eChapters

9.1. Introduction

The human population of the world will reach nearly 10 billion by 2050, boosting agricultural order-in a situation of humble financial development (FAO, 2017). At present, approximately 37.7% of total land surface is being utilized for crop production. From employment generation to contribution to National Income, agriculture sector is contributing a significant role in economic prosperity of the developed nations and is playing an active part in the economy of the developing countries as well. The growth of agriculture has resulted in a significant increase in the per-capita income of the rural community. Thus, a greater emphasis on agricultural sector is the need of the hour. For countries, like India, the agricultural sector accounts for 18% of GDP and provides employment to 50% of the country’s workforce. Development in the agricultural sector will enrich the rural development which will lead toward rural transformation and ultimately the desired structural transformation (Mogili and Deepak, 2018; Shah et al., 2019). The role of Artificial Intelligence (AI) has been obvious in the agricultural sector recently. The sector needs the impetus from AI based technology to maximize its yield including improper soil treatment, disease and pest infestation, big data requirements, low output, and knowledge gap between farmers and technology.

Introduction

The term "artificial intelligence" was originally coined in 1956 at a conference hosted by Dartmouth College. While the aspirations and dreams of the attendees were sky-high, the reality was sobering. The thought of citizens in space or self-driving cars within a decade was ancient science fiction. The building blocks of the required computing infrastructure, as well as AI, notably deep learning and statistical machine learning methods, were not even in the distant future. The aim was to create an artificial brain for a machine, where the "brain" is the so-called "expert system", a software program that can integrate a large knowledge base and use it to solve problems in the same manner that a human expert could. The intellectual merits and achievements of a few AI systems designed solely to narrowly excel in a specific problem became the actual problem of the nascent field.

12.1. Introduction

Artificial Neural Networks (ANNs) are computational models inspired by the way biological neural networks in the human brain process information. They consist of interconnected nodes or “neurons” arranged in layers: an input layer, one or more hidden layers, and an output layer. Each neuron in the network processes data and passes the results to the next layer. ANNs are designed to recognize patterns, learn from data, and make predictions or classifications based on that learning.

In an ANN, the connections between neurons are weighted, meaning each connection has a strength or value that determines the influence of one neuron on another. Through a process called training, the network adjusts these weights to minimize errors in its predictions. This is typically done using techniques like backpropagation, where the error is propagated backward through the network to update the weights (Abiodunet al., 2018).

ANNs are highly versatile and have been applied across various domains, including image and speech recognition, natural language processing, and decision-making systems. Their ability to model complex relationships in large datasets makes them a crucial component in the field of machine learning and artificial intelligence (Ahmed et al., 2023).

What is an Average ?

The word “average” is used in everyday life to describe where the middle number of a data set is. It’s the typical number you would except to find in a series of numbers. In statistics, the average is called the “arithmetic mean” usually just shortened to the mean. Both the average and the mean use the same formula :

Avg = total sum of all the numbers/ number of items in the set.

In other word, to find the average, add up all of the numbers in the set, and then divide by however many items you have. Let’s say you have 5, 10, and 15. Add them all up to get 5 + 10 + 15 = 30, then divide by 3(the number of items). The answer is 30/3 = 10.

In probability and statistics, you’ll see the following formula used

In this chapter, the essentials of basic statistics are explored, with a focus on obtaining all key measures of basic statistics with just a single click. Additionally, the process of creating informative data distribution graphs is also explained.

Basic Statistics

Basic statistics offer fundamental tools that help us to make sense of data. Below, you’ll find concise descriptions of key basic statistical measures.

i. Arithematic mean (average)

The average represents the central value of a dataset, calculated by summing all values and dividing by the number of data points.

ii. Median (middle value)

It is the middle value in a dataset when all values are arranged in ascending order. It’s less sensitive to outliers.

iii. Mode (most common value)

The mode is the value that appears most frequently in a dataset.

iv. Variance

Variance measures how much individual data points deviate from the mean. A higher variance indicates a greater spread.

v. Standard deviation

The standard deviation is the square root of the variance. It provides a measure of how spread out the data is.

vi. Range

It is the difference between the maximum and minimum values in a dataset.

