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The artificial intelligence (AI) world is quickly growing as it reaches several different industries. You currently will see AI used for many different purposes from agriculture to automotive; with the passage of time you will see even more of it. Agriculture is one of the most important branches of the IT industry. Agriculture is an important industry and is a big part of our economy’s base. In terms of annual sales, the agricultural industry contributes to our economy almost 17%.AI is becoming a technical advancement that boost and protects crop yield, as climate change and populations rise. Here in this chapter basics of Artificial Intelligence have been discussed how it can be related to agriculture.

1. Introduction

Artificial Intelligence has become most prominent in current generation. Every field is utilizing Artificial Intelligence for their better results and which also result in saving a lot of time. As Artificial intelligence is boosting different sectors to increase the productivity and efficiency and its utilization across different sectors like in the field of CIVIL Artificial Intelligence can be used to analyze the structures of buildings, dams etc., and Artificial Intelligence solutions are assisting in so many ways to overcome the traditional challenges in every field.

I. What is Artificial Intelligence

Artificial Intelligence is one of the most prominent field of Computer Science Engineering which attempts to redefine the tasks which are carried out by humans in their daily life with better results and maximum accuracy. Artificial Intelligence is actually meant to ease the work of human where the word artificial states the work done without any human interference and intelligence states the capabilities like human brain to perform tasks. The field artificial Intelligence has given so many definitions from time to time which can be typically stated as

Artificial Intelligence (AI) is technology which can store, retrieve, manipulate, manufacture, transmit or receive, analyze any digital information and can apply machine vision for increasing efficiency and workability of the system. The AI permits systematically scope of ‘3S’ i.e. Safety, Security and Surveillance, which is also known as manmade intelligence. In fact, it needs more reliable and accurate data or information for processing and correction for predicting performance of any system and devices.

Artificial Intelligence (AI) technology is increasingly used in our everyday lives. It is used in a variety of industries from gaming, journalism/media, to finance, as well as in the research fields from water resources to robotics. In this chapter concept and applications of AI are discussed in brief.

6.1 Historical Background

After World War II, a number of people worked independently on intelligent machines. The English mathematician Alan Turing was the first who explained the concept of intelligent machines through a lecture in 1947. He stated that intelligent machines could be developed by programming computers rather than by building machines. Since late 1950 many researchers are working on intelligent machines, mostly on programming computers

Abstract

Natural sunlight is the cheapest source available, but for horticulture it is not always attainable in sufficient quantities. Therefore, the use of artificial light has become very common in order to increase production and quality. Under artificial lighting technologies, LED lighting is one of the technology that can help in producing crops and flowers in a more effective and sustainable way around the world. The benefits of LEDs are that the spectrum and intensity can be selected and adjusted, the energy efficiency is higher than most conventional light sources and their small size, durability, long lifetime, cool emitting temperature, and the option to select specific wavelengths for a targeted plant response make LEDs more suitable for plant-based uses than many other light sources. These advantages, coupled with new developments in wavelength availability, light output, and energy conversion efficiency, can bring about a revolution in horticultural lighting. Light quality plays a major role in the appearance and productivity of ornamental and food specialty crop species. Far-red light, for example, is important for stimulating flowering of long-day plants (Deitzer et al., 1979; Downs, 1956) as well as for promoting internode elongation (Morgan and Smith, 1979). Blue light is important for phototropism (Blaauw and Blaauw-Jansen, 1970), for stomatal opening (Schwartz and Zeiger, 1984), and for inhibiting seedling growth on emergence of seedlings from a growth medium (Downs, 1956). The interactions are complex and continue to be unraveled at the molecular level (Devlin et al., 2007), but much of our understanding of these responses comes from studies with narrow-waveband lighting sources, in which LEDs provide obvious advantages. Thus, one potential role of LEDs in horticulture could be to enhance desired characteristics for specific crops. The artificial LED lightings increase the possibility of year-round production of ornamental plants, which will subsequently increase the income of growers related to ornamental industry. Light-emitting diodes pave the way for a better understanding of the interaction between light and pests, diseases, natural enemies and plants, as they make possible the use of very narrow wavelengths. LEDs as an option for reducing the use of growth retardants and obtaining better product quality in ornamental pot plants. Production of secondary metabolites increase with higher blue light ratio. Secondary metabolites production are increased and act against Reactive Oxygen Species (ROS) and pathogens, precondition the plant for environmental changes so they can cope with stress more efficiently.

