eChapters

Population Growth
Around 1810, it took all of human history for the population to reach one billion. This quadrupled to 2 billion people in just 120 years (1930). Then, in 45 years, 4 billion (1975). From 4.4 billion in 1980 to 5.8 billion in 1997 and 7 billion now, the world’s population has increased. It is predicted that in less than 40 years, that number will double again to 11 billion. This indicates that more people than ever before are joining the group every day.
The United Nations Population Fund (UNPF) predicts that by 2025, there will be 10 billion people on Earth, and by the next century, there will be 14 billion.
Birth rates have decreased along with mortality rates, although death rates have decreased more quickly. In most affluent countries, there are approximately 1.6 births for every death, but in less developed countries, the ratio is closer to 3.3 births for every death. Every twelve years, a billion more people live on the planet.
Reasons Behind the Quick Population Growth
• A rise in the distribution and production of food.
• Gains in the field of public health
• medical technology, as well as improvements in education and living standards in many poor countries.

A drop spillway is a type of structure used to release extra water from a dam or reservoir. It is characterized by a steep or nearly vertical drop in the water channel, which allows excess water to be discharged safely while reducing its energy. The main components of a drop spillway include a crest or weir at the top, a drop section for the falling water, and a downstream channel equipped with energy dissipation features to control erosion. The suitability of a site for a drop spillway depends on factors such as the terrain, soil stability, and flow conditions.
A spillway in general is designed to release surplus water from a reservoir once it reaches its storage limit. Water usually flows over the spillway during flood situations, when the inflow exceeds the dam’s capacity. Typically located at the top of the reservoir, a sphelps prevent water from overtopping the dam. In addition to spillways, some dams have bottom outlets controlled by gates or valves to release water. In cases where there is no overflow spillway, dams may rely entirely on these bottom outlets for flood control.
DEFINITION OF SPILLWAYS
A spillway is a structure that enables the controlled release of water from a dam or levee to flow downstream, usually into the original riverbed. Before examining its different types, it’s essential to understand why a spillway is necessary for dams.
A spillway acts as a channel to safely discharge surplus floodwater once a reservoir reaches its full capacity. Every dam is equipped with a spillway as a critical safety feature. Spillways can be integrated within the dam itself, positioned at its edge, or constructed as separate entities. It is essential that the spillway has adequate capacity; an undersized spillway could cause the dam to overflow, potentially leading to severe damage or complete failure of the dam.

Abstract

Conversion of an already arid land into a desert through indiscriminate human actions magnified by droughts. Such actions include overgrazing, repeated burning, intensive farming, and stripping of vegetation for firewood. One quarter of the earth’s surface is threatened by desertification - an area of over 3.6 billion hectares. Since 1990, Six million hectares of productive land are lost every year due to land degradation. Desertification threatens the liveihoods of more than one billion people and has already made over 135 million people homeless. Desertification does not occur in linear, easily mappable patterns. Deserts advance erratically, forming patches on their borders. Areas far from natural deserts can degrade quickly to barren soil, rock, or sand through poor land management.

Introduction

Global warming directly ref lects on rising sea levels due to melting of ice caps and natural expansion of sea water as it becomes warmer. Consequently, areas adjoining the coast and wetlands could be frequently flooded and the distribution pattern of monsoon rains may alter, through more intense downpours, storms and hurricanes. The meteorological data available at the Annamalai University, for the tail end of the Cauvery river delta region of Tamil Nadu State, India, shows that the average annual rainfall during the last 10 years segment has increased by 233 mm compared to the average of the previous 10 years segment (1588 mm and 1355 mm, respectively). In contrast, annual evaporation has reduced by 453 mm (2153 mm and 1700mm, respectively) (Kathiresan, 2011). The year 1990 was the hottest year in the last century with all other five of the warmest years in the century falling within the last 22 years. This trend indicates that f lash floods in wetlands and droughts in uplands have become more frequent. Such a changing climate warrants reinforcement of climate resilience in farming to protect the livelihoods of farmers and the biodiversity. The sub-project of the National Agricultural Innovation Project implemented by Annamalai University in Tamilnadu, sponsored by ICAR has identified three types of clusters in each of the four disadvantaged districts, based on the prevailing agro-ecology viz. wetland clusters, rainfed upland clusters and coastal shore farming clusters. The constraints for farming and livelihoods in these clusters are identified as frequent floods, inundation, drought, weed problems, crop failure, and lack of diversification (Kathiresan, 2009a and Kathiresan, 2010). Farming systems that would be resilient to such climatic and farming constraints designed through rigorous institutional and on-farm experiments (Kathiresan, 2007; Kathiresan, 2009b and Kathiresan, 2012) were disseminated for adoption by the farmers in these clusters.

