eChapters

Sending a message from one place to another place, which are physically separated from each other, is called Communication. The Communication Engineering or Communication Technology deals with the design and development of engineering systems to transmit information from its source, and at the same time, to receive the same information at a remote destination with reliability, accuracy and quality.

Journal Citation Reports (JCR) and SCImago Journal Rank (SJR) are widely used metrics for evaluating journal impact. While JCR is based on Web of Science and emphasizes high-impact journals, SJR uses Scopus data, offering broader geographic and disciplinary coverage. This chapter evaluates their relative strengths and weaknesses, focusing on their applicability across disciplines and geographies. Multiple trends also impact research landscapes across the globe (Fig. 3.1).
Comprehensive tables provide a comparative overview of which metric is more suitable for various disciplines and global regions. The assessment of academic journals plays a critical role in research evaluation, academic promotions, and funding decisions. Two prominent journal-ranking systems, JCR and SJR, serve as benchmarks for these purposes. JCR, managed by Clarivate Analytics, primarily focuses on citation frequency over a two-year window. Conversely, SJR, developed by SCImago using Scopus data, incorporates a prestige-weighted citation approach over a three-year period (Fig. 3.2). Several of our Indian Scientific Journals are not covered by JCR. As more than 80 per cent Journals are from Europe and North America. The CLARIVATE covers only few Indian Journals most of which are published by Central Government Agencies

Abstract

Rice blast disease (Pyricularia grisea cavara) which is very much weather sensitive, continues to be an enigmatic problem of West Siang District of Arunachal Pradesh, owing to its geographical location and climate. There is an urgent need of a forewarning system which can provide advance information for outbreak of the diseases attack so that protection measures can be implemented before the actual economic damage of the crop occurs. Two most important statistical techniques for disease forecasting viz. regression analysis and artificial neural network were chosen and forecasting models has been developed for the area and analyzed with local weather data to find best fit for the area and also compared with the Support Vector Machine (SVM) based model RB-Pred developed by Institute of Microbial Technology, Chandigarh. The model based on artificial neural network was found to be best with accuracy of 75-80%, with the present data and algorithm. The accuracy can be improved with long archive records and proper implementation and algorithm selection of the hidden layer, of both ANN and SVM based (being special case of ANN) models, for site-specific forecasting.

The lives of farmers are intricately woven with the rhythms of nature, with weather playing a pivotal role in determining the success or failure of their crops. In an era where climate variability and extreme weather events are becoming increasingly common, the importance of accurate and reliable weather forecasts cannot be overstated. Farmers rely heavily on weather forecasts to make critical decisions that impact their agricultural practices, from planting and harvesting to irrigation and pest management. However, the process of using weather forecasts is not as straightforward as it may seem.
6.1. The Multifaceted Nature of Decision-Making
Farmers operate within a complex web of factors that influence their decisionmaking processes. Some of these factors include:
• Economic Considerations: Market fluctuations, input costs, and profit margins all play a role in shaping a farmer’s decisions. Weather forecasts must be considered in the context of these economic factors to ensure that decisions are financially viable.
• Social and Cultural Factors: Local traditions, community norms, and personal values can influence a farmer’s choices. For example, some farmers may prioritize environmental sustainability over profit maximization.
• Environmental Concerns: Soil health, water conservation, and biodiversity are all critical considerations for farmers. Weather forecasts must be integrated with these environmental concerns to ensure sustainable agricultural practices.
• Technological Advancements: The increasing use of precision agriculture, drones, and other technologies is changing the way farmers’ access and utilize weather information.
6.2. Challenges in Interpreting and Applying Weather Forecasts
The intricacies of interpreting and applying weather forecast data pose significant challenges for farmers. Some of these challenges include:
• Forecast Reliability: The accuracy and reliability of weather forecasts can vary significantly, making it difficult for farmers to trust and act upon the information.

Heat units also called growing degree-days (GDD), effective heat units, or growing degree units, are a heuristic tool in phenology. GDD is a simple means of relating plant growth, development and maturity to air temperature. The concept is widely accepted as a basis for building phenology and population dynamic models. Degree day units are often used in Agrometeorology, Agronomy, Entomology and Pathology essentially to estimate or predict the lengths of the different phases of development in crop plants, insects, pathogens etc.

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.

