eChapters

Plant physiological processes determine growth, development, productivity and adaptation to changing environmental conditions. This chapter examines how artificial intelligence can improve understanding of plant physiology and stress responses. It considers physiological processes including photosynthesis, transpiration, nutrient dynamics and hormonal regulation and explores how AI can analyze complex biological, environmental and phenotypic datasets. Machine learning, deep learning, computer vision and predictive analytics support interpretation of information obtained through sensors, imaging, remote sensing and omics technologies. The chapter emphasizes early detection and prediction of stresses such as drought, salinity, temperature extremes, nutrient imbalance and pathogen pressure. It also discusses explainable AI, digital twins and federated learning as emerging approaches for plant stress research.

Introduction 
The evolution of health care since 1950s has been attributed to artificial intelligence (AI). Remarkable advances in the health sector are being made particularly after the years, following 2010, thanks to AI technology. The significance of AI as a game-changer in healthcare has tremendously grown together, with its accompanying range of challenges, opportunities, and reward. There is increasing utilization of AI across public health activities including biomedical research with its capacityto reduce medical errors and improveaccess to care. The efficiency and ability of AI to analyse large volumes of data in different medical fields so as to identify diseases and guide treatment plans has turned it into an area of significant impact in clinical decision-making. Machine learning as well as data analytics are examples of AI technologies employed in monitoring, diagnosis, treatment and measurement of risks/benefits thereby transforming various healthcare processes. It is revolutionizing medical practices in several areas such as diagnosis, treatment, rehabilitation and surgical operations [Tagliaferri et al., 2020]. Studies have found that, the use of AI has improved patient outcomes and simplified management duties, resulting to an efficient health service delivery system. As the influence of AI continues to grow, understanding its implications, benefits, and challenges becomes essential for healthcare professionals and policymakers alike.

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.

Early and accurate detection of plant diseases is essential for protecting crop productivity and reducing agricultural losses. This chapter explores artificial intelligence-based plant disease detection using image processing and deep learning. It introduces major plant diseases and the fundamentals of image processing before examining deep learning approaches for identifying disease symptoms from plant images. AI models can automate disease recognition and support timely interventions in smart agricultural systems. The chapter presents a proposed methodology and discusses results and analysis, practical applications and limitations. It also considers challenges associated with data quality, environmental conditions and model performance, followed by future directions for developing more accurate, scalable and intelligent plant disease diagnostic systems

Plant phenotyping is essential for understanding plant growth, development, morphology, physiology and responses to environmental conditions. This chapter examines how artificial intelligence is transforming conventional phenotyping through automation, high-throughput data collection and advanced analysis. It covers image-based, UAV-based and sensor-based phenotyping, together with genotype-to-phenotype modelling and machine learning and deep learning methods. Imaging systems, spectral sensing, drones, sensors and robotic platforms provide diverse sources of plant information for AI analysis. Applications include trait measurement, disease and stress detection, crop performance prediction and resource management. The chapter also discusses limitations, emerging technologies, digital twins, IoT, cloud computing and future opportunities for scalable and sustainable phenotyping.

Soil health and nutrient availability are fundamental determinants of plant growth, crop productivity and sustainable agricultural production. This chapter examines how artificial intelligence can support soil health monitoring and nutrient management. It introduces soil health concepts and nutrient dynamics before exploring AI technologies capable of processing soil, environmental and crop-related datasets. AI-based approaches can assist in identifying soil conditions, assessing nutrient requirements and developing site-specific nutrient management strategies. Integration with precision agriculture technologies can improve fertilizer-use efficiency and reduce unnecessary inputs. The chapter also considers practical case studies, challenges and future opportunities. By combining AI with agricultural data, soil monitoring can become more timely, precise and useful for informed crop management.

Any undesirable change in physical, chemical and biological properties of air is called air pollution. Air pollution interferes with wellbeing of living livelihood. Various human made and anthropogenic sources which deteriorate the air quality are called air pollutant. Some of the common air pollutant are dust, smoke, smog, fog, aerosol, SO2, CO2, O3 and NOx etc.
Sources of Air Pollution
The sources of air pollution are classified into two categories:
1. Natural source
2. Man-made source
1. Natural sources
• Ash from burning volcanoes, dust from storm, forest fires
• Pollen grains from flowers in air are natural sources of pollution
2. Anthropogenic (man-made) sources
They are population explosion, deforestation, urbanization and industrialization whose effect can be explained as fallow:
a) Power stations using coal or crude oil release CO2 in air
b) Also furnaces using coal, cattle dung cakes, firewood, kerosene, etc.
c) Steam engines used in railways, steamers, motor vehicles, etc. give out CO2.
d) So do Motors and internal combustion engines which run on petrol, diesel and kerosene. etc.
e) Vegetable oils, kerosene, and coal as household fuels
f) Sewers and domestic drains emanating foul gases
g) Pesticide residues in air
h) Carbon monoxide and Nicotine from smoking
i) Thermal power plants pollute air by emitted sulphur dioxide and fly ash
j) Fertilizers and pesticides