Importance of Food Storage

Food storage is both a traditional domestic skill and is important industrially. Food is stored by almost every human society and by many animals. Storing of food has several main purposes:

•Storage of harvested and processed plant and animal food products for distribution to consumers

•Enabling a better balanced diet throughout the year

•Reducing kitchen waste by preserving unused or uneaten food for later use

•Preserving pantry food, such as spices or dry ingredients like rice and flour, for eventual use in cooking

•Preparedness for catastrophes, emergencies and periods of food scarcity or famine

•Religious reasons (Example: LDS Church leaders instruct church members to store food)

•Protection from animals or theft

Deep learning has significantly advanced automated plant phenotyping and disease detection by enabling computers to identify complex patterns in large volumes of plant imagery and sensor data. This chapter examines the fundamentals and applications of deep learning architectures in plant science. It discusses convolutional neural networks for image-based plant analysis and advanced architectures including recurrent neural networks, transformers and hybrid models. Data acquisition through imaging and sensor technologies is also considered. The chapter highlights how deep learning can support automated identification of plant characteristics, disease symptoms and crop health conditions. Challenges related to datasets, environmental variability, computational requirements and model generalization are examined alongside emerging research opportunities.

Plants are frequently subjected to environmental stresses such as water deficit, freezing, heat and salt stress. Soil salinity is a major problem in arid and semi- arid regions, where rainfall is insufficient to leach salt and excess of sodium ion down and out of root zone. In Asia, around 21.5 million ha of cultivable land was affected by salinity. India had 8.6 million ha of salinity affected cultivable land. It is observed that the area of salinity has been increasing in India. In Tamil Nadu, the area affected by salinity is around 6 lakh ha. Salinity affected areas are Chengalpattu, Salem, Thanjavur, Trichy, Tirunelveli, Dharmapuri and Ramanathapuram. Salinity is the most serious threats to the agriculture especially in the arid and semi-arid region. The presence of salts in the soil and their effects on the plant physiological mechanisms was major restrictive factor for agricultural productivity. Investigations on such soils have revealed that they were charged with high and abnormal soluble salt concentrations that lowered agricultural yield, limited crop distribution and even lead to extinction of certain species.

Salinity adversely affects plant growth and development, hindering seed germination, seedling growth, enzyme activity, DNA, RNA, protein synthesis and mitosis. There has been a variation in the response of plants to salinity with its growth stages according to quantity and period of exposure to salt. Salinity by competing in nutrition element absorption causes growth reduction. Primary salt injuries include metabolic disturbance and inhibition of growth and development. Secondary salt effects include nutrient deficiency and osmotic dehydration. Excessive amounts of salts, especially sodium chloride (NaCl), in the soil induce osmotic effects, leading to changes in plant metabolism. General symptoms of damage by salt stress are growth inhibition, accelerated development and senescence and death during prolonged exposure. Salt stress causes the reduction of rice yield and severe salt stress may even threaten survival.

Introduction

The state of Maharashtra now again reels under the scarcity of water. Geologically, major part of the state is covered by Deccan basaltic flows. This basaltic terrain, as such, has been labelled notorious as far as the availability of groundwater is concerned. Uncertain availability of groundwater in the terrain is due to heterogeneity in the nature of piles of basaltic flows which adversely affects the geohydrological characters of the rocks.