The Asia-Pacific region, particularly the rural areas within CMCs, faces severe climate change impacts, including frequent natural disasters, rising temperatures, and sea-level rise, which threaten agriculture, livelihoods, and food security. Climate- resilient rural development (CRRD) is crucial for mitigating these vulnerabilities, addressing challenges such as agricultural dependency, limited infrastructure, governance gaps, financial constraints, and socio-cultural barriers. Key approaches like climate-smart agriculture, strengthened infrastructure, innovative start-up ecosystems, enhanced access to climate information, and inclusive policies empower rural communities to adapt and thrive. Stakeholders—governments, international organizations, the private sector, and local communities—play complementary roles in driving resilience through policy, funding, technology, and grassroots innovation. Sensitising all stakeholders across countries to mainstream climate change aspects and its impact on livelihoods, among all departments and the information dissemination by these departments to the public and relevant stakeholders regarding the climate change effects needs to be a continuous process. Success stories from Bangladesh

There is widespread belief now that forests can be used to reduce the costs of slowing climate change. While the role of forests in the global carbon cycle has long been acknowledged, recent discussions within the context of the United Nations Framework Convention on Climate Change, as well as efforts to write climate change legislation in the United States, have emphasized the role forests might play. The most recent policy efforts have focused on near-term actions to reduce deforestation in tropical countries. The rationale for considering forests at all in the policy mix stems partly from the physical components of the issue. The world’s forest estate is exceedingly large:

Introduction
The richness of a country can be measured by its biological diversity. It is essential to both human existence and financial security. The most basic concept of biodiversity is expressed by the diversity of creatures. Biodiversity is represented by the supplies of food, clothing, shelter, and medicines provided by plants; by the sources of food, assistance, wild life, and human resource efficiency provided by animals; and by the patterns of variation seen in the microbiological world. More than 20,000 kinds of ants, 3,000,000 species of beetles, 28,000 species of fish, and almost 20,000 species of orchids would be hard for the average person to believe.
Definitions
• The collection and interplay of genes, species, and ecological diversity in a specific location and time is known as biodiversity. Different genes, species, habitats, and their relative abundance are all included in the phrase biodiversity.
• The portion of nature known as biodiversity comprises the genetic variations between individuals of the same species as well as the variety and richness of all plant and animal species at various spatial dimensions, such as a region, a nation, and the entire planet.
• “The diversity of life, which encompasses ecosystem or community diversity, species diversity, and genetic diversity, and includes the entire range of variety and variability within and among living organisms and the ecological complexes in which they occur,” is another definition for it.

Solid waste disposal has become Nation’s great concern since after spending more than 50 percent of their budget by Municipalities, they could collect and dispose about 70% of MSW leaving many parts of the municipality unclean every day and residents are not at all satisfied (Sannigrahi, 2016). The Central Pollution Control Board (CPCB) collected data from 299 cities of India on the mode of collection of MSW and found that manual collection comprised 50% while collection using trucks comprised only 49% (CPCB Report, 2000). It is very interesting to note that majority (more than 90%) of MSW are disposed by dumping in unscientific open land filling on outskirts of towns and cities while 10% of collected and 2% of uncollected MSW are burnt in open condition.

ABSTRACT

The improvement of spatial resolution of satellite remote sensing (RS) imagery, it is possible to update spatial data infrastructure efficiently by satellite RS images. But the RS data volume is vast, so high processing capacity is required. In order to extract the anticipated images from vast image database, effective retrieval technique is vital. The rapid development of Earth Observation System (EOS), the spatial resolution, temporal resolution and spectral resolution of Remote Sensing (RS) image have been improved greatly. Based on contentbased image retrieval, content-based RS image retrieval is put forward and some topics including retrieval pattern, useful image content, feature extraction and similarity measure are researched. After related images are retrieved from vast image database, it is necessary to discover those change areas, so change detection from multitemporal RS images are discussed further. RS image data mining and knowledge discovery is the synergy of Spatial Data Mining (SDM) and Image Data Mining (IDM). It is pointed out that content based RS image retrieval will be potential advantageous in the future and all the techniques discussed in this paper including retrieval modes, content-useful in RS image retrieval. Content-based image retrieval (CBIR) is one of the hot issues in image retrieval, and it can be used to RS image retrieval. That lead to a new area: content-based RS imagery retrieval.

Keywords : Remote Sensing, content based RS image Retrieval, Earth Observation System, Spatial Data Mining, Image Data Mining.

Continental drift is the theory that continents move slowly about the earth’s surface, changing their positions relative to one another and to the poles of the earth. It includes the idea that former continents may have broken apart and that the fragments have recombined in different patterns (Fig. 15.1). Some authorities have considered that this happened only once, beginning within the past 200 million years; others believe that it has long been a continuous process, so that all existing continents are mosaics. Those who accept the theory believe that the rates of motion have been only a few inches a year. Earlier, it was considered that the continents moved like ships through a slightly plastic ocean floor. Today some consider that the continents are frozen into the earth’s surface like rafts in ice, and that the motions are the result of growth of the sea floor along certain midocean ridges and reabsorption of the sea floor elsewhere beneath deep ocean trenches and young mountains. The continents are carried passively about embedded in moving plates of the surface. Some authorities  now  agree  that  there  is  a  deformable  layer,  called  the asthenosphere, at depths of from about 50 to 400 kilometers within the earth. It is white hot, and slightly viscous, and many believe that the surface can break into plates which slide over it.