Air or atmosphere constitutes one of the integral components of the environment. Among all the environmental components, the air has the highest fluidity. Therefore, the entry of a foreign substance into the air imparts a quick and severe effect on the health of the organisms. Air in the biosphere has a mixture of various gases in a dynamic proportion which is conducive to maintaining the health and sustenance of all living organisms. An alteration in the composition of the atmosphere beyond a certain limit adversely affects organisms’ health, and might even be of more serious consequences for life.

Air pollution, thus, is defined as the occurrence of undesirable gases (e.g., carbon monoxide, sulfur dioxide, etc.) or particles (e.g., suspended particulate matter) in such concentrations that are deleterious to terrestrial life (vegetation, animals, and human beings) and some physical assets, like buildings, statues, and monuments. Air pollution especially affects the health of human beings living in highly modified (anthropogenic) ecosystems, e.g., cities and industrial areas.

Air is one of the most important constituents of man’s environment. It is calculated that a roan breathes about 22,000 times a day inhaling about 16 kg of air by weight. Air is present everywhere and its entrance from one place to another cannot be checked. Clean and pure air is very essential for the health and survival of man.

Algae are a large and diverse group of eukaryotic organisms that contain chlorophyll and carry out the oxygenic photosynthesis.

Study of algae is referred as phycology or algalogy

They vary from cyanobacteria or blue green algae by their eukaryotic nature.

Introduction

Soil algae are not as numerous as bacteria, actinomycetes and fungi. Furthermore, these are photosynthetic microorganisms. Recent work has led to a more complete knowledge of the ecology and importance of terrestrial algae. These algae are found abundant in the habitats in which moisture is adequate and light is accessible.

Classification

Soil algae are classified into four major classes:

1. Cyanophyceae (Blue green algae)
2. Chlorophyceae (Green algae)

Introduction 
The increase in the concern over the climatic changes as well as the dwindling fossil fuels reserves have intensified the search for the sustainable energy sources. The algal biofuels garnered the attention because of their fastest growth rates, as well as their capability to grow in a non-arable land, higher lipid content, as well as the capability to absorb the CO2. Therefore, the larger scale implementation of the algal biofuels can face a lot of environmental, economic, as well as the technological challenges which needs further investigations (1). One of the current green energy and renewable alternative answers to the world’s current energy problems is the development of algal biofuel (2). Because algae biofuels are environmentally beneficial, they have been seen as a clean energy source (3). An integrated biorefinery strategy could be applied to bio transform algal biomass into many biofuels, including bioethanol, biobutanol, biogas, biohydrogen, and biodiesel, in order to fulfil the energy requirement. Nonetheless, a lot of an obstacles stand in the way of the development, manufacturing, as well as use of microalgal biomass technology. (4). These problems include the creation of affordable microalgal production systems, efficient microalgal growth systems, effective microalgal harvesting techniques that save energy, effective microalgal extraction methods, and economical and environmentally friendly microalgal conversion procedures. Many microalgal technologies are examined in this chapter, along with their applications, difficulties, case studies, and prospects. (5). Algae are appealing feedstock sources for the manufacturing of biofuels. Algae may be applicable to produce important goods like as nutraceuticals, biodiesel, biogas, as well as bioethanol (6). Biofuels are benign to the environment, renewable, and biodegradable. Algae are very valuable because of their higher lipid content as well as fast development. The largest class of algae, known as chlorophytes, includes both macro- and microalgae and is used in bioremediation, the water treatment, the food production, the medicine, as well as the energy production. We discussed many techniques for planting, harvesting, and processing in this chapter. The selection of high lipidcontaining algae strains, commercial harvesting, high infrastructure, operation, and maintenance expenses, and water evaporation problems seem to be the main obstacles. It will take creative and effective methods to make the manufacturing of algae-based biofuels desirable. Increasing the production of biofuels will contribute to the preservation of natural resources, which will protect the environment (7).

1. Introduction
Metals pollution in water particularly in wastewater has become a center ofconcern due to social changes such as industrialization and urban development(Blaszczak-Boxe et al., 2023). It is in this context that most of the conventionalwastewater treatment fails to remove the heavy metals in question satisfactorilyand that there is a pressing need for new ideas that would be sustainable. Thischapter aims to discuss algal bioremediation as an environmentally suitableprocess for the decontamination of wastewater containing toxic metals.

