is freely available. This huge source of energy is non-polluting and inexhaustible in nature. It has the potential of supplementing the conventional energy sources to a great extent. There is acute shortage of conventional energy sources, which affects the overall development. Work on the utilization of such a huge, non-polluting and everlasting energy source has been carried out at CAZRI, Jodhpur for various domestic, industrial and agricultural applications in order to supplement the energy demand. This includes the development of a lot of solar thermal devices, such as, solar still, solar dryer, solar cooker, animal feed solar cooker, solar candle device and cool chamber. The details of some of these devices are given as following.

Introduction

Solar air heating is the conversion of solar radiation to thermal heat. The thermal heat is absorbed and carried by air which is delivered to a living or working space. The transparent property of air means that it does not directly absorb effective amounts of solar radiation, so an intermediate process is required to make this energy transfer possible and deliver the heated air into a living space (Fig.10.1). The technologies designed to facilitate this process are known as solar air heaters. Solar air heating technologies use only free, renewable and clean energy and can help defray the rising cost of conventional energy.

Introduction

Solar refrigeration technology engages a system where solar power is used for cooling purposes. It offers a wide variety of cooling techniques powered by solar collector-based thermally driven cycles and photovoltaic (PV)-based electrical cooling systems. Cooling can be achieved through four basic methods: solar PV cooling, solar thermal cooling, solar thermo-electrical cooling and solar thermo-mechanical cooling. The first is a PV-based solar energy system, where solar energy is converted into electrical energy and used for refrigeration much like conventional methods. The second method utilizes a solar thermal refrigeration system, where a solar collector directly heats the refrigerant through collector tubes instead of using solar electric power. The third one produce cool by thermoelectric processes. The fourth method converts the thermal energy to mechanical energy, which is utilized to produce the refrigeration effect.

The performance of solar refrigeration systems is determined based on energy indicators of these systems. The COP (coefficient of performance) can be calculated as follows:

Introduction

We are blessed with solar energy in abundance at no cost. The solar radiation incident on the surface of the earth can be conveniently utilized for the benefit of human society. Solar energy can be used as thermal energy for water heating, cooking, drying, distillation, space heating, cooling and power generation or it can be converted to electricity through photovoltaic cells, commonly known as solar cells. One of the popular devices that harness the solar energy is solar hot water system (SHWS). Flat plate collectors are used for low temperature applications (below 100^oC) while concentrators are preferred for higher temperatures. The most efficient application of flat plate collector is for getting hot water. Hot water in winter is an essential requirement for domestic uses such as bathing, cleaning of utensils and washing of clothes. In rural areas hot water is also required for softening of animal feed. Generally it is obtained by using firewood and cow dung cake in rural areas or using kerosene, liquid petroleum gas, coal or electricity in urban areas. Therefore, the use of solar water heaters will conserve lot of commercial and non-commercial fuels which are being wasted in merely getting hot water. A solar water heating system (SWHS) is made of several important elements: one or more solar collectors, a pump, a heat exchanger, a storage tank (or multiple tanks) and a back-up storage tank. The Solar heating system can be classified as passive or active. For water heating purposes, the general practice is to use flat plate solar energy collectors (FPC). The evacuated tube collectors (ETC) and evacuated tube heat pipe collectors (ETHP) are more efficient, though, the initial cost is comparatively higher. There are three type of water heaters extensively studied by various workers viz. natural circulation, collector-cum-storage and forced circulation in all over the world.

Introduction

Flat plate collector is the heart of a solar heat collection system designed for delivery of heated fluid in the low to medium temperature range (5o70oC above ambient temperature) for applications, such as water heating, space heating, drying and similar industrial applications. The flat plate collectors absorb both beam and diffuse radiation. The absorbed radiation is converted into heat which is transferred to water or air flowing through the collector tubes or duct, respectively. Such collectors do not require tracking of the sun and little maintenance is required.

The conventional flat plate solar air heater, shown in Fig. 7.1, consists of a flat blackened absorber plate, a transparent cover (such as a glass cover) at the top and insulation at the bottom and on the sides. The air to be heated flows through the rectangular duct below the absorber plate. The glass cover transmits a major part of solar radiation incident upon it to the absorber plate where it is converted into heat. The glass is, however, opaque to longwavelength radiation and thus it does not allow the infrared radiation from the heated absorber plate to escape.