12.1. Introduction

Artificial Neural Networks (ANNs) are computational models inspired by the way biological neural networks in the human brain process information. They consist of interconnected nodes or “neurons” arranged in layers: an input layer, one or more hidden layers, and an output layer. Each neuron in the network processes data and passes the results to the next layer. ANNs are designed to recognize patterns, learn from data, and make predictions or classifications based on that learning.

In an ANN, the connections between neurons are weighted, meaning each connection has a strength or value that determines the influence of one neuron on another. Through a process called training, the network adjusts these weights to minimize errors in its predictions. This is typically done using techniques like backpropagation, where the error is propagated backward through the network to update the weights (Abiodunet al., 2018).

ANNs are highly versatile and have been applied across various domains, including image and speech recognition, natural language processing, and decision-making systems. Their ability to model complex relationships in large datasets makes them a crucial component in the field of machine learning and artificial intelligence (Ahmed et al., 2023).

INTRODUCTION

There is growing concern in both the scientific community and the general public over the possibility of important climatic changes due to increases in relatively active gases in the atmosphere, a phenomenon commonly referred to as “climate change.” In the past century, the activities of a rapidly expanding and industrializing human population have added significant quantities of heat retaining gases to the atmosphere. For example, the Intergovernmental Panel on Climate Change (IPCC) estimates that by 2100 increases in greenhouse gases will likely lead to an increase in temperature of between I0⁰ C and 3.50⁰ C (Houghton et al., 1996) and an increase in sea level of between 13 and 94 cm (Warrick et al., 1996). The IPCC also reports that warmer temperatures will most likely intensify the hydrological cycle, leading to an increase in the precipitation intensity and number of storm events (Houghton et al., 1996). Our results of this study also agree with it.

INTRODUCTION

Artificial Neural Network (ANN) is a branch of the field in computer science known as “Artificial Intelligence (AI)”. The field goes by many names, such as connectionism, parallel distributed processing, neuro-computing, machine learning, and artificial neural networks. A tremendous growth in interest of this computational mechanism has occurred since Rumelhart(1986) rediscovered a mathematically rigorous theoretical framework neural network. Traditional (physically-based) modeling of physical processes tries to explain the underlying processes. On the contrary, the data-driven models are based on a limited knowledge of the modeling process and rely on the data describing input and output characteristics. Data-driven models are based on pure relationships between input and output data and not the physical principle linking input and output. They offer real advantages over conventional methods, including the ability to handle large amounts of dynamic, non-linear or noisy data and such tools can be especially useful when the underlying relationship are not fully understood. Data-driven modeling uses results from such overlapping fields as data mining, artificial neural networks, rule-based type approaches such as expert systems, fuzzy logic concepts, and machine learning systems.

Artificial neural networks are suited to problems where relationships between the variables to be modeled are not well understood. Mathematically, an ANN may be treated as a universal approximator. The ability to learn and generalize ‘Knowledge’ from sufficient data pairs make it possible for ANN to solve large-scale complex problems such as non-linear modeling, pattern recognition, classification, control and others – all of which find application in water resources management today. Since the early nineties, ANNs have been successfully used in water management area such as rainfall-runoff modeling, stream flow prediction, groundwater modeling, water quality, hydrologic time series, and reservoir operation.

The artificial neural network (ANN), a data driven model, is inherently suited to the problems that are mathematically difficult to describe. An ANN is a flexible mathematical structure that is capable of identifying complex nonlinear relationships between input and output data sets, where explicit knowledge of the internal processes is not available. In this chapter general structure of ANN is described and also explained briefly with a case study.

7.1 What is Artificial Neural Network?

An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous system, such as brain process the information. An ANN is an adaptive (Adaptive means that the system parameters are changed during operation i.e. training phase), most often nonlinear system that learns to perform a function (an input/output map) from data. In more practical term, neural networks are non-linear statistical data modeling tools. They can be used to model complex relationships between inputs and outputs or to find out patterns in the data.

