eChapters

Geographic location of India is in Northern hemisphers and located between 80 4' & 370 6' N latitude and 640 7' & 970 25' E longitude. Extent of area is 329 M Ha or 329 x 104 Sq.Km or 3.29 MSq.Km. The physiography of India can be broadly grouped into triangular plateau, mountains, Indo-gangetic plains and islands. India is divided into twenty agro ecological regions and sixty five agro ecological sub regions as per NBSS and LUP. Agro ecological regions( AER) is referred to identification and demarcation of land units having comparable landform, soils, bioclimatic conditions and the length of growing period (LGP).

India is endowed with varied types of soils and this could be attributed varied relief features, landforms, climatic realms and vegetation types in the subcontinent. In ancient times, soils used to be classified in India into fertile and sterile soils. Later, the inherent characteristics and external features of soils such as texture and colour, were used to classify the soils. Based on texture, soil types were identified as sandy, clayey, loam, etc. In terms of colour, they were red, yellow, black, etc.

Scientific surveys of soils in India have been conducted by various agencies since independence. All India Soil Survey Organization, established in 1956, carried out detailed soil surveys in the catchment areas of major river valley projects like the Damodar Valley. Later, the National Bureau of Soil Survey and the Land Use Planning (NBSSLUP) ofthe Indian Council of Agricultural Research, established in 1976, made extensive studies on Indian soils and classified them as per the USDA SoilTaxonomy

1.1. Definition

Soil: Thin layer of earth’s crust made up of disintegrated and decomposed rocks, complex mineral compound, organic matter, water/air and living organism like bacteria, fungi, insects and worms and serves as the natural medium of growth of plants.

Provides nutrients, moisture, anchorage (support) and air to plants.

Parent materials are igneous rocks, sedimentary rocks and metamorphic rocks.

Soil Science: Science dealing with soil (Natural resource) on the surface of the earth, including pedology (Soil genesis, classification and mapping), physical, chemical, biological and fertility properties and their relation to management for crop production.

19.1. Plant Available Fraction of Nickel, Cadmium, Lead and Chromium

The DTPA method was originally developed to extract Mn, Cu, Fe and Zn from slightly acidic to alkaline soils (Lindsay and Norvell, 1978), but in some cases it can be used for other metals also such as Nickel (Ni), Cadmium (Cd), Lead (Pb) and Chromium (Cr). Among them Ni is considered as an essential micronutrient. The DTPA is used to extract soil metals by forming chelates with free metals in solution, which lowers their ionic activities so that additional quantities of metal are released from the soil until equilibrium is attained. Triehanolamine (TEA) is used to buffer the extracting solution at pH 7.3 in an attempt to optimize the extraction of various metals. Calcium chloride is included in the extracting solution to minimize dissolution of CaCO₃.

26.1 Plant Available Fraction of Nickel, Cadmium, Lead and Chromium

The DTPA method was originally developed to extract Mn, Cu, Fe and Zn from slightly acidic to alkaline soils (Lindsay and Norvell, 1978), but in some cases it can be used for other metals also, such as Nickel (Ni), Cadmium (Cd), Lead (Pb) and Chromium (Cr). Among them Ni is considered as an essential micronutrient. The DTPA is used to extract soil metals by forming chelates with free metals in solution, which lowers their ionic activities so that additional quantities of metal are released from the soil colloids until equilibrium is attained. Triehanolamine (TEA) is used to buffer the extracting solution at pH 7.3 in an attempt to optimize the extraction of various metals. Calcium chloride is included in the extracting solution to minimize dissolution of CaCO₃.

Introduction

Spatial data is different from a non-spatial data in the sense that it always has a spatial reference system which is nothing but a pair of coordinate values and a system of reference for these coordinates. For example, the locations of benchmark soil series of India can be defined as pairs of longitude and latitude in decimal degree values with respect to the prime meridian at Greenwich and zero latitude at the equator. The World Geodetic System (WGS84) is a frequently used system of reference on the Earth. The different types of spatial data are: (i) point, which is a single point location, such as a GPS reading or a geocoded address (ii) line, which is defined as a set of ordered points, connected by straight line segments (iii) polygon, an area, marked by one or more enclosing lines, possibly containing holes and (iv) grid, a collection of points or rectangular cells, organised in a regular lattice. Among the above said spatial data, the first three are vector data models and represent entities as exactly as possible, while the fourth data model is a raster data model, representing continuous surfaces by using a regular tessellation. In this chapter, we discuss how a spatial data can be handled in R. There are several packages available in R in which spatial data analysis can be handled e.g. sp, rgdal, raster etc. All the contributed packages for spatial data analysis in R address two broad areas: moving spatial data into and out of R, and analysing spatial data in R. In the following section, the sp package for spatial data handling is briefly discussed.