3.1 Introduction
Since the dawn of humanity, the expansion of invention and technological innovation has made it possible to make a variety of goods to meet its needs. To get a competitive edge in the market, the manufacture of a product has three main goals, viz. efficient product planning, efficient manufacturing, and effective cost control.
These can be achieved through a random thinking, if the designer is fortunate to have the best design of a product of interest, which satisfies the needs of its customers. But it will rarely happen. Design thinking maximizes the possibility of happening of the best design in the journey of design a product in shorter possible time.
Design thinking, an emerging field in this direction, tries to create a product by utilizing the following steps.
i. Empathize
ii. Define
iii. Ideate
iv. Prototype
v. Test
3.2 Definition of Design Thinking
A design methodology called “Design Thinking” offers a problem-based approach to problem solving. By understanding the human needs which changes quickly, re-framing the issue in human-centric ways, generating a lot of ideas during brainstorming sessions, and taking a hands-on approach during prototyping and testing, design thinking is incredibly helpful in handling complicated challenges that are ill-defined or unknown. Anyone can use the Design Thinking approach to tackle complex problems that arise in our society, such as those in our businesses, governments, and other spheres of influence, if they have an understanding of these five steps of the process.

In statistics, sampling is concerned with the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population. Two advantages of sampling are that the cost is lower and data collection is faster than measuring the entire population. Each observation measures one or more properties (such as weight, location, colour) of observable bodies distinguished as independent objects or individuals. In survey sampling, weights can be applied to the data to adjust for the sample design, particularly stratified sampling. Results from probability theory and statistical theory are employed to guide the practice. In business and medical research, sampling is widely used for gathering information about a population. Acceptance sampling is used to determine if a production lot of material meets the governing specifications.

The sampling process comprises several stages

• Defining the population of concern

• Specifying a sampling frame, a set of items or events possible to measure

• Specifying a sampling method for selecting items or events from the frame

• Determining the sample size

• Implementing the sampling plan

• Sampling and data collecting

Cochan W.G. (1946). Relative Accuracy of Systematic and Stratified Random Sampling for a Certain Class of Populations. Ann. Math. Statist, 17, 164- 177.

Cochan, W.G. (1977). Sampling Technique, 3rd edition, Wiley Eastern Ltd., New Delhi.

Biological assay or bioassay

• A bioassay is an analytical method to determine the concentration or potency of a substance by its effect on living animals or plants (in vivo), or on living cells or tissues (in vitro).

• Bio-assays are thus a type of experiments with the object of comparing the efficacy of two or more substances, or preparations, like drugs, by using responses produced by them on suitable living organisms.

• The bio assay involves a stimulus applied to a subject and the response of the subject to the stimulus.

• When a stimulus is applied to a subject there may be a change in some characteristics of the subject. Such changes in the subject are known as responses.

• The response may be quantitative as in the case of weight or qualitative as in the case of mortality. The magnitude of response depends upon the dose.

• Normally, two preparations having a common effect are taken for assaying. One of the preparations is of known strength and is called the standard preparation and the other is of unknown strength and is called test preparation.

Introduction
Biotechnology represents a transformative force in modern agriculture, offering innovative solutions to enhance crop production, improve resistance to diseases, and address the global challenges of food security and climate change. Chapter 9 delves into the multifaceted role of biotechnology, particularly in the realm of genetic innovation, and how it is reshaping the agricultural landscape. The chapter begins by exploring the various applications of biotechnology in crop improvement. This includes the development of genetically modified organisms (GMOs) and transgenic crops, which have been engineered to exhibit desirable traits such as increased yield, pest resistance, and enhanced nutritional content. These advancements have significantly contributed to the global food supply, particularly in regions facing harsh climatic conditions or limited agricultural resources. A detailed examination of genomic tools and techniques follows, highlighting the sophisticated methodologies employed in biotechnology. Techniques such as CRISPR -Cas9 gene editing, marker-assisted selection, and genomic sequencing are discussed, showcasing their pivotal role in accelerating the development of improved crop varieties. These tools have enabled scientists to manipulate genetic material with unprecedented precision, paving the way for more efficient and targeted crop breeding programs. The chapter also addresses the role of biotechnology in crop protection, emphasizing its importance in developing crops that are resistant to pests, diseases, and environmental stresses. By enhancing the natural defense mechanisms of plants, biotechnology reduces the need for chemical pesticides, thereby promoting more sustainable agricultural practices and minimizing the environmental impact of farming. The importance of biotechnology and genetic engineering in ensuring food security is a central theme of this chapter. As the global population continues to grow, the demand for food is expected to increase dramatically. Biotechnology offers a viable solution to meet this demand by enabling the production of higheryielding and more resilient crops. This section also explores the ethical and regulatory considerations surrounding the use of GMOs and other biotechnological innovations in agriculture.

We must know our family, its members and their relationships. Let us first look at basic relationships which often figure in the questions. If you are conversant with the given items in the table below, half of your battle is already won. It is not required to mug-up these names, but instead make a clear picture in your mind as you go through each one of them. This will go a long way in retaining the details of these given shortcut tricks for solving Blood relations problems for logical and analytical reasoning. Read the following thoroughly before moving on

In most of the examinations, the questions related to boat and stream are asked. These questions are seemed to be very difficult but are very ticklish and easy to solve. The students need to concentrate on flow of stream upward and down ward. If the boat is moving towards the flow (downstream), the speed of boat and stream will be added and if the boat is moving towards opposite of stream flow (upstream), the distance covered by the baot will be equal to boat speed minus strean speed. Few important tips are given below to solve such type of questions.

Downstream and Upstream Motion

Downstream motion of a boat is its motion in the same direction as the flow of the river. Upstream motion is exactly the opposite. There are two parameters in these problems.

Q.1 We can apply the change of origin or change of scale or change of origin and scale both to the original data in case of applying ANOVA technique to make the analysis easy and speedy.

Q.2 The conclusions drawn from ANOVA based on transformed data or original data are different.

Cartography has much back history than what we seen in portable form by its name or definition. Many prehistoric painting of caves had their own time frame record of maps, artifacts and design have been preserved hoping that they are sufficient to the era of forgotten habit and culture, and treasure explicit ancient world. The ancient wall painting of seventh millenium BC, might have indication of oldest maps in the world and the painting is assumed to be the location of ancient city Antolia as Mellaart, depicted 1967. Onward 600 BC, the modern form of cartography started to develop map. The pioneer contribution in map designing and development, old Greek and Roman were main contributor in this direction. One Greek Philoshopher Ptolemmy, multi-talented genius are recalled in this context, who later credited with first documented map of world during production of Geographica, is a treatise on cartography, very soon one Arabian Scholar Muhammad Al-Idrisi prepared a medieval Atlas involving knowledge of world collected by Arabic merchants in 1154.

6.1 Introduction
The success of design thinking depends on its applicability in industries and service organization. The applications of design thinking applied to product development should focus on the needs of users. The design thinking can also be applied to design new processes as well as improve an existing process.
6.2 Case Studies
The section presents several case studies of applying design thinking in realities as listed below.
1. Design thinking in eye care
2. Design thinking in foundry
3. Design thinking in FMG company
4. Design thinking in auto industry
5. Design thinking at retail outlet
6. Design Thinking in Flight Reservation System
7. Design thinking in winding fabric on roller in textile mill
6.2.1 Case study: Design Thinking in Eye Care
Eye care is a crucial part of health care system. There are many factors that are responsible for poor eye vision. Normally in the early stages, viz. children and adults up to 40 or 45 years of age may have eye power issues, which are managed

Quantitative studies are preferred over qualitative studies for their relatively higher objectivity. But many of the times, complexities involved in the occurrence of phenomenon, its causes, and consequences demand for in-depth analysis of a single unit of study. Whenever we want to study a single unit in depth both qualitatively and quantitatively then case study is the best method of research. Case study is a best tool to use, when we are interested in exploring a new phenomenon on which there is very less scientific information available and also when we are interested in formulating new hypothesis for causal study.

We have seen the procedures of testing hypothesis about population parameters in chapter 11. Frequently our interest is to test whether a set of observed values are in agreement with those which would occur if some specified hypothesis were true. The X² statistic provides a measure of agreement between such observed and expected frequencies.

The X² distribution has a number of applications in testing of hypotheses. It is used to test, (1) the independence of attributes, (2) the  goodness of fit, (3) the homogeneity of variances, (4) the homogeneity of correlation coefficients, and (5) the linkage in genetic problems. For applying X² test procedure we have to use only the actual observed frequencies and not the percentages or ratios. Further, the samples should be independent and the observations within a sample should be non-overlapping. The expected frequency in each category should be more than 5. The number of observations should be large, say, more than 50.

12.1 Chi-Square Variate

The square of a standard normal variate is known as a chi - square variate or chi square statistic with one degree of freedom. Thus if , then the variate Z defined as 

Z is a standard normal variate with mean zero and variance unity (one) and  = Z² is a chi - square variate (statistic) with one degree of freedom.

In general if xi, (i=1,2,.......n) are n independent normal variates with means μ_i and variance , then  is a chi - square variate (statistic) with n degree of freedom. It is denoted by the notation  and follows chi-square distribution. Symbolically, it is expressed as .

12.2 Chi-Square Distribution

Let  be a chi - square variate with n degree of freedom. Then its probability density function is written as