7.1 Introduction

The occurrence of extreme weather events like drought and flood is increasing in Bihar due to climate change. Bihar receives nearly 80% of annual rainfall during June to September which is quantitatively enough for growing most of the crops. However, aberration in temporal and spatial distribution of rainfall makes the crops vulnerable to drought as well as flood. Rainfall is the most important factor for planning of crop production in rainfed areas. The information on annual, seasonal and weekly rainfall of a region is helpful to design different agricultural operations like field preparation, sowing, irrigation, fertilizer application as well as overall crop planning (Singh et al., 2008). Zone I and zone II of Bihar are receiving too much rainfall which causes flood in this region whereas zone III B is identified as rainfed region which has low, erratic and uncertain rainfall pattern with frequent dry spells during the monsoon season. Hence kharif cropping is tricky operation in this region and sudden crop failures is a common phenomenon due to frequent dry spell or early withdrawal of monsoon. In India many scientists studied on rainfall probability pattern (Suchit et al., 2012) and they concluded that rainfall occurrence is certain at greater than or equal to 80 % probability, while 50% probability is the medium limit of certainty and may involve dry spell risk. Based on these climatic and probability factors, the study was carried out for four different locations situated in different agro climatic zones of Bihar for interlinking the rainfall probability pattern with the crop planning pattern in those regions.

This chapter introduces continuous cover forestry as an approach to managing forest ecosystems while maintaining relatively continuous canopy cover and ecological processes. It examines the concept and evolution of continuous cover forestry, its ecological foundations, and advanced silvicultural practices. The chapter considers its ecological, economic, and social benefits and discusses its relevance to climate change adaptation and sustainable forest management. Comparative perspectives with conventional forestry, global case studies, challenges, limitations, and future prospects are also addressed. The approach emphasizes maintaining forest structure, regeneration, biodiversity, and ecosystem functions while allowing appropriate harvesting and resource utilization.

Crop disease forecasting is a crucial component of integrated crop management, enabling farmers and agricultural practitioners to anticipate and prepare for potential disease outbreaks. The importance of crop disease forecasting cannot be overstated, as it allows for the timely implementation of disease management strategies, reducing the risk of yield losses and minimizing the use of chemical pesticides. Conventional methods of crop disease forecasting have been widely used for decades, relying on observations of weather patterns, crop phenology, and disease symptoms to predict the likelihood of disease outbreaks. These methods have been refined over time, incorporating new technologies and data sources to improve their accuracy and reliability (Fig. 10.1). Despite the advent of modern technologies, such as machine learning and remote sensing, conventional methods of crop disease forecasting remain a vital tool for many farmers and agricultural practitioners. These methods are often simple, cost-effective, and accessible, making them particularly suitable for smallscale farmers and resource-constrained agricultural systems. This chapter will provide an overview of conventional methods of crop disease forecasting, highlighting their principles, advantages, and limitations. The chapter will also discuss the importance of crop disease forecasting in modern agriculture, emphasizing its role in promoting sustainable crop production and reducing the environmental impact of agricultural practices.
10.1 Conventional methods of Forecasting
There are several conventional methods of crop disease forecasting that have been widely used for decades. Some of these methods include:
10.1.1 Weather-based forecasting
This method uses weather data, such as temperature, rainfall, and humidity, to predict the likelihood of disease outbreaks. Weather-based forecasting models use statistical relationships between weather variables and disease incidence to make predictions (Fig. 10.2).

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.

Correlation and regression are essential statistical tools for examining relationships among climate variables and agricultural responses. Correlation measures the strength and direction of association, while regression develops mathematical relationships for explanation and prediction. This chapter discusses their application to rainfall, temperature, humidity, evapotranspiration, crop growth and yield. It explains simple and multiple relationships, assumptions, interpretation, model fitting, predictive power and potential errors. These methods support crop-weather modelling, yield forecasting, climate-impact assessment and agricultural decision-making. By identifying how climatic variables influence biological and production processes, correlation and regression analysis help researchers generate reliable predictions and design more resilient and productive agricultural management strategies under changing environmental conditions.

2.1 Introduction
As time passes, the customers are used to the practice of having short life for the products, which they buy and use. This is because of the globalization of business and growing global competition all over the world. The companies engaged in manufacturing and selling the products which are used at large volume by households and partly by industries, introduce new models to attract customers and to have more market share. This in turn has impacts on customer buying and retention decisions of those products. Though the companies introduce new models, they are expected to keep the prices of the products at affordable ranges so that there is always a larger customer segment to buy those products. This is like egg or chicken situation, because companies introduce many new models to out beat competition and in turn customers expect improved versions of those models in shorter time intervals. This forces companies to continuously engage themselves to come out with new models or improved models which have convergence of technologies to give maximal customer satisfaction. So, creativity in product design becomes an essential requirement of the product development teams of the companies.
2.2 Product Design
Product design aims to include the following tasks to solve the problems of the users or address expected needs of customers in a given market. These tasks are human intensive.
i. Understanding end-users’ requirements
ii. Imagining alternate product ideas to address their needs