Introduction

Global diversity in domestic animals and aquatic organisms is considered to be under threat worldwide. Worldwide 11 % birds, 25 % mammals and 34 % of fish species are threatened (Lovell-Badge, 2001; Vemuganti and Balasubramanian, 2002) and aquatic ecosystem is becoming more vulnerable to disasters due to run off from agriculture and industrial sources, oil spills or sudden environmental changes leading to total elimination of stocks from different ecosystems. The multifarious human activities in open waters have threatened the fish diversity all round the world. Fish worldwide are in crisis. The effective population sizes in fish hatcheries are getting smaller and smaller that causes loss to farmers worldwide. The World Resources Institute has reported that nearly 70 percent of the world’s marine fish stocks are over fished or are being fished at their biological limit. Apart from fish, across all live stock species there is a shrinking pool of genetic diversity. For instance, the effective population size for all dairy cattle breeds is less than 60 animals. To preserve the gene pool of unique populations by protecting them from extinction, loss of genetic variability (inbreeding) and dilution due to inter breeding with unrelated populations (hybridization) gene banks or germplasm repository can play a vital role. Germplasm repositories in fisheries could be used for: 1) restoration of threatened species, 2) brood stock improvement through genetic modification of a targeted population, and 4) information and technology development including gene pool database management.

The artificial seed production technique was first used in clonal propagation to cultivate somatic embryos placed into an artificial endosperm and constrained by an artificial seed coat. Today artificial seeds represent capsules with a gel envelope, which contains not only somatic embryos but also axillary and apical buds or stem and root segments (Vdovitchenko and Kuzovkina, 2011).

The prime requirements for preparation of artificial seeds are high frequency somatic embryogeniccalli, synchronous embryo development and maximum conversion of embryos into plants. Success in production of synthetic seeds mainly depends on how best callus development and plantlet regeneration are achieved. However, over time the morphogenetic competence of differentiated cultures declines (Lynch and Benson, 1991). Therefore, the primary goal of synthetic seed production is to produce somatic embryos that resemble more closely to the true seed embryo in storage and handling characteristics so that they can be utilized as a unit for clonal propagation and germplasm conservation. Synthetic seeds may be hydrated or dehydrated and may be quiescent or not. Encapsulation of micropropagules enables to satisfy the requirements of synthetic seed development. The gelling agents used for encapsulation for production of synthetic seeds act as protective cover. The encapsulated synthetic seeds also contain growth nutrients, plant growth promoting microorganisms (mycorrhizah, Rhizobium, etc.), and/or other biochemical constituents necessary for optimal embryo-to-plant development (Fig. 4.1). Fungicides may also be used in the liquid gelling medium to protect them during storage and germination in vitro and in vivo.

Introduction

1. The condition has been reported the world wide in growing broiler chickens.
2. The affected birds show clinical signs usually during 4 - 5 weeks of age. The clinically affected birds are smaller, listless with ruffled feathers, pale head and shrunken comb. Severely affected birds have distended abdomen, which restricts their normal movement. Death may occur suddenly, and not all the broilers which die of the condition have ascites. The incidence of the disease varies between 2 to 20%.

Fruit, vegetables and their products are important sources of ascorbic acid. Aonla, guava and citrus fruits are good sources of Vitamin C (Ascorbic acid).Ascorbic acid is sensitive to heat, light and oxygen but is stable in acidic
 media. The methods for estimation of Ascorbic acid are based on the reduction of 2,6 -dichlorophenol - indophenol dye by ascorbic acid and those based on the reaction of dehydroascorbic acid with 2,4- dinitrophenylhydrazine. However,the 2,6 -dichlorophenol - indophenol dye reduction method is most commonly used in the estimation of ascorbic acid in fresh fruit and vegetables and their processed products.
2,6 dichlorophenol - indophenol visual titration method
The dye, which is blue in an alkaline solution and red in an acid solution, isreduced by ascorbic acid to a colorless form. The reduction is quantitative and specific for ascorbic acid in solutions in the pH range of 1-3.5.
Reagents
i. 3% Metaphosphoric acid (HPO3) solution:Dissolve 3g sticks/ pelletsofMetaphosphoric acidin a volumetric flask and make volume to 1oo ml with glassdistilled water.

Ascorbic acid (vitamin C) is present in almost all fresh fruits and vegetables in varying quantities ranging from 0.02 to 1.0 mg per g fresh weight. It is most abundantly found in bittergourd and berries.

The method for estimation of ascorbic acid is given here below as described by Albrecht (1993). It is an easy and simple method based on titration technique.

Explant

The tissue obtained from plant to be cultured is known as Explant. It may include the parts of shoots, leaves, stems, roots, flowers and callus.

