eChapters

Ixora is an evergreen flowering shrub valued for its attractive clusters of brightly coloured flowers and its suitability for loose flower production, religious offerings, garlands and landscaping. The crop performs well under tropical and subtropical conditions and is appreciated for its extended flowering period. This chapter presents the botanical characteristics, origin, distribution, genetics and important varieties of Ixora. It discusses suitable climate and soil conditions, propagation, land preparation, planting, nutrient management, irrigation and cultural practices. Special attention is given to pruning and canopy management for improving flowering. The chapter also covers important insect pests and diseases, harvesting, postharvest handling and yield, providing practical guidance for commercial Ixora production.

Jasmine is one of India’s most important fragrant loose flower crops, valued for garlands, Veni, Gajra, religious ceremonies, festivals and perfume production. Flowers and buds are also important raw materials for jasmine oil, cosmetics, soaps and other fragrance products. The chapter describes jasmine’s origin, distribution, genetics and commercially cultivated species, particularly Jasminum sambac, J. auriculatum and J. grandiflorum. It discusses climate and soil requirements, propagation, planting, nutrient management, irrigation, pruning and other cultural practices. Major pests and diseases and their management are included. The chapter also explains harvesting, postharvest handling, marketing and the value-addition potential of jasmine flowers and essential oil.

In this chapter we will look at the Expert system which can guide and assist an individual once it is provided with the enough Knowledge to do so. Here we will discuss about expert system and its architecture with proper steps of making an expert system in any field by several tools which are available to do so. For expert systems the most important is Knowledge Engineering which is the collection of Knowledge from an expert and Engineering it in such a form that a machine could understand it.

Introduction

One of the pioneering fields of AI study is Expert Systems (ES). It is proposed by researchers in the computer science department of Stanford University. An expert system is a computer system which emulates a human expert’s decision-making skills. Expert systems are developed by reasoning through information structures, representing laws, rather than through traditional procedural code, in order to overcome complex problems.

Expert Programs use professional expertise systematically to address human expert problems. An expert is an individual with experience in a specific field. The expert has expertise or special credentials which most people do not learn or care about. An expert can solve or solve problems more effectively than people can solve. Expert System actually emulates all these qualities of expert and solve the problem in a better way and fast.

Today, Expert System (ES) is used in the fields of finance, research, engineering, technology, medicine and many others where there is a well-defined problem area. The fundamental principle is that an expert method should also define the steps to illustrate why a problem can be solved. Unless an individual can justify his reasoning for Expert System.

10.1. Introduction
In the production process, the farmers combine the required input factor in various proportion. This type of usage of input by the farmer gives way for the operation of law of returns. In the production process, when the single input factor is varied keeping other required factor constant, the relationship take place between single variable input and the consequent output pertain to either one or a combination of the following relationship.
1. Law of increasing returns;
2. Law of constant returns; and
3. Law of decreasing returns.
10.1.1. Law of increasing returns
According to the law of increasing returns, each additional (or marginal) unit of input leads to a proportionally larger increase in output compared to the previous unit. In other words, the productivity of inputs rises as more units are employed.

In production one or a combination of the following relationship are commonly observed:

1. The law of constant marginal returns (productivity)

 2. The law of increasing marginal returns (productivity)

 3. The law of decreasing marginal returns (productivity)

As we have seen how artificial Intelligence can be useful in agriculture but the nest question always come in mind is that from where and how we can start to learn and what are the model of learning an artificial Intelligence. As Artificial Intelligence is a huge part of Computer Science which means we need an language as medium to perform it and we always prefer Python for that which is very dynamic and easy to learn. In this chapter we will learn about python basics so that in further implementation of artificial Intelligence it will be helpful to us.

I. Introduction

Python is a remarkable language that can claim to be easy and strong. You will be amazed to see how simple it is not to focus on the language you are programming, but on solving the problem. Guido van Rossum was the creator of the Python language which is named after the BBC show “Monty Python’s flying Circus”.