Karwa et al. (2002) have presented the following deductions of heat collection rate and pumping power equations for the flat plate solar air heater.

Introduction

Cooking accounts for a major share of energy consumption in developing countries. Fifty per cent of the total energy consumed in India is for cooking (Fritz 1981). Most of the cooking energy requirement is met by non-commercial fuels such as firewood (75%), agricultural waste and cow dung cake (25%) in rural areas. The fuel wood requirement is 0.4 tons per person per year in India. In rural areas firewood crisis is far graver than that caused by a rise in oil prices. Marginal villagers have to forage 8 to 10 hours a day in search of firewood as compared to 1 to 2 hours ten years ago. One third of India’s fertilizer consumption can be met if cow dung is not burnt for cooking and instead is used as manure. The cutting of firewood causes deforestation that leads to desertification. Fortunately, India is blessed with abundant solar radiation (IMD, 1985). The arid parts of India receive maximum radiation i.e. 7600-8000 MJ m^-2 per annum, followed by semi-arid parts, 7200-7600 MJm^-2, per annum and least on hilly areas where solar radiation is still appreciable i.e. 6000 MJm^-2 per annum. Therefore, solar cookers seem to be a good substitute for cooking with firewood.

Principle of cooking

The different methods of cooking of food are boiling, frying, roasting, and baking. For boiling of rice, lentils etc. the temperature of food being cooked is about 100° C while for other methods, high temperatures are required. Heat is supplied at the bottom of the vessel for frying and boiling purposes in conventional cooking. Roasting and baking is generally performed on open fire or in ovens, wherein food is surrounded by hot surfaces and heat is transferred to the food by radiation and convection.

For electromagnetic radiation, there are four “laws” that describe the type and amount of energy being emitted by an object. In the following sections, these four laws of radiation e.g. Planck’s law, Wien’s displacement law, Steffan-Boltzman law and Kirchhoff’s law are described briefly.

Planck’s Law

Planck’s law describes the spectral density of electromagnetic radiation emitted by a black body in thermal equilibrium at a given temperature T. The spectral radiance of a body, B_í, describes the amount of energy it gives off as radiation of different frequencies. It is measured in terms of the power emitted per unit area of the body, per unit solid angle that the radiation is measured over, per unit frequency. Planck showed that the spectral radiance of a body at absolute temperature T is given by

Introduction

The age-old necessities of life are food, clothing and shelter. The 20^th century had dramatized a fourth-energy. Energy starvation of the technological complex that maintains modern society may soon be as crucial a problem as feeding the hungry. Indeed, energy starvation could well precipitate more wide spread food starvation. Solutions for the energy crisis are strongly dependent on the technology of how energy is produced and used. To make a physical change in the world it is necessary to use four resources: energy, matter, space and time. How well a task has been performed can be measured in terms of amount of fuel consumed, the mass of material used, the space occupied, the hours of labour to accomplish it, and the ingenuity with which these resources are utilized. Squandering of irreplaceable energy resources, waste of materials, or large expenditure of space and time cannot be tolerated if the necessities of life are to be provided for all. Energy is intricately linked to development of a country. Growing demand on account of industrialization, urbanization, transportation and also increase consumption in rural areas is putting added pressure on energy supply network. Electricity generation has been increased from 1,400 MW in the year 1947 to 303,118 MW up to August 31, 2016 in India but still there is 10-15% shortage of power in peak hours and all villages are not electrified. Even many electrified villages receive 6 to 8 hours of electricity per day in India. To overcome this shortage there is a need to utilise new and renewable sources of energy. Fortunately, India is blessed with abundant solar energy. The arid parts of India receives much more radiation as compared to the rest of country i.e. maximum solar radiation i.e. 7600-8000 MJm^-2 (2111-2222 kWhm^-2) per annum, followed by semi-arid parts, 7200-7600 MJm^-2 (2000-2111 kWhm^-2), per annum and least on hilly areas where solar radiation is still appreciable i.e. 6000 MJm^-2 (1667 kWhm^-2) per annum (IMD 1985). In this chapter review of different solar thermal technologies in Indian perspective has been carried out.

