eChapters

Early and accurate crop-disease diagnosis is essential for preventing production losses and reducing unnecessary pesticide use. Manual diagnosis can be slow, subjective and difficult to apply across large agricultural areas. This chapter introduces computational approaches using artificial neural networks, fuzzy logic, support vector machines, deep learning and hybrid optimization models. It discusses image acquisition, preprocessing, feature extraction, pattern recognition and classification of disease symptoms from leaves, crop canopies and sensor data. Integration with precision-agriculture systems, mobile advisory applications and real-time monitoring enables timely intervention. Challenges involving data quality, field variability, model interpretation and large-scale deployment are examined through practical examples and case studies.

Decision Support Systems convert farm data into recommendations that improve livestock productivity, health and sustainability. This chapter explains data-driven, model-driven and knowledge-based systems and their principal components: data management, analytical models and user interfaces. Technologies such as IoT, artificial intelligence, machine learning and cloud computing support applications in feed optimization, disease prediction, reproductive management, milk production and animal-welfare monitoring. Dairy and poultry examples demonstrate how DSS can reduce operational costs and strengthen farm decisions. Implementation challenges include inadequate data quality, high costs, limited technical expertise and maintenance requirements. Future systems are expected to integrate blockchain, robotics, Edge AI and increasingly autonomous management technologies.

5.1 Introduction

The application of chemical pesticides is crucial in horticultural production, but it is also one of the most hazardous agricultural operations. Overapplication can lead to significant environmental concerns. Spraying is vital for reducing harvest losses and improving productivity. According to Cho et al.,(2012), 30-35% of production damages can be prevented by reducing harmful insects and diseases through spraying. Plant protection is essential for ensuring yield quantity and quality. Effective pesticide use is crucial, as excessive application can lead to pests developing immunity, destruction of beneficial insects and natural pest enemies, and increased harmful insect populations. Excessive pesticide use also disrupts pollination, preventing fruit formation. The method of pesticide application is key to targeting the correct quantity and quality, reducing environmental contamination, and increasing sustainability (Abbas et al., 2020). Site-specific application rates in precision horticulture can decrease pesticide use. Only 30-40% of pesticides typically reach their targets, with most lost to the environment, contaminating workers and causing pollution. Protecting plants with chemicals aims to prevent infections and ensure healthy growth.

Spray drift can harm non-target plants and contaminate watercourses, posing a severe and costly problem for horticultural farmers using air-assisted variable sprayers (Seol et al., 2022). Spray drift refers to airborne spray droplets moving beyond the intended application area from aerial or ground-based methods. Studies have found that a significant portion of pesticides is lost to the air and ground, with up to 30-50% of the applied pesticides being lost through various means (Abbas et al., 2020). Modern trends emphasize the importance of sensors and precision agriculture for effective pesticide use. Understanding tree canopy physical features is crucial for effective spray application. This study focuses on recognizing and geometrically classifying canopies for adequate pesticide protection. Variable rate spraying targets only the canopy, enhancing pesticide application efficiency (Rathnayake et al., 2022). Electronic methods and innovative equipment reduce operational and environmental costs. Sensors tracking canopy characteristics and spacing ensure optimal use of inputs, promoting sustainability.

Computational crop-disease detection uses machine learning, neural networks and image-processing techniques to provide timely and accurate diagnoses. This chapter presents a complete framework covering agricultural intelligence, disease categories, image acquisition, preprocessing, model training and classification. It examines datasets collected in fields or through drones and considers deployment through mobile devices, cloud platforms and other digital systems. Evaluation measures are introduced for assessing model accuracy and reliability. The chapter also considers limitations caused by insufficient or poor-quality data and variation in field conditions. Future directions include multimodal data integration and explainable models that provide transparent recommendations for sustainable crop protection and improved food security.