Python is formally introduced as:

“Python is an easy to learn, powerful programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming. Python’s elegant syntax and dynamic typing, together with its interpreted nature, make it an ideal language for scripting and rapid application development in many areas on most platforms.”

10.1 Introduction

Least square theory is a statistical technique used to estimate the parameters involved in the regression model in such a way that the sum of squares of errors (residuals) is minimum (least). We have generally two methods of estimation of population parameters. These are (i) Ordinary least square (O.L.S.) method and (ii) Maximum likelihood (M.L.) method. The method of ordinary least square (O.L.S.) is used extensively in regression analysis primarily because it is intutively appealing and mathematically much simpler than the method of maximum likelihood estimation (M.L.E.). In the context of lineaer regression analysis, the two methods generally give similar results.

Introduction

Agribusiness refers to the industry that deals with farming and the selling of agricultural products, including the cultivation of crops, their processing, and their distribution to retailers. Producing and distributing crops, agrichemicals, fodder, breeding, farm equipment, seed supply, raw and processed commodities of food and fibre, warehousing, transporting, packing, marketing, retailing, soil testing, and testing for nutrient deficiencies are all part of the agribusiness industry.

It's important for an excellent brand name to provide a solid basis on which a company can grow. Companies need to identify who they are trying to sell to. Will people have no trouble pronouncing it? The tongue shouldn't have to work too hard to say it. Can you pronounce the name easily? Your brand name needs to be audible to consumers. Have you tested the name's spelling ability?

Choosing what aspect of agriculture or agribusiness you want to enter is the f irst step. So, you can't make a call like this until you have a firm grasp on your company's long-term goals, operational strategy, and financial situation. You can still enter the market if you aren't an expert in the equipment, products, grains, soil, seeds, or plants involved; nevertheless, you should go in with your eyes wide open, a well-thought-out business plan, and a competent team.

Introduction

Eastern India comprises of Odisha, Chhattisgarh, Jharkhand, West Bengal, Assam plains, Bihar, and eastern Uttar Pradesh with a total geographical area (GA) of 71.84 million ha with highest in Odisha (15.57 million ha) and lowest in Assam (7.84 million ha) (Bhatt et al., 2016). Net cultivated area in the region is 31.21 million ha (about 44% of GA) with a cropping intensity of 150%. The total food grain production from the region is 86.71 million tonnes (31.51% of the total production). Eastern region is having population of 406 million (33.6 % of total) with the density of 567 person/sq km, which is much higher than national density i.e., 382. 

INTRODUCTION

According to the World Bank, youth present an opportunity for a sustainedeffort to participate in nation’s development process because they possess greaterenergy and potential and have the capacity to drive positive change. Stimulating growthof employment in the agricultural sector remains paramount priority in countries with alarge agricultural sector, as improvements in agricultural productivity can generatemore and better jobs in most developing countries (World Bank, 2008). Youth who possess non-farm skills are 1.4 times more likely to move out ofagriculture. Their age has significant role in favor of shifting out of agriculture, becauseoccupational mobility was higher among younger farmers (Sharma and Bhaduri, 2012). India has the largest youth population in the world. About 70 per cent of India’spopulation is below the age of 35 years, making India the youngest nation in the World. According to some estimates, the proportion of population under 25 years in India is 51per cent. Some experts refer to this large proportion of youth as “demographicdividend” because greater proportion of the population is young and in the workingage-group which can lead to economic growth. However, out of the youth population of460 million, only 333 million youth in India are literate and unemployment rate ishighest (10.6 per cent) among youth (CIA World Factbook, 2012).

