eChapters

The word cereal was derived from Ceres, the name of the Roman goddess of harvest and agriculture. Cereals are grasses (members of the monocot family Poaceae, also known as Gramineae) cultivated for the edible components of their grain (botanically, a type of fruit called a caryopsis), composed of the endosperm, germ and bran. Cereal grains are grown in greater quantities and provide more food energy worldwide than any other type of crop and therefore they are called staple crops.

System of Rice Intensification (SRI) was first developed in Madagascar during the 1980s SRI uses less inputs. It uses less seed, water, chemical fertilizers and pesticides but uses more organic manures. Rice grown with SRI technology has large root volume, profuse and strong tillers with big panicles, more and wellfilled spikelets with higher grain weight

Maize is one of the world’s leading crops cultivated over an area of about 194 million hectares with a production of about 1147 million tonnes of grain (2020). Among the maize growing countries, USA has the largest area followed by China, Brazil, Argentina, Ukraine and India. In respect of production also USA stands first followed by China. Regarding average yield per hectare USA ranks first followed by China. The average maize yield in India is only 3065 kg per hectare which is much lower than most of the maize growing countries of the world.

History of jute in India: In Mahabharata times the mention has been made regarding Jute garments as pattagam (patta produced) for jute fibre products and kitajam (insect produced) for silk products. In Ain-e-Akbari, there is mention of sack cloth made from jute, in Rangapur district.

Forages: The term forages denote plants that are used for feeding the animals that are cultivated in the farm, or it may be a wild used for stock feeding grown for the vegetative purpose. The vegetative phase is encouraged and the reproductive phase is suppressed.

Paratition Value

Median is calculated for dividing the series into two equal parts. If the series is divided into four, eight, ten or hundred parts then the measures are called Quartile, Octile, Decile and Percentile respectively denoted by Q_(1,2,3), O_(1,2..7), D_(1,2…9) and P_(1,2…99).

MOMENTS

The term moment in mechanics refers to the measure of a force with reference to its tendency to produce rotation. The strength of this tendency is depen- dent upon the amount of the force and the distance from the origin at which the force is applied. If on both the sides of the origin the force are equal than there will be a balance, at a balanced position the positive product equals the negative product.

Moment is used in Statistics in a quite analogous sense. The class frequencies are looked upon as the forces and the deviations of the different values from the mean are taken as the distances. In other words, moment is the mean of the first, second, third, fourth, etc. powers of deviations of the different values from the arithmetic mean. The first moment about the mean is always zero and second moment about the mean is variance.

If the moment calculated from arithmetic mean are called central moments and denoted by μ with a subscript which indicate the order of the moment. Moments are the mean values of powers of the deviations in any frequency distribution taken about three points

Smart farming, otherwise called smart agriculture, uses progressed agriculture advancements to improve ranch the farm management. By consolidating Industry 4.0 advancements, Precision farming achieves doable outcomes like extend production, resource insurance, and lessened petroleum derivative result. Internet of Things (IoT) accepts a fundamental part in Smart farming, by Precision agriculture to screen and deal with things, as a matter of fact. Information examination is in like manner essential for isolating huge encounters from the colossal proportion of data created by IoT devices. The reconciliation of present day modern digital technology has change into smart farming as information empowered and information driven activity. After affiliation, further examination of each IoT and data analytics frameworks in the agricultural region can be positive.
The total population is predicted to increase by 10 billion in 2050, by enhancing interest for agricultural goods, and notable food. This has brought about a developing interest supply befuddle. Issues including restricted assets, natural changes, and work deficiencies increment the jumble, endangering food security and agricultural production. To defeat these worries, farming should be feasible and utilize state of the art innovation. One important choice is “smart farming,” that attempt to increased food productivity by meeting the population demand in a sustainable manner. This is steady with the Assembled Countries’ Sustainable Development Goals (SDGs), which hold back nothing security utilizing reasonable ways.
Smart farming strategy for agriculture business had changed Ancient farming into smart agriculture. Ancient times, human cultivated the soil to provide food for livelihood. Such activity is known as agriculture, and it follow a long path between agriculture 1.0 to agriculture 4.0. Producers employed agricultural implements including pitchforks and sickles for cultivation throughout the 17th century, a period called as Agriculture 1.0. Farming practices, a huge number of workers are needed to conduct agricultural tasks. Moreover, agricultural

SKEWNESS

Skewness means lack of symmetry. Skewness denotes the tendency of a distri- bution to depart from symmetry. A frequency distribution is said to be skewed if the frequencies decrease with markedly greater rapidity on one side of the central maximum than on the other side. This characteristic of a frequency distribution is known as skewness and the measures of asymmetry are usually called measures of skewness.