8.1 Introduction
Every fusion welding procedure that uses a heat source capable of producing incredibly high weld input levels is known as high energy density welding. For welding, the density of the energy obtainable from a heat source is frequently more significant than the source’s absolute energy. Electron beam welding (EBW) and laser beam welding (LBW) are the two main categories of high energy density welding technologies. In both procedures, a very high-intensity beam is used as the heating source for the weld, and this beam’s energy is highly concentrated due to optical or electromagnetic lenses, respectively. These processes have an energy density of roughly 1010–1013 W/m2. The energy density of typical arc welding procedures is roughly 5 x108 W/m2. As fast-moving electrons in EBW and photons in LBW collide with the workpiece, their kinetic energy is converted into heat. This causes melting, vaporisation, and heating in a very small region. Due to non-uniform weld metal shrinkage or thermal contraction, penetration into the workpiece can be significant. This results in deep, thin, parallel-sided fusion welds with narrow heat-affected zones and little angular distortion. Since the electron beam welding is nearly always done autogenously, the joint fit needs to be flawless.
This welding is classified into the following types.
i. Plasma arc welding
ii. Electron Beam welding (EBW)
iii. Laser bean welding (LBW)
8.2 Plasma Arc Welding
An electric plasma arc and a non-consumable electrode are used in the fusion welding method known as plasma arc welding (PAW) to fuse metals together. The electrode is typically constructed of thoriated tungsten, just like in TIG. It is an excellent option for welding thin metals and producing deep, narrow welds because of its distinctive torch design, which provides a more focused beam than TIG welding. Compared to conventional procedures, plasma welding is frequently used to weld tough metals like aluminium, stainless steel, and others. This method, like oxy-fuel welding, can also cut metal (plasma cutting), which gives fabricators and manufacturers another useful tool.

Introduction

Soil, water and vegetation are three basic natural resources. The survival of God’s creation depends upon them and nature has provided them as assets to human beings. The management of natural resources to meet people’s requirements has been practised since the pre-Vedic era. Farmers were ranked high in the social system and village management was in their hands. Over-exploitation of natural resources by growing population resulted in various severe problems. Destruction of vegetation has resulted in land degradation, denudation, soil erosion, landslides, floods, drought and unbalanced ecosystems. A balanced ecosystem is an urgent need.

There is an increasing realization throughout the world, about the state of our environment, caused by over-exploitation of natural resources for economic development. Resource exploitation in the developing world has largely been geared to meet the very basic needs of food, fodder, fuelwood and shelter of a large section of deprived societies. On the other hand, developed parts of the world have largely concerned themselves in trying to maintain and accelerate the already very high levels of resource consumption that they have achieved for a much smaller segment of the world’s population, based on an early initiative taken and advantage gained through industrialization. Natural resource base has often viewed as limitless by the industrialized world either due to a myopic view of the future or because of their immense faith in technology being able to substitute for the natural resource capital. This faith in technological quick-fixes for world’s problems linked with over-consumption is still prevalent.

The chapter will delve into the fundamental physics governing energy exchange at the Earth’s surface and how water is managed within the soil profile. These concepts are crucial for understanding microclimate, evapotranspiration, and overall water availability for crops. This chapter explores the intricate interactions between incoming solar radiation, its transformation into various energy forms at the Earth’s surface, and the dynamic movement and storage of water within the soil. Understanding these processes is vital for agricultural meteorology, as they directly influence temperature, humidity, and water availability for plant growth.The Earth’s surface, particularly a vegetated land surface, continuously exchanges energy with the atmosphere. The Surface Energy Budget quantifies these energy fluxes, explaining how incoming solar radiation is partitioned. It operates on the principle of conservation of energy: the energy received must equal the energy expended or stored (Fig. 6.1). The Earth’s surface energy budget numbers derived from Kiehl-Trenberth 1997. The surface of the Earth is cooled 40 W/m2 by radiation; this part of surface radiation is radiated straight into Space. 324 W/m2 of all outgoing surface radiation returns nearly simultaneously as back radiation. The remaining 26 W/m2 of surface radiation is absorbed by greenhouse gases and results in the warming of the atmosphere near the