Abstract

Different algal taxa involved in the formation of lichen/s are presented. To-date 43 algal genera, comprising members of Cyanophyta, Chlorophyta and one genus each of families Phaeophyceae (brown algae) and Xanthophyceae (yellow-green algae) are recorded, based on personal observations coupled with literature. The various algal taxa forming different lichens belonging to various families is presented. The taxa Trebouxia de Puymaly and Trentepohlia Born. amongst green algae; Scytonema Ag. and Nostoc Vauch. Among blue-green algae are dominant genera involved in the formation of lichens. The yellow and brown algae are only of rare occurrence.

Alkali soil
 
Alkali soils are those that contain measurable amounts of soluble salts mostly as carbonates and bicarbonates of sodium. 

Principle
Alkalinity of a water sample is can be determined by titrating with standard sulphuric acid. Titration to pH 8.3 or de-colourization of phenolphthalein indicator will indicate complete neutralization of OH and ½ of CO3 while to pH 4.5 or sharp change from yellow to pink of methyl orange indicator, that will indicate total alkalinity (complete neutralization of OH, CO3, HCO3)
Chemicals
1. Phenoplthalein indicator
2. Methyl orange indicator
3. Sodium carbonate, Na2CO3
4. Sodium hydroxide, NaOH
5. Sulphuric acid, H2SO4
Reagents
1. Phenolphthalein indicator: Dissolve 0.5 g in 500 ml 95% ethyl alcohol.
2. Methyl orange indicator: Dissolve 0.5 g of methyl orange in 100 ml of water and dilute to 1000 ml in deionized or distilled water (CO2 free deionized or distilled water should be use).
3. Standard Sulphuric acid, H2SO4 (0.02N): to prepare 0.1N H2SO4 take 3 ml conc. H2SO4 and dilute it in to 1000 ml of deionized or distilled water. Standardise it against standard Na2CO3.
4. Sodium carbonate, Na2CO3 (0.05 N): Take 7g standard Sodium carbonate (Na2CO3) dry at 250 oC for 4 hr and cool in desicator then weigh 5.3 g and transfer it a 1000 ml volumetric flask after that fill to mark with distilled water. Solution should be use within 1 week.

Animal Husbandry is a major activity in hilly areas and women contributed the most in maintaining this enterprise. It is of immense importance to conduct study for alleviating drudgery of farm women in hilly areas. Since women’s contribution in animal husbandry is significant to our economy, their technological empowerment is equally important and needs to be given high priority.

This chapter covers a study of a project conducted in Udaipur district of Rajasthan. The concerted efforts made under this project through skill training, field demonstration, constant technical support and motivation resulted in acquisition of knowledge and skills in handling various drudgery reducing technologies in animal husbandry. Technological intervention has made farm women aware of new technologies available to assist them in performing the tasks. As a result there is a change in the practices of women and they have begun to adopt new technologies to improve their work efficiency in animal husbandry. By the use of technologies, there is considerable reduction in the drudgery of farmwomen. Equipment like rake, shovel, and wheel barrow and chaff cutter has not only decreased time and energy consumption but have improved work efficiency in terms of output.

Introduction

Livestock development has always been a major concern in the Indian economy along with agriculture. Animal husbandry and Dairy development play a prominent role in the rural economy in supplementing the income of rural household particularly, the landless, small and marginal farmers. It also provides subsidiary occupation in semi-urban areas and more so for people living in hilly, tribal and drought prone areas where crop output may not sustain the family. Animal husbandry output constitutes about 30 percent of the country’s agricultural output. In recent years, animal husbandry has become more indispensable for human kind because of increasing pressure on cultivable land owing to the population explosion.

In the current situation, crop intensification is of topmost importance to increase agricultural production from ever-limiting and degrading resources. In this regard, the potential of agroforestry can be thought that offers ample opportunity to enhance farm output with multifaceted benefits (Xu et al., 2019). Forests are known to maintain ecological balance in many ways with greater ecosystem services; hence, the advantages of forestry can be included in agroecosystem in the form of agroforestry which enables yield of forest products and crops together

The basic points that are to be considered while choosing appropriate enterprise in IFS are:

1. Soil and climatic features of an area/locality.
2. Social status of the family and social customs prevailing in the locality.
3. Economic condition of the farmer (Return/income from the existing farming system).
4. Resource availability at farm and present level of utilization of resources.
5. Economics of proposed IFS and credit facilities.
6. Farmer’s managerial skill.
7. Household demand.
8. Institutional infrastructure and technological knowhow.
9. Market facilities.