The area understudy is unfortunately, a part of basaltic terrain in which no storage structures like major dams or medium projects have been constructed across the existing rivers. Consequently, the irrigation in the area and domestic needs of the people solely depend on whatever water is available in the existing dug wells. The wells do not suffice the need of the people because the percolation of water in the wells is not adequate in major part.

1. INTRODUCTION

With the rapid development of Information Communication Technology (ICT), many new services have been introduced in libraries to satisfy the need of users in a better way. Institutional Repository is a type of information service which involves collection, organization, preservation and dissemination of digital scholarly materials such as theses, dissertations, technical reports, journal articles and teaching materials. It is created and maintained by an individual library/ organization or a group of libraries/organizations with an aim to preserve the institutional knowledge .Thus basically it is an online locus for collecting, preserving and disseminating the intellectual output of an academic or research institution in digital form, created and maintained by the institution itself.

“Clifford Lynch defines “ a university based Institutional Repositories as a set of services that a university offers to the members of its community for the management and dissemination of digital materials created by the institution and its community members. It is most essentially an organizational commitment to the stewardship of these digital materials, including long-term preservation where appropriate, as well as organization and access or distribution” ¹

Similarly Mark defines IR as “An institutional repository (IR) is defined to be a web-based database (repository) of scholarly material which is institutionally defined (as opposed to a subject-based repository); cumulative and perpetual (a collection of record); open and interoperable (e.g. using OAI-compliant software); and thus collects, stores and disseminates (is part of the process of scholarly communication). In addition, most would include long-term preservation of digital materials as a key function of IRs”²

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.

Introduction

Agriculture has played an important role in the global economy in recent years. The further growth of the population is leading to a gradual decrease in the area of the agricultural land and further growing stresses on the farm system. Agricultural food production methods are rising in demand for successful and healthy. Innovative sensoring and motive technologies and advanced information and communications systems and Artificial Intelligence must supplement conventional agricultural management methods to speed up farm productivity growth more accurately, thereby promoting the production of high-quality and high-performance agriculture. Computer vision monitoring systems have become valuable tools in farm operations over the past decades, and their use has increased significantly.

What is Computer Vision?

Computer vision can be characterized as a field of AI for extracting information from digital images. The type of data obtained from an image may be different from recognition, navigational space measurements or applications of increased reality.

The applications of computer vision can also be described. Computer vision builds algorithms that recognize and use the quality of pictures for other purposes.

Introduction

Okra, Abelmoschus esculentus (L.) Moench, is annual herb grown in tropical and subtropical parts of world for green tender immature fruits and consumed as cooked mostly fresh but sometimes sun dried or frozen also used as vegetable. Some parts of plant and roots are used for medicinal as well as purification of sugarcane juice. Green fruit contains vitamins, calcium whereas seeds possess 13-22 per cent edible oil and 20-24 per cent edible protein. India is leading producer of okra and exporting fresh as well as processed fruits to other countries and also ranked first in area (0.48 million ha.), production (3.5m tones) and productivity (9.8 t/ha) during 2005 against the world area (0.78 million ha.), production (4.99 m tones) and productivity (6.4t/ha).

Introduction

One of the major problems being faced by cities and towns relate to management of municipal solid waste (MSW). Waste quantities are increasing day by day due to the increasing population and the municipal authorities are not able to upgrade or scale up the facilities required for proper management of such wastes. In many cities and towns, garbage is littered on roads and foot-paths. Citizens are also not accustomed to use the available storage facilities (dust bins) set up by the authorities. At large, lack of organized system of house- to-house collection of waste has created the littering habits. By and large, hardly we can see any city/town complying with the Municipal Solid Wastes (Management and Handling) Rules, 2000 in ‘totality”. As per information provided by Central Pollution Control Board (CPCB), 1,27,486 TPD (Tons per day) municipal solid waste was generated in the country during 2011-12. Out of which, 89,334 TPD (70%) of MSW is collected and 15,881 TPD (12.45%) is processed or treated.