The main object of measuring skewness is to know the direction of the varia- tion from an average and to compare the frequency distribution and the shape of their curves.

There are two types of skewness

(i) Positive Skewness (ii) Negative Skewness

GHG emissions as an Agriculture
Agriculture is significant source of GHG emission. Farming records for roughly 10-12 % of absolute anthropogenic GHG emissions, contributing 40 % of all out methane (CH4) and 60 % of nitrous oxide (N2O); agriculture is greatest wellspring of anthropogenic non-CO2 discharges. CH4 begins predominantly from metabolic action of methane-creating microscopic organisms in rice paddy soils and the gastrointestinal maturation of ruminants. N2O discharges get predominantly from farmland soil nitrification and de-nitrification under nitrogen manure application. Rural waste treatment, for example, fertilizing the soil and biogas creation, likewise straightforwardly brings about CH4 and N2O discharges. The change of land use from backwoods to cropland increments soil CO2 discharges. Notwithstanding immediate GHG discharges, farming exercises like apparatus tasks, and compost and feed inputs additionally by implication increment the carbon impression of farming by means of different pathways like petroleum product utilization.
Agriculture a Key Carbon Sinks
Farming moreover addresses a key carbon sink, in spite of the way that it is a huge wellspring of GHG. Horticultural perennials, for instance, field, backwoods, natural item estates and tea houses can get CO2 from the Environment through photosynthesis. It is evaluated that around 13 % of the CO2 emanated by oil based commodity use is consumed by natural vegetation reliably. Vegetation stores are changed into soil natural carbon (SOC) by the activities of microorganisms. In particular, microbial Nero mass can associate with soil minerals to shape a muddled that is a protected carbon pool and the soil carbon can subsequently be taken care of for a surprisingly long time; this carbon tirelessness instrument is known as a soil microbial-mineral carbon siphon. The soil carbon pool (2400 Gt) truly relies upon twice that set aside in the terrestrial vegetation and on different occasions that set aside in the air. The “4 for every 1000 soil carbon overhaul plan,” first proposed by the French service of agribusiness at the Collected Nations Environmental Change Gettogether held in 2015, relies upon the epic carbon-sink breaking point of soils.

KURTOSIS

Kurtosis refers to the extent to which unimodal frequency curve is peaked. Kurtosis is a measure that refers to the peakedness of the top of the curve. Kurtosis gives the degree of flatness in the region about the mode of a frequency distribution.

Types of Kurtosis

There are three categories of kurtosis that can be displayed by a set of data. All measures of kurtosis are compared against a standard normal distribution, or bell curve.

The first category of kurtosis is a mesokurtic distribution. This type of kurtosis is the most similar to a standard normal distribution in that it also resembles a bell curve. However, a graph that is mesokurtic has fatter tails than a standard normal distribution and has a slightly lower peak. This type of kurtosis is considered normally distributed but is not a standard normal distribution.

The second category is a leptokurtic distribution. Any distribution that is leptokurtic displays greater kurtosis than a mesokurtic distribution. Characteristic of this type of distribution is one with extremely thick tails and a very thin and tall peak. The prefix of “lepto-” means “skinny,” making the shape of a leptokurtic distribution easier to remember. T-distributions are leptokurtic.

The final type of distribution is a platykurtic distribution. These types of distributions have slender tails and a peak that’s smaller than a mesokurtic distribution. The prefix of “platy-” means “broad,” and it is meant to describe a short and broad-looking peak. Uniform distributions are platykurtic.

Opportunities

India is home to largest milch animal population, totalling around 100 million cattle and buffalo (out of total population of 300 million) spread over 6.5 million census villages. India is the largest milk producer with CAGR of 4.5%, likely to go up to 5%, which is likely to create around 20 million jobs every year. The most significant factor is that dairy industry is likely to exceed IT industry in employment generation. In year 2018, India produced 146 million tonnes of milk contributing 26% of the agriculture GDP.

But the foundation of dairy industry is economically vulnerable as per animal productivity and fertility are very low leading to meagre profits and frequent demands to enhance procurement price. National schemes like Operation Flood, National Dairy Plan-I and National Dairy Plan II, largely focussed on milk collection, processing, distribution and organising farmers but the animal component has been neglected. Enhancing per animal productivity by empowering farmers to scientific management and breeding to achieve genetic improvement needs urgent attention. Although numerous technologies to improve health and genetics are available but the single most important gap is failure to deliver these to farmers. Unlike western world, India has not been able to develop modern veterinary service delivery models. In order to cater to breeding and health services of cattle and buffalo (300 million in numbers) and also pet animals in rural areas and small towns, huge manpower and infrastructure will be required.

Being polytocous animal, sow give birth several (8-14) piglets in each farrowing, mother pig find several deficiencies and disorders during gestation, farrowing and postnatal conditions. Prior to birth intrauterine competition amongst the feotus piglets for nutrition and gas exchange is a big stress for abortion. Several infectious and non-infectious causes may hamper proper growth and pathological conditions resulting mortality (15-20%) in piglets than weaner and adults (Sarma, et al, 2012). Several risk factors are also responsible for piglet mortality.

Prenatal factors: Insults during pregnancy can impact a piglet’s ability to survive after birth and regulate their body temperature. Some maternal aspects that affect survability and mortality amongst piglets are prolong farrowing duration more than 4 hours, number of parity of sow, usually first and second parity show high mortality than subsequent parities and also older sow lose more piglets.

Introduction

Climate change, a global phenomenon of present time, has emerged as the greatest challenge of the world in the twenty-first century. Rising earth’s surface temperature is one of the most threatening factors coming into view. The substantial changes in the earth’s climate have an impact on ecosystems, economics and societies in a variety of ways (Sharma et al., 2024). It is one of the important environmental, agricultural, human and animal health issue. Climate change is an association of multidimensional effects involving physical characteristics, causes and consequences (Visschers, 2018). The climate change has resulted in to increasing global average temperature of air and ocean, melting of snow, rising of ocean level, change in the rainfall pattern and extreme whether events such as drought, flood, heat waves, storms etc.

Tissue Doppler Imaging

Doppler echocardiography relies on detection of the shift in frequency of ultrasound signals reflected from moving objects. With this principle, conventional Doppler techniques assess the velocity of blood flow by measuring high-frequency, low amplitude signals from small, fast-moving blood cells. In TDI, the same Doppler principles are used to quantify the higher-amplitude, lower-velocity signals of myocardial tissue motion.

There are important limitations to TD interrogation. As with all Doppler techniques, TDI measures only the vector of motion that is parallel to the direction of the ultrasound beam. In addition, TDI measures absolute tissue velocity and is unable to discriminate passive motion (related to translation or tethering) from active motion (fiber shortening or lengthening). The emerging technology of Doppler strain imaging provides a means to differentiate true contractility from passive myocardial motion by looking at relative changes in tissue velocity.

Precision medicine and personalised treatment of systemic or tumour diseases gain their acceptance and application in veterinary care, too. The application of personalised medical protocols and companion diagnostics is easier and ethically well reasoned within the veterinary medical field – and it can offer many advantageous lessons to human clinical transformation, too [1]. In vitro companion diagnostics for testing the applicability and sensitivity levels of certain kinase inhibitor and immunotherapeutic drugs offer a greatly increased efficiency for veterinary cancer patients, too. As the understanding of cancer biology has advanced, the field of veterinary oncology has grown significantly, paralleling developments in human oncology. Precision medicine is an innovative approach to disease treatment and prevention that considers individual variability in genes, environment, and lifestyle. In the context of veterinary medicine, precision medicine aims to provide tailored therapies that are specifically designed for the unique genetic and molecular profile of each animal’s cancer. This approach moves away from the traditional “one-size-fits-all” model, instead focusing on targeted treatments that maximize efficacy while minimizing side effects. Companion diagnostics, such as molecular tests, help determine whether a dog or cat is likely to respond to a particular drug. For dogs and cats, precision medicine holds great promise in improving outcomes for cancers, which are among the leading causes of death in companion animals.

