eChapters

Introduction
Current state of global warming and climate change affecting crops through altered pest and disease incidence patterns, changes in temperature and CO2 levels which adversely affected the crop production (Habib-ur-Rahman et al. 2022). This threat is particularly pronounced in food-insecure regions, especially in Asian countries. Climate change not only jeopardizes crop and livestock health but also impacts soil and water resources and livelihood of rural communities. Furthermore, it contributes to food insecurity by increasing prices and reducing overall food production. The complexity of these effects highlights the need for modern agricultural techniques to mitigate risks and ensure global food security (Habib-ur-Rahman et al. 2018).
Crop improvement, an integral facet of agricultural development, remains a cornerstone in addressing the burgeoning global food demands. Conventional plant breeding methods have played a pivotal role in developing improved crop varieties that exhibit enhanced quality, higher yields and resilience to biotic and abiotic stresses. However, plant breeding methods involving continuous selection and intermating among closely related individuals can result in a reduction of genetic diversity within a cultivated crop species. The narrow genetic base makes crops more vulnerable to emerging threats, such as new pests or diseases and limits their ability to cope with changing climatic conditions. In India, the Green Revolution along with substantial plant breeding activities, led to a significant increase in crop yields, particularly in staple crops like wheat, rice and maize. However, one of the major consequences of green revolution is the development of narrow genetic base in cultivated crop species (Begna 2021).
Moreover, the reliance on a narrow genetic base, a consequence of crossing only two parents, curtails the exploration of broader genetic diversity. This limitation restricts the potential for effectively combining and expressing desired traits in the resulting crop varieties. Most of conventional plant breeding techniques were primarily focused on monogenic traits, while many agronomically important traits are quantitative in nature, controlled by polygenes, and influenced by the environment. This poses challenges for breeders relying only on phenotypic selection. Notably, traits influenced by multiple genes, such as resistance to diseases or tolerance to environmental stresses, are particularly challenging to manipulate using traditional breeding methods.
Quantitative trait loci (QTLs) are genomic regions that influence polygenic traits, first described by Geldermann (1975). Identifying QTLs associated with improving yield and quality traits is pivotal for catalysing a new Green Revolution (Arrones et al. 2020; Pradhan et al. 2015). Marker-assisted selection (MAS), introgression breeding and QTL pyramiding offer efficient avenues for developing improved cultivars. However, identifying QTLs associated with quantitative traits remains a challenge for plant breeders (Arrones et al. 2020). The basic requirement for the detection of QTLs involves appropriate mapping population, marker systems, phenotyping methods and suitable experimental designs (Arrones et al. 2020; Collard et al. 2005)

Introduction

Ligation of DNA fragment is one prime important in genetic engineering because without ligation we could not transfer the interest gene.DNA cloning requires the DNA sequence of interest to be inserted in a vector DNA molecule. For this, both the vector as well as insert DNA is prepare by digestion with compatible restriction enzymes, so that the ends produced during digestion is complementary in both. When setting up ligation, it is important to consider the permutations that can occur and bias the relative concentration of DNA accordingly. Usually a 5- to 10-fold excess of insert over the vector DNA is the norm. This ensures that enough ligated product will be produced in the right orientation.

Principle of DNA Ligase Mechanism

The process of connecting two pieces of DNA together is called ligation, and is catalyzed by an enzyme called ligase. The ligase used most often in molecular biology is derived from the T4 bacteriophage, and uses ATP to supply the energy necessary for the reaction. In addition to ATP, T4 ligase requires DNA with a 5´-phosphate group and free 3´-hydroxyl groups.

Applications

Ligation of blunt or cohesive-ended DNA fragments.

Repair of nicks in double-stranded nucleic acids.

Summary

Over the past several decades, the exponential population and social civilization expansion have been accompanied by rapid industrialization. As such, there has also been a proportionate expansion in the global energy demand. Till date, crude oil has been the only source to meet the increasing energy demand. Nowadays ethanol is the most acceptable product in the fuel market. The importance of ethanol is increasing due to a number of reasons such as global warming and climate change, limited reserves of fossil fuel and reduction on imports of transport fuel. Lignocellulosic biomass is an abundant renewable source for commercial alternative fuel. Maximum bioethanol production depends on the whole process and optimum process parameters itself. Bioethanol from lignocellulosics is a type of second generation biofuel. Many researchers have been focused on bioethanol production from renewable sources. This chapter highlights the processing steps for second generation bioethanol production from lignocellulosics.

Lilium is a high-value bulbous cut flower known for large, attractive and fragrant flowers and extensive international demand. This chapter examines its taxonomy, origin, genetics, botanical description and major hybrid groups, including Asiatic and Oriental lilies. It explains bulb preparation, storage, planting, climate, soil, greenhouse management, nutrition, irrigation and crop regulation. Pest and disease management, harvesting, grading, postharvest treatment and yield are also presented. Special attention is given to commercial standards for Oriental and LA Hybrid lilies, including stem length, flower number, bunching and presentation. The chapter provides a comprehensive foundation for modern commercial lily production.