Maintenance of Aseptic Environment

All culture vessels, media and instruments used in handling tissues as well as the explants must be sterilized. The importance is to keep the air surface and floor free of dust. All operations are carried out in laminar air-flow, a sterile cabinet.In general, the methods of elimination of these sources of infection can be grouped under different categories of sterilization procedures: 

Aseptic packaging is a specialized and advanced method used to extend the shelf life of perishable foods without the need for refrigeration. This technique involves sterilizing both the food product and the packaging material separately and then filling and sealing the product in a sterile environment. Aseptic packaging has transformed the food industry, allowing products such as dairy, juices, soups, and sauces to remain fresh and safe at room temperature for extended periods. The ability to maintain product quality, nutritional value, and safety while reducing reliance on cold storage has made aseptic packaging a preferred choice in both food manufacturing and distribution. The process of aseptic packaging is both complex and precise, as it requires thorough sterilization of every component to ensure food safety. Packaging materials, typically a combination of paper, plastic, and aluminum, are carefully selected to withstand high temperatures while providing an effective barrier against contamination. This chapter explores the principles of aseptic packaging, its significance in food preservation, and the advanced technologies and materials used to achieve and maintain sterility in packaged foods.

7.1. Introduction

In the modem lifestyle, people are extremely busy in their work and lack the time to cook their own foods. They mainly depend upon processed and packaged food for their nutritional intake. So it becomes essential that the packaged products should be safe, hygienic and shelf stable. To preserve the food for long duration, thermal processing is most suitable method. Aseptic packaging is a form of thermal food processing. Aseptic packaging can be defined as the filling of sterile containers with a commercially sterile product under sterile conditions and then sealing the containers so that reinfection is prevented; that is, they are hermetically sealed. 

Vegetative propagation enables horticulturists to multiply plants while retaining desirable genetic characteristics of selected mother plants. This chapter covers important methods including cuttings, layering, budding, grafting, division, and specialized vegetative structures. The physiological basis of rooting, wound healing, callus formation, compatibility, and environmental requirements is discussed. Grafting and budding are particularly important for combining desirable scion characteristics with suitable rootstock traits such as vigor, disease resistance, stress tolerance, and adaptation. Proper sanitation and selection of healthy propagation material are emphasized to minimize pathogen transmission. Modern techniques such as rooting hormones, mist chambers, protected propagation, and clonal multiplication further improve the efficiency and uniformity of vegetative propagation.

The inorganic residue remaining after the destruction/burning of organic matter in foodstuffs is called ash. Ash contents in food commodities roughly represent the mineral contents, however, some minerals are likely to be lost due to volatilization or some interaction between constituents during ashing. Estimation of ash content is also used as a measure to judge the quality of fruit products. The presence of high ash content or low alkalinity of the suggests the presence of adulterants in the sample. Similarly, the presence of a high amount of acid-insoluble ash indicates the presence of sand and other silicious matter in the food products.
Principle
Organic matter is burnt off at as low a temperature as possible. Heating is done in stages, first to char the product thoroughly and finally to ash it at 550ºC in a muffle furnace. The inorganic material left after burning organic matter is cooled and weighed.
Apparatus
* Silica Crucibles
* Muffle furnace
* Desiccator
* Balance

Botany: The ash gourd(Benincasa hispida) belonging to the family cucurbitaceae is an annual creeping vine that can either climb structures or be allowed to spread out on the ground. This plant features large green leaves and thick stems covered with coarse hairs. The showy, golden yellow blooms appear early in the summer, and female flowers give way to round or spherical fruits. Young ash gourds are covered with a soft down that disappears with maturity. Fully matured gourds have a white, waxy coating covering the surface.

Field preparation:The field selected for seed production must not have been sown with ashguard in the previous season. This is done to avoid volunteer plants that cause admixture.After proper ploughing, at a spacing of 2.5 x 2 m distance take pits having 45 cm length, width and height. Ten days after that, apply 10 kg FYM and urea 30 g. Super phosphate 72 g and potash 19 g per pit. Then mix the above nutrients with soil and fill the pits and level them.

Soil and climate:Soil with neutral pH must be selected. Loam or clay loam soils are best suited. Higher organic matter will lead to production of vigorous seed.Seed is very sensitive to whether. Hence selecting the right season is necessary. Though ashgourd can grow through out the year, seed crop should be grown such that the seed matures in cool dry climate. This will facilitate proper ripening of fruits and reduce the pest and disease infection. Seasons are selected with this idea in mind. In

I. FILL UPS

1. Ashgourd is a _____ crop.
2. Stigma receptivity is for _____.
3. Ashgourd is a _____ pollinated crop.
4. Ashgourd fruit is a _____.
5. The isolation distance for ashgourd certified seed production is_____.