Introduction

In Indian Thar desert regions, excessive heat is the major problem that causes human and animal thermal discomfort. Space cooling is, therefore, the most desirable factor for the inhabitants. Various examples of dwellings responsive to climatic constraints are found in vernacular architecture throughout the world. Compact cellular layout with minimum external surface exposure to the sun, whitewashed surfaces to reduce absorptivity, blind external facades, courtyards, vegetation to provide humidity and shade, and heavy buildings constructed from high thermal capacity materials are common passive features in most of the arid regions. Wind towers for cooling ventilation are well known in Iranian and Middle East architecture, which along with cooling of the air by earth and water evaporation keep the building comfortable in hot periods. Building underground to take advantage of the large thermal storage capacity of the earth is also used in Tunisia and central Turkey.

Introduction

The development of any region is reflected in its quantum of energy consumption. With a view to keeping the pace we have to grow our energy resources at a rate that commensurate to sustainable development. There is already shortage of electricity, reflected in power cuts notwithstanding enhancement of electricity production of several folds in the country. The problem is more severe in context with rural areas in India where some 70% of population live and have agriculture as the main occupation. Although statistically few thousand villages are yet to be electrified, the availability of regular supply in far off places has been a problem and the farmers are unable to derive benefits of electricity. The fast depleting kerosene is used for lighting and diesel oil for running agricultural machinery including pumps. In addition people burn firewood, agricultural waste and cow dung cake for cooking food causing irreparable damage to the ecosystem. According to Lester Brown there will be plenty of food to eat but no fuel to cook. The burning of cow dung deprives farmers the use of potential source of organic manure. Further, due to shortage of energy resources, farmers are unable to process the agricultural products to enable them to accrue more benefits. The situation is still worse in arid region where bio mass is scarce and there is no hydro electricity. In addition, farmers are unable to generate additional income due to lack of energy resources to run appropriate device in cottage industries. In this context, solar energy utilization has a tremendous potential for providing needful energy to the farmers for domestic, agricultural and allied applications leading to a sustainable development.

Introduction

In order to keep pace with the development there is rise in energy use but it has adverse effects on climate due to greenhouse gas emissions from burning of fast depleting fossil fuels. In this context, we need to harness and use more and more renewable forms of energy, especially solar energy that is plentiful on most part of India. Also, at several locations harnessing wind power and utilizing biomass could be effective alternatives. Solar based devices may also work in an integrated manner with small wind turbines as hybrid devices. At present, about 20% of the country’s installed electricity generation capacity is contributed by renewable sources e.g. wind, solar, bioenergy, hydro etc., which is about 71.325 Giga Watt (GW) as on 30^th June, 2018. In agricultural sector, energy is directly used for pumping irrigation water, operating different mechanized farm implements/tools and processing of foods. Share of agricultural sector in total energy consumption is about 7-8% and further increase in energy use from its present value of 1.6 kW ha^-1 to 2.5 kW ha^-1 is expected to meet the production target of next 20 years.

12.1 Meaning and Definition

A flow chart is a graphical or symbolic representation of a process. Each step in the process is represented by a different symbol and contains a short description of the process step. The flow chart symbols are linked together with arrows showing the process flow direction. This diagrammatic representation can give a step-by-step solution to a given problem. Flowcharts are used in analyzing, designing, documenting or managing a process or program in various fields.

11.1 Concept of Internet

The internet is not a computer network. It is a network of networks, linking computer to computers. Each runs software to provide access and view information.

In other words, internet is the connection of two or more networks so that a computer on one network can communicate with a computer on another network. The communication between two computers takes place such that the user does not have to worry about the technology used by the networks. The router, also called gateway, acts as an interface between two networks. The internet covers the entire globe and includes large, international networks as well as many smaller, local area networks (LANs). A user can access data, graphics, sound, software, text and people through a variety of services and tools for communication and data exchange. To get, provide and compile information, internet is the cheapest and fastest medium. Some of the popular tools and services available for communication and data exchange are as follows:

MS-ACCESS is a very powerful RDBMS (Data Base Management System) which manages data in term of special table called relation. Using MS-ACCESS you can manage all your information from a single database file. Database files are stored with extension. MDB.

ACCESS stores information in the form of tables and you build your database on its basis. Each row in a table holds all information’s & for a particular person it is called a record. And each column contains different pieces of information regarding that person, like Roll No, Name, Class, Address etc. Same as MSACCESS involves.

In ACCESS, you have few things known as Forms, Reports and Queries. They allow you to check and get desired information as and when required.

Solution:

Steps used for plotting the bar graph are as:

Step 1: Open Excel sheet and feed the above data as given in figure. 9.1.