Since the mid 1980s, genome identification and selection has progressed rapidly with the help of PCR technology. A large number of marker protocols that are rapid and require only small quantities of DNA have been developed. Three widely-used PCR-based markers are RAPDs (Williams et al., 1990), SSRs or microsatellites (Tautz, 1989), and AFLPs (Vos et al., 1995). Each marker technique has its own advantages and disadvantages. RAPD markers are very quick and easy to develop (because of the arbitrary sequence of the primers) but lack reproducibility (Karp et al., 1997; Hansen et al., 1998; Jones et al., 1999; Virk et al., 2000). AFLP has medium reproducibility but is labour intensive and has high operational and development costs (Karp et al., 1997). Microsatellites are specific and highly polymorphous (Karp et al., 1997, Jones et al., 1999), but they require knowledge of the genomic sequence to design specific primers and, thus, are limited primarily to economically important species.

The choice of a molecular marker technique depends on its reproducibility and simplicity. The best markers for genome mapping, marker assisted selection, phylogenic studies, and crop conservation have low cost and labour requirements and high reliability. Since 1994, a new molecular marker technique called inter simple sequence repeat (ISSR) has been available (Zietkiewicz et al., 1994). ISSRs are semiarbitrary markers amplified by PCR in the presence of one primer complementary to a target microsatellite. Amplification in the presence of nonanchored primers also has been called microsatellite-primed PCR, or MP-PCR, (Meyer et al., 1993).

Introduction

In the last two decades global climate change has perceptible and marked impact on the monsoon pattern in India. Delayed and inadequate monsoon has become a recurrent feature and the country is in the grip of drought-flood cycles. Right from the beginning of large scale irrigation that brought green revolution and food self sufficiency to the country, there is a strong feeling that full potential of rivers of the country is not tapped (Table 1).

ABSTRACT

A study was conducted to compare inter-sensor variability by computing vegetation indices, NDVI & SR. The two sensors compared were Hyperion and ALI (Advanced Land Imager) present on EO-1 satellite. Hyperion is a hyperspectral sensor having a spectral range of 400-2500nm with 10nm bandwidth. ALI is a multispectral sensor with 10 bands of 100nm bandwidth. Hyperion and ALI data were acquired at the same time for two different seasons, dry (April) and wet (October) seasons over Shoolpaneshwar Wildlife Sanctuary, Gujarat, India. Vegetation indices such as SR “ Simple Ratio and NDVI -Normalized Differential Vegetation Index were performed. Seasonal variations were observed in both the data sets. For SR index, Hyperion performed well using October month data. In comparison with ALI, Hyperion showed high NDVI for both deciduous and evergreen species using October month data. The highest NDVI value is assumed to represent the maximum vegetation “greenness” during that period. Results clearly indicated the superiority of Hyperion in vegetation analysis using NDVI & SR indices.

Keywords : Hyperion data (EO-1), ALI (Advance Land Imager),Shoolpaneshwar Wildlife Sanctuary, NDVI, Simple Ratio (SR)

Abstract

Development of reliable automated techniques for the identification and annotation of exon and intron regions in a gene has been an important area of research in the field of computational biology. Several classical approaches like probabilistic model, artificial intelligence and digital signal processing are already in use to cater to this challenging task. In this work, we propose a Support Vector Machine based kernel learning approach for detecting splice site patterns and their conservation across the species. Electron-Ion Interaction Potential (EIIP) values of nucleotides were used to map DNA character sequences to corresponding numeric sequences. Radial Basis Function (RBF) SVM kernel was trained with cross validation using these EIIP numeric sequences. This was then tested on test gene datasets of multiple species for detection of splice site pattern. Receiver Operating Characteristic (ROC) curves and various other statistical measures have been utilized for displaying the performance of the classifier. Results indicate an inter-species splice site conversion profile.

Introduction

In recent decades, integrating trees with crops for food and wood production has received considerable attention in both tropical (Garrity et al., 2010) and temperate regions (Palma et al., 2007). The growing of trees in combination with the agricultural crop is a land management practice that farmers have been practicing since the ancient period. Seasonal agricultural/horticulture crops, woody perennial trees (multipurpose), and/or animals are major components of various agroforestry systems. According to Sanchez (1995), the science of agroforestry revolves around four aspects: competitiveness, complexity, sustainability, and profitability, and all of these factors must be balanced in order to produce beneficial outcomes. In fact, agroforestry is significantly assisting in meeting the United Nations Millennium Development Goals (MDGs) such as poverty and hunger eradication, improved health, nutrition, and education for people, gender equality, and environmental sustainability, particularly in developing countries (Garrity, 2004, 2006).

An electron impringing on a solid surface may be scattered once, several times, or never. When an incident electron beam bombard atoms of the sample, secondary electrons (SEs) and backscattered electrons (BSEs) emerge from the sample surface. Multiple scatterings occur if the dimension of the material is greater than approximately twice the mean free path of the electrons. This scattering may be elastic or inelastic. A pear shaped volume (interaction volume) is formed as shown in Fig. 1 and this volume of excitation increases with increase in the beam voltage (electron energy) and decreases with the increasing specimen density (ion atomic number).

Plant parasitic nematodes favour the establishment of secondary pathogens viz., fungi, bacteria, virus etc. The nematodes alter the host in such a way that the host tissue becomes suitable for colonization by the secondary pathogens. Even though the nematodes themselves are capable of causing considerable damage to the crops, their association with other organisms aggravate the disease. The nematodes cause mechanical wound which favours the entry of microorganisms. In some cases, the association of nematode and pathogen breaks the disease resistance in resistant cultivators of crop plants.

Nematode - Fungus Interaction

Nematode - fungus interaction was first observed by Atkinson (1892) in cotton. Fusarium wilt was more severe in the presence of Meloidogyne spp. Since then the nematode - fungus interations had received considerable attention on important crops like banana, cotton, cowpea, brinjal, tobacco and tomato. Some examples of nematode - fungus interaction are given in the following table.

Plant parasitic nematodes favour the establishment of secondary pathogens viz., fungi, bacteria, virus etc. The nematodes alter the host in such a way that the host tissue becomes suitable for colonization by the secondary pathogens. Even though the nematodes themselves are capable of causing considerable damage to the crops, their association with other organisms aggravate the disease.

Introduction

The realm of agricultural policies and environmental policies stands as two critical pillars influencing the sustainability and equilibrium of our ecosystem. Agricultural policies primarily focus on regulations and measures instituted by governments or institutions to manage agricultural practices, production, distribution, and rural development. Conversely, environmental policies aim to safeguard and conserve natural resources, biodiversity, and ecosystems while mitigating the adverse impacts of human activities on the environment.

Overview of Agricultural and Environmental Policies

Agricultural policies often encompass subsidies, price controls, land use regulations, and trade agreements aimed at bolstering agricultural productivity, ensuring food security, and supporting rural economies (Fischer & Edmeades, 2010). Conversely, environmental policies encompass a broad spectrum of regulations, such as pollution control measures, wildlife protection, habitat conservation, and climate change mitigation strategies (Boyd & Banzhaf, 2007).

Importance of Understanding Their Interactions

Understanding the interactions between agricultural and environmental policies is crucial due to their interconnectedness and potential trade-offs. Agricultural activities significantly impact the environment through deforestation, water pollution from agricultural runoff, greenhouse gas emissions, and loss of biodiversity (Pretty et al., 2008). Conversely, environmental conditions greatly influence agricultural productivity and sustainability, including soil quality, water availability, and climate patterns. Recognition of these interdependencies is vital for devising coherent policies that foster sustainable agricultural practices without compromising environmental integrity (Tilman et al., 2011). The challenge lies in formulating policies that strike a balance between agricultural production goals and environmental conservation imperatives.

Introduction

Biological control of plant pathogens is currently accepted as a key practice in sustainable agriculture. Plant parasitic nematodes and mycorrhizal fungi are commonly found inhabiting the same soil and colonizing roots of their host plants. These two groups of microorganisms exert a characteristics, but opposite effect on plant health. The potential role of mycorrhizal fungi as biocontrol agent for the control of nematode plant diseases has recently received considerable attention. The literature on the various aspects of VAM research such as interaction of VAM- mycorrhizal fungi with nematode pathogens (Sikora, 1995, Pinochet et al. 1996), biotechnology and future of VAM commercialization (Wood and Cumming, 1992) and trends in mycorrhizal research (Sahay et al. 1998) has been reviewed. The damage due to nematodes diseases is generally decreased in mycorrhizal fungi has shown an antagonistic influence on the population of plant parasitic nematodes (Nagesh et al. 1999, Kantharaju et al.

Extreme weather events, such as hurricanes, droughts, and floods, have become increasingly frequent and intense in recent years, posing significant threats to agricultural productivity, food security, and rural livelihoods. The impacts of extreme weather events can be devastating, resulting in crop failures, livestock deaths, and damage to infrastructure and property. Traditional approaches to dealing with extreme weather events have often focused on top-down, expertdriven solutions, where external experts provide advice and guidance to farmers and rural communities on how to prepare for and respond to extreme weather events. However, these approaches have often been criticized for being ineffective, unsustainable, and neglecting the needs and perspectives of local communities.
In recent years, there has been a growing recognition of the importance of interactive community approaches for dealing with extreme weather events. These approaches involve working closely with local communities to identify their needs, priorities, and capacities for dealing with extreme weather events. They also involve empowering local communities to take ownership of their own disaster risk reduction and management efforts, through the provision of training, resources, and support.
A critical component of these interactive community approaches is the development and implementation of Early Warning Systems (EWS). EWS are designed to provide timely and accurate warnings to communities about impending extreme weather events, allowing them to take necessary precautions to minimize losses and protect their lives and livelihoods (Fig.8.1).

Abstract

Intensification of agriculture during the last few decades has resulted in considerable depletion of the productivity of Indian soils and the balance sheet of potassium (K) is highly negative due to very low level of K fertilization. Although the need for balanced fertilization is known for many years, it is not always observed in practice by farmers. Since the effects of nitrogen are easily seen and crops look more vigorous and yield better, excessive use of nitrogenous fertilizers, accompanied by an inadequate supply of other nutrients, is considered by farmers as a reasonable insurance against yield losses. Often, the lack of K restricts nitrogen uptake and utilization, and hence plant growth. When K is limiting, nitrogen fertilizers are used inefficiently by the plants. This nutrient imbalance is thus closely associated with low nitrogen-use-efficiency (NUE) of crop plants, which is detrimental to crop production and environment – because of the consequences of N unused by the crop.

Introduction

Interactive media is the integration of digital media including combinations of electronic text, graphics, moving images, and sound, into a structured digital computerised environment that allows people to interact with the data for appropriate purposes. The digital environment can include the Internet, telecoms and interactive digital television. No wonder it is difficult for new entrants to understand. The important concepts to hold on to are ‘interactive’ and ‘media’ across a range of ‘delivery channels’ or ‘platforms’. There are many terms used to denote the interactive nature of digital applications—multimedia, new media and interactive design are common examples. Because the interactive sector has quickly evolved through phases, the terms have often been coined to reflect a phase that then gets surpassed. A quick historical overview will give the background that causes confusion for those joining the dynamic sector.

The word Multimedia used to have a specialist connotation for the audio-visual industry. Uses of multiple or mixed media in such analogue systems as slide shows or overhead projectors were known as ‘multimedia’. But this specialist use was superseded by the arrival of digital technology. Integrated digital media was termed interactive multimedia and usually shortened to plain multimedia for convenience. The need to differentiate between analogue (linear) and digital (interactive) uses of media spawned other terms like New Media and Digital Media. The term ‘New media’ carries its own problems as the media associated with the original term are replaced with newer instances of the ‘new’. Obsolescence is endemic in the interactive arena. However, the term remains in use although Digital Media and Interactive Media are more stable terms and are being used increasingly. The term Interactive Media highlights the interactive connotation that is a key characteristic of the difference between the older style media and the new. ‘Social media’ has evolved to describe the more recent success of digital social sites such as Facebook and MySpace, and would include the phenomenon of mobile texting, especially twitter.