In spite of various measures taken towards the proper waste collection, segregation and disposal, proper waste processing units were established as per the following details as per CPCB. The waste processing technologies reported in the country are; composting, vermi composting, biogas plant, RDF- pelletisation and others. Some of the sepelletization plants are associated with power plants for generation of electricity. However, mechanical composting and vermi composting are more popular in the country.

In this chapter we will cover all the basics libraries and some frameworks of machine learning, deep learning and computer vision. We will discuss all about their use with an example and instruction which will states the commands to install to those libraries for building any project in our system. So let’s begin

What is a Library?

Library is an aggregate of features and methods that allow you to take many actions without getting your own code written.

For instance, the libraries that you have to know are if you work with data, numpy, scipy, pandas, etc. They have very simple data processing functions that save you time for small tricks.

Nutrient management is one of the key aspects that ensure good quality produce in optimum quantity. Nutrient availability influences growth and productivity of crops. Sixteen elements are recognized as essential nutrients for growth and development of plants. These essential elements are carbon, hydrogen, oxygen, nitrogen, phosphorus, potassium, calcium, magnesium, sulphur, iron, zinc, manganese, copper, boron, molybdenum and chlorine. While carbon, hydrogen and oxygen are derived from air, soil and water, other elements are taken by the plant roots from the soil, according to the requirement of the plant. The soil contains these elements in varying quantities depending on the origin of the soil. According to the quantity of requirement of these elements by plants for its various growth processes,the elements are classified into different categories.

Major nutrients or primary nutrients - These are required by plants in large quantities because they are involved in all the processes of plant growth and development. Major nutrients are nitrogen, phosphorus and potassium.

Secondary nutrients - These are required by plants in relatively smaller quantities for growth and development. Secondary nutrients are magnesium and sulphur.

Micro nutrients - As the name suggests, these are required by plants in minor quantities for their physiological processes. These elements are iron, zinc, manganese, copper, boron, molybdenum and chlorine.

Each of the plant nutrients has a definite quantitive role and specific function in the physiological processes of plants. If any of the essential element is deficient or absent in the soil sysem, the plant growth is affected negatively and this is reflected as inferior quality produce. On the contrary, if a nutrient is excess in the soil system, it hinders in absorption of other nutrients and also may itself be toxic. Hence to get the required growth, quality and quantity of the produce, optimum availability of all the nutrients should be ensured.

Introduction

Earth’s magnetic field (MF) is a natural component of environment and is an inescapable factor for all living organisms including plants. Earth has an inner core made of molten iron which rotates at a fairly high speed creating a magnetic field just like a dynamo. This Geo Magnetic Field (GMF) extends from the earth’s interior into outer space. It interacts with the solar wind, which is actually a stream of charged particles emanating from the sun. As earth is engulfed in a GMF, all living organisms, both plants, animals and all biological processes are under its influence. However, local differences in strength and direction of the geomagnetic field have been detected. The vertical component of GMF is maximum at the magnetic poles, which is around 67 μT, and the horizontal component is zero there. Conversely, at the magnetic equator the vertical component is zero and the horizontal component is maximum, about 33 μT (Kobayashi et al., 2004).

Turbulent flows of the fluid metallic inner core of earth mainly contribute to GMF and the effect of external MFs located in the ionosphere and the magnetosphere is very meagre (Qamili et al., 2013). As GMF extends beyond the earth’s surface it is this force along with magnetosphere that protects the earth and its biosphere, from the solar wind by deflecting most of its charged particles (Occhipinti et al., 2014).