Limitations
• Data Reliability and Availability: The availability and dependability of data is one major drawback. Accurate assessments of livelihood dynamics can be hampered by the absence of complete and current data that is frequently present in rural areas. Research results may also be impacted by data inconsistencies seen in local reports, surveys, and official sources.
• Complex and Dynamic Variables: Because livelihood transitions are multifaceted and influenced by a variety of policy, environmental, and socioeconomic factors, it can be difficult to separate out and evaluate individual components. The interaction of these variables may make it more difficult to determine the precise cause and effect correlations.
• Temporal and Spatial Variability: The dynamics of livelihood can change dramatically over time and between Bengal’s many regions. The generalizability of results and suggestions may be limited by the nonuniform application of research findings across domains.
• Implementation Difficulties: Implementation issues with well-designed policies and initiatives can include local opposition, corruption, and ineffective bureaucracy. These difficulties may impede the efficacy of interventions and make evaluating their effects more difficult.
• Response Variability and Adaptation: Rural communities’ responses to changes in the economic, environmental, or social spheres can vary greatly. Adaptation techniques can be influenced by local cultural norms, resource availability, and unique household situations, making it challenging to create universally applicable solutions.
• Future Uncertainties: The trajectory of livelihood transitions may be impacted by uncertainties about future technical advancements, economic situations, and the effects of climate change. Accurately predicting these changes is intrinsically difficult, which may compromise the validity of subsequent estimates and suggestions.

3.1 Definition

 Let f (x) be the function of a random variable ‘x’. If there exists a number ' ' ?

 such that f(x) may be made as close to ' ' ? as we proceed to choose x close to

 a given number ‘k’, then we say that the limit of f(x) as x approaches to k, is ' ' ?

 and we write it symbolically as

 x k

 limf(x)

 ?

 ??

 Example 3.1 : We are giving some examples as below:

 Example 3.1.1 : A function f (x) may become infinite as x tends to a value ‘c’

 and we write that

 x c

 limf(x)

 ?

 ??

 Example 3.1.2 : Consider the function 1 f(x) x ? . Then we say that as x ??,

 f(x) tends to zero and write it as :

 x

 1 lim 0

24.1 Introduction

Linear programming is a budgeting technique which is more refined and systematic than conventional budgeting in determining the optimum combination of enterprises or inputs so as to maximise the income or combination of enterprises or the income or minimise costs within the limits of available resources. It may be defined as “The analysis of the problem in which a linear function of a number of variables is to be maximised or minimised when these variables are subject to a number of restraints in the form of linear inequalities”, In linear programming models, the objective of the typical farm i.e maximisation of net profit or cost minimisation is achieved through an optimal plan generated from its solution.

In the recent years it has been increasingly realized that only increase in physical production of crops no longer guarantees the smooth development of agriculture at both the farm and the national economy levels unless and until it is linked with efficient system of marketing. As such, now a day in planning for agricultural development specially in developing economies marketing system had drawn much more attention than customarily assigned to it. Efficient marketing of agricultural commodities in general and fruits crops in particular plays an important role in safeguarding the interest of producer as well as consumer.

Review of literature enables a researcher summarizing and quoting relevant works to explain the topic and objectives stated in the thesis study. This is a fantastic scholastic activity to justify and validate the reasoning and outcome of the research study conducted.
Following is the categorized review of literature for a systematic illustration and interpretation of the issue and axioms:
Source of risk
Boris and Radivoj (2017) classified agricultural risk and uncertainty into five primary management-related categories: production and technology, pricing and the market, money, law, and personnel. Production and technology risks encompass challenges such as crop diseases, pests, and technological failures that can adversely affect yields. Managing these risks involves implementing advanced farming techniques, investing in new technologies, and employing effective pest and disease control measures. Pricing and market risks include price volatility and issues related to market access and consumer preferences. Strategies to mitigate these risks include using futures contracts, diversifying markets, and conducting thorough market analysis. Money risks pertain to financial management, including fluctuating interest rates, capital investment needs, and cash flow challenges. Effective management here involves maintaining a robust financial plan, leveraging insurance, and building financial reserves. Law risks relate to compliance with regulations on land use, environmental standards, and food safety. Addressing these involves staying up-to-date with legal requirements and seeking legal counsel when necessary. Personnel risks involve labour shortages, skill gaps, and workforce management issues. These can be managed through investments in training, fostering a positive work environment, and implementing effective recruitment and retention strategies. Addressing these five areas comprehensively helps agricultural operations reduce the likelihood and impact of adverse events, enhancing resilience and sustaining productivity and profitability amidst uncertainty.