As the heart diseases in companion animals is becoming common as in humans, the research in canine cardiac diseases is one of the most extensive studies in the present scenario. As per the available literature, the overall occurrence of various cardiac abnormalities ranges from 0.37 percent to 1.67 percent in various parts of the country. Whereas, in western parts of the globe, nearly 8 million dogs suffer from heart disease accounting 10% of all dogs in the United States. Heart diseases are common among older dogs; up to 75% of senior dogs have some type of heart condition, and unfortunately, most go undiagnosed due to varied reasons. The recognition of canine cardiac diseases has been delayed, and ignored on account of lack of awareness and knowledge by the owner and inadequate diagnostic facility in our country. Physical examination, radiography, electrocardiography and echocardiography are most useful diagnostic procedures employed in diagnosing the cardiac diseases in dogs. However, majority of the acquired cardiac diseases are being managed medically with one or a combination of drugs. Improved diet management and health care of companion animals have led to increased life expectancy, as a result, cardiac diseases have become pivotal causes of death for both dogs and cats. For such reason, a major objective has become to increase the quality of life by reducing the expansion of cardiac diseases via early detection of diseases and provision of effective treatment.

Introduction

Wound is a disruption to the epithelial integrity of the skin, extended in-depth to subcutaneous tissue and other body parts such as ligaments, muscles, blood vessels, nerves, and bone (Robson et al. 2001). An appropriate wound healing is essential for the restoration of disrupted anatomical continuity (Begum, 2000). We have observed increasing interest in research based on traditional medicines of the world in the recent era (Medina-Franco et al. 2013). Traditional and complementary medicine regains popularity not only in developing countries but also in developed countries. Common and known practices across the globe of traditional medicines include Herbal, Ayurveda, Siddha, Unani, Iranian, Islamic, Vietnamese, Chinese, Acupuncture, African and other pseudo-medical knowledge (Dorai 2012). According to World Health Organization (WHO), traditional medicine ‘the health practices, approaches, knowledge and beliefs incorporating plant, animal and mineral-based medicines, spiritual therapies, manual techniques and exercises, applied singularly or in combination to treat, diagnose and prevent illnesses or maintain well-being’. Saxena et al. (2013) reported that phytochemicals present in plants are liable for preventing disease and promoting health. Antibiotics used in the treatment of wound infections are now proved to have adverse effects in the human body and in pathogens where resistance is emerging. Attention has, therefore, turned towards extracting biologically active compounds from plant species having wound healing property (Pattanayak and Sunita, 2008).

Abstract

Conversion of an already arid land into a desert through indiscriminate human actions magnified by droughts. Such actions include overgrazing, repeated burning, intensive farming, and stripping of vegetation for firewood. One quarter of the earth’s surface is threatened by desertification - an area of over 3.6 billion hectares. Since 1990, Six million hectares of productive land are lost every year due to land degradation. Desertification threatens the liveihoods of more than one billion people and has already made over 135 million people homeless. Desertification does not occur in linear, easily mappable patterns. Deserts advance erratically, forming patches on their borders. Areas far from natural deserts can degrade quickly to barren soil, rock, or sand through poor land management.

Deep learning has significantly advanced automated plant phenotyping and disease detection by enabling computers to identify complex patterns in large volumes of plant imagery and sensor data. This chapter examines the fundamentals and applications of deep learning architectures in plant science. It discusses convolutional neural networks for image-based plant analysis and advanced architectures including recurrent neural networks, transformers and hybrid models. Data acquisition through imaging and sensor technologies is also considered. The chapter highlights how deep learning can support automated identification of plant characteristics, disease symptoms and crop health conditions. Challenges related to datasets, environmental variability, computational requirements and model generalization are examined alongside emerging research opportunities.