Step 2: Select data and go to insert menu and select column chart. (figure 9.2)

Step 3: Select 2-D column, option first from dialog Box (figure 9.3).

Step 4: The desired output will be as shown (figure 9.4).

Remark The data labels, axis name, title of the graph etc. can be inserted through selecting option formatting chart area and add data labels by right clicking on the graph output (figure 9.5).

Some basic and most common analysis tools used in research are summary statistics. These can be analyzed in Ms-Excel through the following ways.

8.1 Summary Statistics

The following data are the yield (q/ha) of 60 varieties of paddy maintained in a research station for breeding trails:

53 ,74, 89, 65, 88, 65, 45, 70, 25, 80, 75, 85, 47, 63, 78, 44, 42, 51, 59, 86, 76, 48, 30, 38, 67, 78, 74, 68, 58, 59, 36, 76, 89, 48, 67, 69, 35, 46, 89, 75, 79, 56, 42, 41, 60, 65, 68, 69, 61, 63, 95, 94, 85, 83, 76, 71, 68, 62, 64, 62.

Calculate the summary statistics for the study of the data.

Solution: The summary statistics of the data using MS – excel can be obtained / analyzed through following steps:

Step 1: Open Excel sheet and feed the data as given in Fig. 8.1.

Step 2: Go to data menu and select Data Analysis. Sometimes this menu is not available in the data, it can be added through add in → check in, analysis tool pack → go. It will appear as the way showing in Fig. 8.1.

7.1 The Formula Palette

By clicking on the equals sign in the formula bar a drop – down panel called the formula palette appeared.

Consider an example, type number 25 in cell A1 and move the cell pointer to cell B2. type or click the equal sign in the formulabar. Adrop – down panel will display formula result = and wait for further inputs. Enter sqrt result to 5 at B2.

The dialog box divides into parts as

(a) Name of the function at the top left of the argument entry box.

(b) Below of this there is the definition or description of the function.

(c) Next, there is an entry explanation.

(d) Formula result.

Things to remember: Any statistical or mathematical function can be activated with = (equal to) sign.

Now we have to learn how to enter numbers and text, how to edit any erroneous entry and then how to do simple calculations. Open empty excel spread sheet by clicking on the start menu in the window tool bars, click on Programs and then click on the Excel menu item.

6.1 Entering Numbers

Numeric entries are constant, can be integers, decimal fractions, integer fractions and scientific nations which can be entered by typing the number in the active cell [cell which can be made active by clicking the mouse on the cell.

5.1 Introduction

According to Croxton and Cowden, “Either for one’s own use or for the use of other, the data must be presented in some suitable form”.

Now – a – days everybody uses tables in one form or another, and accountants use them quite often. The work become tedious, especially if you had to do for 12 columns for the 12 months of the year, and more so if there were some modification to be done after having set up the entire table. The Americans have a special name for such tables- spreadsheet which makes life easy.

Steve Jobs and Steve Wozniak two young Americans in 1976 AD developed the first desktop computer – The Apple which was not user friendly and needed programming for useful work. Dan Bricklin, Robert Frankston and Dan Fylstra proposed a program to turn the computer’s memory into a giant virtual spreadsheet where necessary corrections and modifications could be done fairly, quickly and easily and marketed the program in 1978 under the trade name VisiCalc. VisiCalc was soon succeeded by Sugercal and other computer programs which were more user friendly. The Lotus 1-2-3 designed and developed by Larry Kaplan put further step, which set the standard for many applications under the IBM, Now a days Microsoft under the leadership of Bill Gates had been working on the sidelines perfecting graphical user Interface (GUI) packages for the PC range of computers that would widen its reach. Now Microsoft office program Suite the integrated word processing, spreadsheet, presentation and data base management.

The spreadsheet package in Microsoft office is called Excel – our basic software, or program for this manual.

There are manyreasons for using computerstoday. Word processing is area which has proved computers to be powerful than ever. There are many different kinds of word processors availabletoday, however, most share the same basic functions and do not require advanced computeror typing skills to use. These basicfunctions include typing, savingdocuments,openingdocuments, copying, pasting, cutting printing, checking spelling, etc. The commonly used word processors include Microsoft Word, ClarisWorks, Word Perfect, Word Pad, Apple Works, etc.