Logical Framework Approach (LFA) Background and Description, Variation of LFA

Definition The Logical Framework Approach or LFA is a systematic and analytical process for objectives-oriented project planning and management

Background: The Logical Framework Approach (LFA) was developed for USAID in the 1960s. Since then, it has been adopted and adapted by many other international development organisations. Among them was the German agency GTZ, which derived its Goal-Oriented Project Planning (ZOPP) from it.

 

Introduction to LFA – Background and description
The Logical Framework Approach (LFA) is a methodology mainly used for designing, monitoring, and evaluating international development projects. Variations of this tool are known as Goal Oriented Project Planning (GOPP) or Objectives Oriented Project Planning (OOPP). The logical framework Approach is a method to develop the project’s goals and activities and identify key information (such as risks) through a participatory approach. Basically, this means that instead of having a single person who designs the project all on his own, you seek the involvement of all concerned parties – also known as the stakeholders. The most important stakeholders are the potential beneficiaries of the project, or the target group.Getting the involvement of your target group and other stakeholders not only ensures that your project is well designed. It also makes sure that everybody is aware of the project and what it is supposed to change. It allows people to get involved, and develop a feeling of responsibility for the project and its results.This is known as developing ownership for the project’s outcomes, and it is important to make sure that the positive realizations of the project are embedded locally and durable over time. People will care about the project and take care about its results.

Logical Framework Approach (LFA) is a framework for designing change process, monitoring progress and evaluating impact. It is a powerful tool for project design, management, assessment, follow up and evaluation of projects. In designing change process, monitoring progress and evaluating impact, ordering the change process in a logical sequence where inputs or resources identify as necessary means for performing activities are very important. Specific activities are then identified because they result in outputs which are necessary to achieve objectives. The two terms i.e.; log frame and logical framework approach are sometimes confused by many. The log frame is a document whereas the logical framework approach is a project design methodology. LFA originally developed by the American Defense Department and was first formally adopted as a planning tool for United State Agency for International Development (USAID) by the end of 1960’s and today it is being used, in one form or other and with different names, by major organisations in the field of development co-operation and private industry. Among these are British DFID, the OECD expert group on Aid Evaluation, ISNAR International Service for National Agricultural Research, Germany’s GTZ and the Inter-American Development Bank. It has also been widely adopted by NGOs, though not without reservations and concerns by some. In the 1990s it was often mandatory for aid organisations to use the LFA in their project proposals.

Lotus is an important aquatic flowering plant with exceptional cultural, religious, ornamental and commercial significance in India. Its flowers are extensively used in worship, religious ceremonies, decoration and traditional floral practices. The chapter introduces lotus and discusses its botanical characteristics, origin, distribution, genetics and important varieties. It explains the environmental conditions required for successful cultivation, including suitable water, soil and climatic conditions. Propagation, pond or field preparation, planting, nutrient management, irrigation and routine cultural practices are described for commercial production. The chapter also covers pest and disease management, harvesting of flowers, postharvest handling, yield and potential uses, providing practical information for growers and students of floriculture.

In this chapter of machine learning algorithms we will cover the basic algorithms of machine learning with their implementation using dataset and discuss their pro’s and cons of algorithms for better understanding and how to use them in any project or with any dataset to get an correct result which can useful for research or study.

Key Component

We defined a data set composed of audio, images and binary labels that provided an understanding of how we could train a model from snippets to classifications. This sort of problem is one of many kinds of machine learning problems when we try and predict a specified unknown label given known inputs, provided that a dataset of examples for which labels are known is called supervised learning. Let’s discus what are the major key components we have to get there