Metagenomics is a relatively new field that combines molecular biology and genetics in an attempt to identify, and characterize the genetic material from environmental samples and apply that knowledge. The genetic diversity is assessed by isolation of DNA followed by direct cloning of functional genes from the environmental sample. But more importantly, it is extremely beneficial in the study of synthetically made genomes. Metagenomics is described as “the comprehensive study of nucleotide sequence, structure, regulation, and function”. Scientists can study the smallest component of an environmental system by extracting DNA from organisms in the system and inserting it into a synthetic model organism. The model organism then expresses this DNA where it can be studied using standard laboratory techniques. The development of metagenomics stemmed from the ineluctable evidence that as-yet-uncultured microorganisms represent thevast majority of organisms in most environments on earth. This evidence was derived from analyses of 16S rRNA gene sequences amplified directly from the environment, an approach that avoided the bias imposed by culturing and led to the discovery of vast new lineages of microbial life.

Metagenomics is employed as a means of systematically investigating, classifying, and manipulating the entire genetic material isolated from environmental samples. This is a multi-step process that relies on the efficiency of four general steps as shown in the diagram. The procedure consists of: the isolation of genetic material, the manipulation of the genetic material, library construction, and the analysis of genetic material in the metagenomic library. Metagenomics provides a second tier of technical innovation that facilitates study of the physiology and ecology of environmental microorganisms. Novel genes and gene products discovered through metagenomics include the first bacteriorhodopsin of bacterial origin; novel small molecules with antimicrobial activity; and new members of families of known proteins, such as an Na⁺(Li⁺)/H⁺ ant porter, RecA, DNA polymerase, and antibiotic resistance determinants. Reassemblyof multiple genomes has provided insight into energy and nutrient cycling within the community, genome structure, gene function, population genetics and micro heterogeneity, and lateral gene transfer among members of an uncultured community. The application of metagenomic sequence information will facilitate the design of better culturing strategies to link genomic analysis with pure culture studies.

Bees are fundamental to maintaining ecological stability and enhancing agricultural output, making it vital to understand their diversity—especially in the context of natural farming systems. Different bee species vary in behavior, environmental adaptability, honey yield, and efficiency in pollination. India is home to several native and introduced bee species, each contributing uniquely to honey production and ecosystem health. Choosing a species for natural beekeeping depends on factors like climate, local flora, and whether the beekeeper’s goal is honey production, crop pollination, or species conservation.

Among these species, Apis dorsata—the rock bee—is a large, wild bee known for building single, exposed combs on high structures such as trees and cliffs. While it can produce up to 40 kg of honey per colony annually, its aggressive nature makes it unsuitable for domestication. In contrast, Apis cerana indica, the native Indian hive bee, is better adapted to India’s diverse climates and is compatible with low-maintenance, eco-conscious farming. Producing around 6–10 kg of honey per colony annually, it suits small-scale, sustainable practices.

INTRODUCTION

Library Consortia is the sharing of resources among the libraries based on a number and utility over their respected areas. The resources that are shared in today’s consortia atmosphere are largely in electronic form such as electronic journals and databases. The aim of consortia is to accomplish what the members of the group cannot achieve individually, and its intention is sharing of resources, manpower and money, etc. It is regarded as a valuable strategy to increase the buying power and risk-sharing capacity of academic libraries over a short term. It is also a chance to maximize the opportunities for joint collection development and resource sharing over the long term. A consortium has the ability to share resources without forfeiting the individuality of member library. As a result, the end-users can collect the benefits of more resources than would be accessible through one library, while staff can adapt the system to meet their individual library’s needs. It can be a single agency with multiple locations around the world, all sharing one name, or the consortia members can retain their own name, but use the name of the consortia to recognize that they are the part of a larger, often worldwide, organization. It acts as director for the electronic resource sharing at the national, regional and local levels. The accessibility of IT-based electronic information products put forth portions of their budgetary allocation for either acquiring or accessing web-based online full-text search services, online databases shared services, on the one hand, permits flourishing consumption and desktop access to electronic resources at highly increasing users demands and growing cost of journals.

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

Introduction

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

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