4.1 Microsoft Word

4.1.1 Introduction

Microsoft Word, or Word as it is commonly known, is a software application that allows the user to perform word processing. The word processing means the creation, input, editing, and production of documents and texts by means of computers systems. Microsoft Word allowthe user to format text with attributes such as bold, italics, font size, and margins. It is part of the Microsoft Office suite of applications and is a highly popular programme for the workplace.

Microsoft Word was first released in 1983 under the name Multi-Tool Word for Xenix systems. Versions were later writtenfor several other platformsincluding IBS PCs running DOS in 1983,the Apple Macintosh in 1984 and Microsoft Windows in 1989. The latest release is Word 2010.

Windows is an operating system with a Graphical User Interface (GUI). GUI displays graphical objects called icons, which represents the items you use. Also it is a menu driven interface, where you have menus and submenus to perform even the most basic tasks such as File open, Copy, and Help etc. The user need not know the computer commands, he just need to know simple English language to operate windows. This feature of windows made it a widely accepted operating system. Also windows allow multiprocessing environment, where we can perform more than one task simultaneously. For example, if you need to take a print out of a very long report which may take a couple of hours, you may give the command, and leave it at the background, and then you can keep working on the other applications. Windows supports a very useful pointing device called a mouse. With the mouse you can go anywhere on the screen and click where ever you want and pass instructions.

2.1 Definition and Concept

An operating system (abbreviated as ‘OS’) is the program that, manages all the other programs in a computer. Every general-purpose computer must have an operating system. It provides support for the loading and execution of other programs which are known as Application Programs.

1.1 Introduction

The word ‘computer’comes from the word ‘compute’, which means to calculate or perform a sequence of operations. Hence, a computer is normally considered to be a calculating device, which can perform arithmetic operations at enormous speed.

In other words, a computer takes information (or input), processes it according to a set of instructions (or process), and gives back a result (or output). In this respect, it is very similar to a calculator, but obviously somewhat more complex.

A computer is a multi-function platform. The same machine can perform many different tasks by using different programs. The programs (also termed as applications) are written to do everything imaginable. In other words, we can say that a program is a set of instructions issued to a computer in order to get the desired output.

Definition

A Computer can defined as an electronic device that takes input from its user, stores, processes data and generates the required output as per the processing instructions given to it by the user.

Maps make it easier to interpret and communicate the results of any GIS analysis. R has a large number of packages for making thematic maps. Static mapping is straightforward with plot()methods provided by core spatial packages sf and raster. The use of plot() function have been discussed in the previous chapters. Another package for mapping isspwithspplot() having enough options for producing good quality thematic maps.It is also possible to create advanced maps using ggplot2. This chapter discusses, separately, preparing thematic maps from vectors and rasters with the above mentioned packages. A dedicated map-making package tmaphas been given special focus in this chapter.

Elevation or height data of land surface above a defined vertical datum is stored in the form of raster grids which is known as Digital Elevation Model (DEM). The DEM data can be processed to derive many useful parameters like slope, aspect, curvature, roughness, etc. This process of quantitatively describing terrain obtained from DEM data through computation of many variables is known as Digital Terrain Analysis. Digital terrain analysis has applications in the various fields like hydrology, ecology, geomorphology, etc. DEM data for digital terrain analysis are available from the satellites like ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), Cartosat, ALOS (Advanced Land Observation Satellite), and SRTM (Shuttle Radar Topography Mission).

In this exercise, we are going to discuss the derivation of some primary and secondary terrain attributes from DEM data in R software. For performing digital terrain analysis in R, following packages are required.

Supervised classification is the technique to classify raster image in which prior information about the classes in the form of training sites is provided by the user. Training site represents a homogeneous area in a particular class. The spectral response pattern of training sites is used to classify the raster image.The pixels of raster imagewhich have spectral response pattern similar to that of training sites of a particular class are grouped in that class. As there is some variability in the spectral response pattern of a particular class, therefore, multiple training sites per class are taken for supervised classification. Supervised classification of raster images can be performed by various methods. Conventional methods include maximum likelihood classifier, minimum distance to mean classifier, and parallelepiped classifier. Now a days, machine learning algorithms are also gaining importance in this context. These classifying algorithms are first trained or calibrated using the data of training sites.This trained model is then validated on the independent datasets for accuracy assessment. If the accuracy of the developed model is of acceptable level, then that model is used for the classification of raster data.

In this chapter, we are going to discuss the execution of maximum likelihood classifier and few machine learning techniques for supervised classification of raster image in R software. The training sites used in this exercise are in the form of points shapefile. The raster image is classified into six classesi.e. built- up, fallow soil, riverine sand, salt-affected soil, vegetation, and water. For performing this exercise, first of all,set the working directory and import the required packages.

Unsupervised classification is the technique of classification of raster data in which no prior information is provided by the user. Raster data is classified based on their spectral characteristics such that the pixels having similar spectral properties in the multispectral feature space are grouped together in one class. Unsupervised classification is mainly performed by clustering algorithms. The purpose of these algorithms is to minimize within cluster variability and maximize between clusters variability. The user must interpret and label the classes after performing unsupervised classification. This method of classification requires minimum data which makes it easy and quick, but user has toput efforts in identification of the classes. This method could be useful when user doesn’t have any prior knowledge about the study area. This method removes subjectivity and enables us to get the unique classes which might be unrecognized by the user. K-means is the most commonly used method for performing unsupervised classification. In this chapter, we have discussed K- means, CLARA and unsupervised random forest method for classification of raster data in R.

Raster transformations involve mathematical manipulation of raster layers to generate new raster images. These transformed raster images either contain the information of interest or highlight the features which are not discernible in the original image. Transformations are also performed to preserve the essential information in reduced number of dimensions or layers. In this chapter, we are going to discuss few most commonly used transformation tools listed below:

Remote sensing images have an important characteristic known as spatial frequency. Spatial frequency is defined as the number of changes in pixel value per unit distance. Few changes in pixel or brightness value over an area in an image are known as low spatial frequency area like agricultural fields, forests, water bodies, etc. If the changes are large, then it is known as high frequency area like roads, built up areas, etc. Spatial filtering is an approach for extracting quantitative spatial information by taking into account the neighbouring pixel values along with the independent pixel value. Therefore, filtering is a technique for modifying or enhancing an image. Filtering is done to improve the interpretability of raster image or to extract certain information from them. It is a neighbourhood operation because the value of any pixel in the output image is determined by applying certain algorithm on the neighbourhood pixels of the corresponding input pixel. The nature of algorithm to be applied depends upon the information which user wants to derive from the raster image. Filtering operations are mainly done for smoothening, sharpening, noise removal and edge enhancement. A filter is a kernel of small array which is applied to input pixel and its neighbouring pixels in the raster. There are many types of filters which can be applied to a raster image in R. These types with their respective R – code examples are explained further.

The sensors present over remote sensing satellites are designed to collect energy or radiance from the varied surface features having wide range of radiance i.e. from features like water bodies having very low radiance to features like sand or snow having very high radiance. But, most of the time, the study area may not have all these features; therefore, radiance values may remain confined to a narrow range. This reduces the contrast of the image, thereby decreases its visual quality. In this context, image contrast enhancement techniques are used for effective visualization and display of remote sensing image on computer screens or hard copy so that maximum amount of information can be derived through its visual interpretation.

Contrast enhancement of a digital image is a technique to modify the original grey levels of input image to the entire available range of the grey levels. This increases the contrast between various features in the image which improves the visual quality of original image. Contrast enhancement can be done by both linear and non-linear methods. Some of the commonly used contrast enhancement techniques with their implementation in R software are discussed in this chapter.

We need following packages for performing this exercise in R. raster package is required for importing of raster image and for performing contrast enhancement. RStoolbox package is also required for contrast enhancement as well as for visualization of raster data with ggplot2 package. rasterVis package is mainly used in this exercise for plotting histogram of the raster data.

The calculation of raster data statistics is very essential before performing some geoprocessing operations. Raster data statistics involve knowing the distribution of pixel values in a band, comparing the pixel values of two bands, extracting values of all bands for a particular geographical location or point, and computing statistics like mean, median, standard deviation, etc. at pixel level across all bands of raster dataset.

In R, codes to perform raster data statistics are present in raster package. The visualisation of raster data statistics can be done by rasterVis package. So, we need to import these packages in R in order to perform this exercise.