Limonium, commonly known as statice, is an important cut and dried flower valued for its attractive inflorescences and excellent keeping quality. This chapter covers its origin, taxonomy, botanical features, varieties and commercial significance. It discusses climate, soil, propagation, planting, irrigation, nutrient management and crop protection. Harvesting at the correct flowering stage, immediate hydration, cold storage and floral preservatives are emphasized for maintaining quality. The chapter also provides commercial grading standards for varieties such as Misty Blue, Saffron and Glow, along with yield information. These practices support both domestic and export production of high-quality Limonium stems.

Linkage

When two or more genes are located on the same chromosome, they are said to be linked. Genes on different chromosomes are distributed to the gametes following the law of independent assortment. However, genes located on the same chromosome, tend to stay together during gamete formation. For example, results of test-crossing dihybrid individuals will produce different results depending upon whether the genes are linked or are located on the same chromosome. This is illustrated as follows:

1. ICAR-Central Island Agricultural Research Institute, Port Blair

2. ICAR-Central Arid Zone Research Institute, Jodhpur

3. ICAR-Central Avian Research Institute, Izatnagar

Directorates/Project Directorates

1. ICAR-Directorate of Groundnut Research, Junagarh, Gujarat

2. ICAR-Directorate of Soybean Research, Indore, Madhya Pradesh

1. ICAR-National Bureau of Plant Genetics Resources, New Delhi

2. ICAR-National Bureau of Agriculturally Important Micro-organisms, Mau, Uttar Pradesh

1. ICAR-National Research Centre for Banana, Trichi

2. ICAR-National Research Centre for Grapes, Pune

3. ICAR-National Research Centre for Litchi, Muzaffarpur

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.

M13 is a single stranded filamentous DNA phage. Vectors have been developed from M13 phase by Joachim Messing (1983), which are suitable for preparation of single stranded DNA required for sequencing by Sanger’s method. These are derivatives of M13 wild type (size 6.407 kb).

Machine learning provides powerful approaches for extracting useful information from complex plant, environmental and agricultural datasets. This chapter examines the principles and applications of machine learning in plant data analysis. It introduces different types of plant data, including phenotypic, genotypic, environmental and image-based information, followed by preprocessing, normalization, feature extraction and feature selection. Supervised learning techniques such as regression, decision trees, random forests and support vector machines are discussed for applications including crop yield prediction and disease detection. Unsupervised learning, clustering, principal component analysis and advanced ensemble and deep learning approaches are also considered, along with model evaluation, data limitations and future developments.

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

As we know there are different branches of artificial Intelligence and machine Learning of one of them which has become a most advance and widely used branch of artificial Intelligence in every field of this generation whether it may be either electronics, civil, mechanics, Bioengineering, medical or agriculture etc., In this chapter we will study about machine learning and its types with the most common algorithms of machine learning which are being mostly used in different scenarios and their implementation using python language has been discussed in the implementation part of this book. Here we will also see where machine learning can be used.

i. Introduction

Machine learning is a subset of Artificial Intelligence and one of the most important part of artificial Intelligence. It is a field of Computer science which is totally differs from traditional computational approaches. In traditional approaches, a set of instruction is explicitly provided to the algorithm to solve any problem. Whereas in Machine Learning is a domain of an AI which uses a statistical analysis and can enables a system to always learn from its data without any external inputs in order to give an essential output. Due to this machine learning makes computers in advance to make model which provide decision based on input data.

Machine Learning involves a lot of algorithms which usually based on their requirements and functionalities to use where and when. To make a machine learning model we always require the data on which model can be made. Machine learning uses the algorithm which continuously learns from the data available as training data. A machine learning model is generated after the training of algorithm from the data. After the training completes, when trained model is provided with other real-life data then you can get an essential outcome. For example, in figure 3.1 Machine learning Model we use a predictive algorithm and make a model by providing it with some data, and later on we can receive prediction’s based on real data as input.

Cells Contain 4 Major Types of Giant Molecules (Macromolecules, Polymers)

Carbohydrates

Lipids

Proteins

Nucleic Acids

BIOLOGICAL POLYMERS ARE MADE FROM ABOUT 60 SMALL BUILDING BLOCKS

Cells contain thousands of types of giant molecules (macromolecules, polymers)

Polymers are macromolecules made from a single building block molecule Most of the macromolecules are made from about 60 types of small building block molecules

Most macromolecules are linear: building blocks linked head to tail- a few have branches (some polysaccharides)

This is what you would get if you heated cells in hydrochloric acid overnight at 110 degrees C: