eChapters

The process of replacing the existing mature forest with a new generation of the forest trees is called forest regeneration. It can also be defined as process by which new tree seedlings become established after forest trees have been harvested or have died from fire, insects, or diseases. Forest regeneration is one of the basic tasks of foresters. Regeneration plays such a prominent role in silviculture that most silvicultural systems are named for their method of regeneration.

7.1 Gel Electrophoresis
    
7.2 Polymerase Chain Reaction (PCR)
    
7.3 Restriction Fragment Length Polymorphisms (RFLPs)
    
7.4 Random Amplification of Polymorphic DNA (RAPD)
    
7.5 Amplified Restriction Fragment Length Polymorphism (AFLP)

7.1. Rice

• Treatment of paddy seeds in diluted bio gas slurry for 12 hours increases resistance of seedlings to pests and diseases.
• During panicle formation in paddy, the flowers of Cycas circinalis are placed on sticks in paddy fields @ 4/ac. Its unpleasant odor repels ear head bugs.
• About 30 kg of tamarind seeds are applied for an acre of paddy field 1 day after transplanting to boost up the crop growth and yield.
• Soaking the paddy seeds in diluted cow’s urine before sowing, considerably reduces the incidence of leaf spot and rice blast
• Presoaking of paddy seeds in milk increases its resistance against ‘tungro’ virus and ‘stunt’ virus
• For control of red leaf spot disease in paddy, the seeds are soaked in ‘Pudina’ leaf extract (Mentha sativa) for 24 hours
• ‘T’ shaped bamboo stands are placed in many places in the paddy fields so that birds can sit on them and feed on the larvae and adults of rice pests.
• Sowing on eighteenth day (Aadipperukku) of Tamil month Aadi (Jul-Aug.) ensures good harvest.
• Daincha (Sesbania spp.) seeds are sown on paddy main fields when paddy nursery is raised and the grown up daincha is ploughed in-situ during field preparation.
• Plough the main field for four to six times for better yield.

Introduction

It is already established that mathematical modeling aims to describe the different aspects of the real world, their interaction, and their dynamics through mathematics. It constitutes the third pillar of science and engineering, achieving the fulfillment of the two more traditional disciplines, which are theoretical analysis and experimentation. Nowadays, mathematical modeling has a key role also in aquaculture. The problem is the complex modeling other than counting. Modeling and reasoning with data of different kinds can get extremely complex. Data streams are common source of Big Data. Now the task is to collect, prepare, represent, model, reason, visualize and additional issues like usage, quality, context, streaming, scalability. A few publications are available on modeling of fisheries and aquaculture in Indian context with live examples of modeling (Roy, et al. 1998, 2002, Roy et al., 2002, Ghosh et al., 2017, Roy, et al., 2006, Roy et al., 2008, 2012). A book on exclusively with various types of model applicable in fisheries and aquaculture has been published (Roy et al., 2009). In all these earlier publications conventional statistical and econometrics techniques are applied. But today there is sea change in this field with the advancement of technologies like artificial intelligence, machine learning, and big data analytics. There are attempts to apply these cutting edge technologies aiming sustainable productivity through efficient management of resources. In the following sections an attempt has been made to present an overview of the advances in analytics along with a briefing on what was developed in the past, and what is available today. Before proceeding to the analytics, I am presenting the scenario of basic characteristics of data along with different types of data generated today with automation of aquaculture industry.

Remote sensing images have an important characteristic known as spatial frequency. Spatial frequency is defined as the number of changes in pixel value per unit distance. Few changes in pixel or brightness value over an area in an image are known as low spatial frequency area like agricultural fields, forests, water bodies, etc. If the changes are large, then it is known as high frequency area like roads, built up areas, etc. Spatial filtering is an approach for extracting quantitative spatial information by taking into account the neighbouring pixel values along with the independent pixel value. Therefore, filtering is a technique for modifying or enhancing an image. Filtering is done to improve the interpretability of raster image or to extract certain information from them. It is a neighbourhood operation because the value of any pixel in the output image is determined by applying certain algorithm on the neighbourhood pixels of the corresponding input pixel. The nature of algorithm to be applied depends upon the information which user wants to derive from the raster image. Filtering operations are mainly done for smoothening, sharpening, noise removal and edge enhancement. A filter is a kernel of small array which is applied to input pixel and its neighbouring pixels in the raster. There are many types of filters which can be applied to a raster image in R. These types with their respective R – code examples are explained further.

History of food safety starts with knowledge about food hazards. The term “safe food” can be defined from different angles since the end users are highly heterogeneous. However, the accumulating data and day by day emerging information aboutfood borne diseaseslead to a boom in public awareness about food safety. Food safety can be defined as “The strategies and activities aimed to protect foods from biological, chemical, physical, and allergenic hazards that may occur during all stages of production, distribution, and consumption” (Paperella, 2020). According to the World Health Organization (WHO), unsafe food,which contains hazardousmicroorganisms or chemicals, causesmore than 200 ailments, ranging from diarrhoea to malignancies among which diarrhoea is common. 

3.1 Overview

3.1.1 Rice straw: What is it?

When harvesting paddy rice, rice straw is produced as a by-product. Approximately 0.7-1.4 kilograms of rice straw can be obtained for each kilogram of milled rice, depending on the variety, moisture content during harvest, and cutting length of the remaining plant stems. The rice straw is separated from the grains after the plants are threshed manually using stationary threshers or with modern combine harvesters, as shown in Figure 3.1.

Rice straw is a type of lignocellulosic biomass comprising cellulose, hemicelluloses, and lignin. Figure 3.2 illustrates that pretreatment can be used to separate these components. Unlike lignin, which is found in the cell wall, cellulose and hemicellulose are organic fibers.

The crop production has been found to make wide use of chemicals with hundreds of newly imported molecules. Unnecessary and excessive use of inorganic chemicals have led to an upsurge in the ecosystem and imbalance of nature. Natural enemies and bees get destroyed with chemical pesticides. Other side effects include resurgence of target pests and outbreak of secondary pests. Moreover, the residues of the pesticides in food products and environment have caused grave health issues. The contamination of pesticides into the fruits causes a quality decrease of the fruits, which is widely reported. The best available solution for sustainable agriculture can be the non-chemical organic/ natural farming that does not permit the usage of inorganic and synthetic chemicals for crop cultivation. Natural farming is the ecology-based production management system promoting and It provides biodiversity, biological cycles, and soil biological activity. The basic principles of organic/natural farming are materials and practices that enhance the ecological balance of natural systems that integrate parts of the farming system into the ecosystem. The first purpose of natural farming is to improve the health and productivity of interdependent communities of soil life, plants, animals, and people, that is termed as ‘one health concept’.
Natural farming is a system in which laws of nature are applied to agricultural practices. It has an advantage of natural biodiversity on every area of farmed resources; it also fosters the intricacy of living organisms, plant and animal alike, which would form each specific ecosystem to grow in harmony with food crops. Insects are indeed very mobile and well adapted to farm production systems as well as pest control tactics. Organic/natural farms focus on management, not eradication, of these insects. Thus, success in organic/natural farms depends on learning the biological, ecological, and behavioural information of the insects. The biological information as to what the insect needs to survive can be used to determine if insect pests can be deprived of some vital resource. Ecological information can be used to inform how the insect interacts with the environment and other species. shape an environment resistant to pests.

Effective crop monitoring is essential for maintaining plant health, optimizing farm operations, and improving agricultural productivity. This chapter explores the application of artificial intelligence for real-time crop monitoring and farm management. AI systems integrate information from sensors, satellites, drones, mobile devices, and other sources to assess crop conditions and identify emerging problems. Machine learning and deep learning techniques can analyse crop images, environmental parameters, growth patterns, and field conditions to provide actionable insights. The chapter examines real-time monitoring, image analysis, crop health assessment, anomaly detection, and decision-support applications. It also highlights the role of IoT and multimodal agricultural data in developing intelligent and responsive farming systems.

Introduction

Rice is the world’s most important staple crop and is a primary source of energy and nutrition for about half of the world’s population. So it is the most important cereal crop with respect to food security of common people. Due to increasing world population and shrinkage of farm land holdings there is an urgent need to increase the grain productivity. Therefore, increasing grain yield has become the primary objective in many rice-breeding programs. India is the second highest rice producer (about 120 million ton/year) in the world and able to meet the demand of rice for the entire Indian population. But along with yield improvement of rice, quality has now become a foremost consideration for rice consumers and millers. Quality is defined as “the totality of features and characteristics of a product or service that bears its ability to satisfy stated or implied needs” (International Standard Organization (ISO) 8402 1986). The concept of grain quality covers many physico-chemical properties such as grain shape and size, milling and cooking properties, amylose content, stickiness, chalkiness, texture, colour, gelatinization characters and various nutritional properties. Thus, rice grain quality generally includes five main classes i.e. (i) milling quality, (ii) physical quality, (iii) cooking and eating quality (iv) sensory quality and (v) nutritional quality. Grain quality and its assessment are very important to consumers, millers and rice breeders who are engaged in varietal improvement programme, related to new features such as high nutritional quality, high yield potential, highly resistant to abiotic or biotic stresses, higher water use efficiency etc.

Plant diseases significantly reduce crop productivity, quality, and economic returns, making early and accurate detection essential for effective crop protection. This chapter examines the application of machine learning and computer vision for identifying diseases at an early stage. It discusses agricultural image acquisition using RGB, hyperspectral, and sensor-based technologies, followed by image preprocessing, segmentation, feature extraction, and classification. Machine learning algorithms such as support vector machines, random forests, and convolutional neural networks are considered for disease recognition. The chapter also explores the integration of AI with IoT and mobile technologies and presents practical applications while addressing challenges involving datasets, environmental variability, model generalization, and computational requirements.

Abstract

Climate change is a matter of great concern considering aquatic resources and the productivity. The likely impact of Climate Change is noted in the domain both in marine as well as estuarine capture fisheries, fresh water capture fisheries and freshwater aquaculture. Climate change is having a linkage with spread of fish diseases as increasing water temperature will shift the balance in favor of either the host or pathogen changing the frequency and distribution of diseases. There are a no of adaptation and mitigation measures in aquaculture in order to increase the productivity and properly maintain the survivability of many aquaculture species.

Elevation or height data of land surface above a defined vertical datum is stored in the form of raster grids which is known as Digital Elevation Model (DEM). The DEM data can be processed to derive many useful parameters like slope, aspect, curvature, roughness, etc. This process of quantitatively describing terrain obtained from DEM data through computation of many variables is known as Digital Terrain Analysis. Digital terrain analysis has applications in the various fields like hydrology, ecology, geomorphology, etc. DEM data for digital terrain analysis are available from the satellites like ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), Cartosat, ALOS (Advanced Land Observation Satellite), and SRTM (Shuttle Radar Topography Mission).

In this exercise, we are going to discuss the derivation of some primary and secondary terrain attributes from DEM data in R software. For performing digital terrain analysis in R, following packages are required.

Introduction

Vast proportions of the population of the world are dependent on plants as their principal source of food either directly or indirectly. Recent decades have also seen increased numbers of people turning to alternative medical therapy based upon traditional folk medicine or food as a form of medicine. These alternative therapies emphasise the importance of a ‘good diet’ in maintaining and restoring health. Under these circumstances, vast majority of population may see diet therapy, dietary supplements and non-invasive alternative therapies as a safer, less technological and more natural way of maintaining health and treating and preventing individuals from illness. These alternatives may be seen as empowering the consumer and shifting the locus of control away from the medical establishment. This chapter covers many crucial topics to understand the chemistry and bioactivity of the active compounds of plant origin. It also aims to cover the latest research on the bioactive compounds.