Many times, users have to work with raster datasets of different spatial and radiometric resolutions. So, there is requirement of bringing their resolutions to the same resolution in order to facilitate their processing. Resampling is the process of deriving pixel values from the existing image to the new raster image of different pixel size. Resampling is most common pre-processing step in remote sensing. It is done both at spatial and radiometric scale. At spatial scale, resampling is done either to increase the spatial resolution i.e. decrease pixel size known as Upsampling or to decrease the spatial resolution i.e. increase pixel size known as Downsampling. Radiometric resampling is performed to increase or decrease the radiometric resolution of the image. In R, resampling of raster image is performed by raster package. The details of the resampling methods and their respective R – code with examples are discussed in this chapter.

First of all, we need to import the raster image on which we wish to perform resampling operations by using following R – code. In R, resampling is feasible on one band only. So, we will import single band raster image.

Mosaic is the combination of two or more raster images into single image. Sometimes, single image captured by remote sensing satellite doesn’t completely covers the desired study area mostly. Therefore, users resort for mosaicking two or more images captured by satellite sensors from different paths so that the whole study area is merged into a single raster image to facilitate the image processing and analysis. The pixel size of the raster images should be same for mosaicking.

In most of the cases, there is spatial overlapping among the raster images from which we want to create mosaic. These overlapping areas are handled in multiple ways which is being discussed further using R – code example. For mosaicking raster images in R, mosaic function of the raster package is used.

Vector data analysis is performed to convert raw input vector data to output that provides new and meaningful information which ultimately supports decision making and disclose hidden patterns. There are various techniques to analyse vector data like buffering, intersect, union, and so on. Some of the most commonly used techniques with their R-code examples are explained in this chapter.

First of all, you need to import all the packages which are required for performing this exercise. These packages include rgdal and sp for importing and re-projecting vector data as well as raster and rgeos for performing vector analysis operations.

Raster sub-setting is done to select the desired area or bands of the raster image on which the user wants to perform analysis. Raster sub-setting is done in two ways:

Spectral subset: Spectral sub-setting is done to extract few desired bands from the multiband raster image which are required for analysis.

Spatial subset: Spatial sub-setting is done to minimize the area or extent of raster image to the desired area or extent for further analysis.

Raster sub-setting in R is done by functions available in the ‘raster’ package. The following packages are required in this chapter:

Coordinate Reference System (CRS) is a framework of coordinate based local, regional or global reference system used to represent the locations of geographic features. Coordinate Reference Systems (CRS) provide a standardized way to define the real-world geographic locations which further enable us to use common locations of geographic datasets for integration. The datasets from different sources and CRS are transformed to a common CRS in order to perform various integrated analytical operations like overlaying of data layers. Coordinate Reference Systems are mainly of two types:

1. Geographic Coordinate System: This is global or spherical coordinate system in which locations of features on earth are defined in terms of latitude and longitude values. Latitude and longitude are angles based on a point at the centre of the earth. In this system, locations are defined by degrees, therefore, distances cannot be measured accurately. The most common geographic coordinate system is the World Geodetic System 84 (WGS84).
2. Projected Coordinate System: In this system, locations of features are defined using cartesian x, y coordinates on a flat, two – dimensional surface. This system is based on sphere or ellipsoid projected on flat surface. Projected Coordinate System are generally referred as projec- tion. It is required for preparing maps on paper or computer screen. Un- like Geographic Coordinate System, projected coordinate system has ac- curate measurements of distance, angles and areas across the two dimen- sions. However, there is alwaystrade-offs between area, direction, shape, and distance in this system due to distortions created while representing ellipsoid or spheroid on a flat map. So, the projection type is selected based on its application. Most common projections are Universal Trans- verse Mercator (UTM), Lambert Conformal Conic and Albers Equal Area.

Vector data consists of points, lines and polygons with x, y coordinates known as vertices representing the geographic location i.e. latitude and longitude. Vector data depicts the shape of the spatial feature. Points consist of single x, y locations. Examples of points are tower location, sampling location, etc. Lines represent 1 – dimensional features like roads, streams, etc., and are composed of many or at least two connected points or vertices. Polygons denote 2 – dimensional features like boundaries with three or more vertices that are connected and closed. The vector data are represented in the following figure (fig. 5.1).

Sensors onboard the satellite records the intensity of emitted or reflected electromagnetic radiations in the form of digital number (DN) for each pixel. These digital numbers can be transformed to more meaningful intensity units like radiance or reflectance. This process of conversion of digital numbers to radiance or reflectance is known as radiometric calibration. Sensor specific information is required to conduct radiometric calibration.