5.1    Population Growth
    
5.2    Exponential Growth
    
5.3    Population Explosions
    
5.4    Over Population
    
5.5     The Causes of Rapid Population Growth
    
5.6    Ways and Means to Solve Over Population
    
5.7    Environment and Health
    
5.8    Family Welfare Programme
    
5.9    Human Rights
    
5.10    Child Welfare
    
5.11    Women Welfare

11.1 INTRODUCTION

Plant growth regulators have remained an important component in horticulture from time immemorial because they were effective means of quantitative as well as qualitative improvement in growth and development of crops. Plant Growth Regulators (PGR) are diverse group of organic compounds other than nutrients, produced artificially or by plants which in low concentrations promote, inhibit or modify the physiological process in plant. The term includes both naturally occurring phytohormones as well as synthetic compounds. These are produced naturally in higher plants, controlling growth or other physiological functions at a site remote from its place of production and active in minute amounts. Plant Growth Regulators are used in different forms like liquid, powder, paste etc. “Hormone” is Greek word derived from “hormao”, which means to stimulate. Thimone (1948) suggested the use of term ‘phytohormones’ as the organic substance, which are produced naturally in plants, synthesised in one Part And usually translocated to other part wherein every small quantity affects the growth and other physiological function of the plants. To distinguish them from the animal hormones they are termed as phytohormones. Auxin was the first hormone to be discovered in plants and at one time considered to be only naturally occurring plant growth hormone. The role of plant growth regulators in various physiological processes such as seed germination, f lowering, fruiting, seed development, fruit ripening and yield etc. in different crop plants is well established (Kiran et al., 2011). There are five classical phytohormones: auxins, gibberellins, cytokinins, ethylene and abscisic acid. Besides classical phytohormones a variety of other compounds that play roles in plant growth and development have also been identified, including jasmonic acid, salicylic acid, brassinosteroids and polyamines (Basra, 2000). The application of plant growth regulators in agriculture has started in 1930 in the United States. Ethylene, a naturally occurring substance, is one of the first plant growth regulators being discovered and used successfully for enhancing f lower production in pineapple (Fishel, 2006). Its toxic effects to human beings are low.

Introduction

Expanding human and livestock population, fast-growing industrial requirements and urbanization are putting ever-increasing pressures on land resources, creating competition and conflicts which results in sub-optimal use of both land and its natural resources. With no additional land for horizontal expansion of agriculture, there is an urgent need to find viable technologies for enhancing productivity and resource conservation on a sustainable basis. In this context, Agroforestry offers huge potential not only to diversify and increase overall land productivity to meet the demands of fuel, fodder, timber, medicine and other non-woody forest products but also to enhance green cover, and reduce the pressure on forest (Chavan et al. 2016; Handa et al., 2016). Agroforestry offers an affordable alternative in place of expensive conventional conservation measures for providing insurance against risks caused due to weather aberrations, controlling erosion hazards and ensuring sustainable production of the land on a long-term basis. The main objective of agroforestry is to take the advantage of complementary relationships between trees, crops, and livestock in such a way so that sustainability, productivity stability, and profitability of the system is maintained. In India, agroforestry practices are important component in the various developmental programmes schemes like Flood Control/Management Programmes, Multipurpose River Valley Projects, Agriculture Development Programmes, Integrated Rural Development Programmes (IRDP), National Watershed Development Programme for Rainfed Areas (NWDPRA), Forestry Development Scheme, Drought Prone Area Development Programme (DPAP) and Desert Development Programme (DDP) from government sector and wood based industry from private sector. After 2000 a transitional shift in agroforestry observed to tackle the ill effects of intensive agriculture, climate change and nutritional security. Under changing climatic scenarios agroforestry rose as a silver bullet to offer not only single benefits but also a basket of multiple benefits. In the present chapter a journey of agroforestry research and developments, region-specific models, area under agroforestry, initiative of government on policy aspects and challenges are elaborated extensively.

9.1 Introduction

Biomass conversion is a transformative process that harnesses the energy and chemical potential of organic materials derived from living organisms. It encompasses various methods of converting biomass, including agricultural residues, forestry by-products, algae, and even waste materials, into valuable products such as biofuels, chemicals, and energy. This process is a cornerstone of sustainable resource utilization and plays a critical role in addressing environmental concerns, reducing reliance on fossil fuels, and promoting a circular economy. Biomass conversion methods range from biochemical processes like fermentation and anaerobic digestion to thermochemical processes such as combustion, pyrolysis, gasification, and liquefaction. Each method employs distinct conditions, catalysts, and technologies to break down complex biomass structures and convert them into desired products.

Agriculture has progressed from labour-intensive traditional practices through mechanization and the Green Revolution to precision agriculture and Agriculture 5.0. This chapter traces that historical development and identifies limitations associated with conventional farming, including inefficient resource use, environmental degradation and weak adaptability. It explains how GPS-guided machinery, sensors and precision technologies prepared the foundation for intelligent systems. Artificial neural networks, deep learning and fuzzy logic now process data from IoT devices, UAVs and satellites to support automated irrigation, crop monitoring and soil analysis. The chapter emphasizes that integrating human knowledge with computational power is increasingly essential for global food security, sustainability and climate resilience.