Introduction

The conventional approach of the human person for plant classification is comparing the color and shape of the leaves, but by using Artificial Intelligence analysis can provide quicker and more detailed results by analyzing the morphology of the leaves and provides more details about the leaf properties. Species Recognition has been a very important part of agriculture and there are thousands of species in plants which can be identified by a single person and even if it does so It will take a huge lot of time to do it.

In this chapter we will discuss and built a project related to identify species in flower with an image. In this project we will be using Artificial Intelligence different branches like machine learning, deep learning and computer vision to build such an amazing project. So, let’s begin

4.1. Data Set Generation for Modelling
We have generated 54 transfomed spectra by applying different combination of parameter. Now this spectrum is to be tagged with respective soil sample and desired soil properties for which spectral modeling has to be done. Here we are generaring data tagged with CaCO3.
For that we have to use cbind() fuction. Here we are keeping soil properties in first column and spectra after that column. We are storing data in R memeory and naming data in such way that we can identify properties and transfoemed spectra attached with that data. After generating data for modeling, data is divided into calibration and validation data set by using random sampling method or Kedard-
Stone method
# Data set for CaCO3 with raw spectra ####
Data.CaCO3.pc.113=cbind(data10nm_$CaCO3_equivalent_per, First_ derivative10nm113) #case 1st
Here in case 1st, CaCO3 indicating properties and 113 indcating without smoothed 1st derivative, 1st order polynomial 3rd point spectra is tagged with these soil samples. Same was followed with other cases also

For applying spectral data models with other machine learning and regression algorithms we have to follow all steps upto Chapter 4 till the partition of whole datainto calibration and validation data set. After that different algorithms is used to develop others models so that,the best model among the different algorithms can be identified and used for better perdiction of soil properties.
5.1 Principal Component Regression (PCR)
Principal Component Regression (PCR) is a regression technique that combines Principal Component Analysis (PCA) and multiple linear regression. It is particularly useful when dealing with multicollinearity in the predictor variables, as it transforms the predictors into a set of uncorrelated components before performing the regression.
set.seed(123)
pcr.CaCO3.pc.223SGS<- train(CaCO3.pc ~., Train_Data.CaCO3.pc.223SGS,
method =”pcr”, trControl = custom,
tuneLength=25, importance = TRUE)
Explaination for code train(CaCO3.pc ~ ., data = Train_Data.CaCO3.pc.223SGS, ...)
Formula (CaCO3.pc ~ .): Specifies the model formula where CaCO3.pc is the
dependent variable and . indicates that all other columns in Train_Data.CaCO3.
pc.223SGS are independent variables.
Data (data = Train_Data.CaCO3.pc.223SGS): The dataset used for training the model.
method = “pcr”:Specifies that the model to be used is Principal Component Regression (PCR).
trControl = custom:Specifies the training control method. custom should be an object of class trainControl created earlier, defining the resampling method (e.g., cross-validation).This example sets up 10-fold cross-validation. tuneLength = 25:Specifies the number of different values for the number of principal components to consider. The package caret will try 25 different values to find the optimal number of components.

Data pre-processing is a crucial step in data analysis and machine learning that involves preparing raw data for further processing and analysis. Proper preprocessing helps in improving the quality of the data, which in turn enhances the performance of machine learning models. Smoothening VIS-NIR spectra enhances signal quality by reducing noise, crucial for better spectral data interpretation. Key methods include Savitzky-Golay and moving average filters, wavelet transform (WT), and frational derivatives.
Savitzky-Golay Filter: The Savitzky-Golay filter is widely used for smoothing spectral data. It preserves the original shape and features of the signal better than simple averaging filters.Savitzky-Golay Filter fits low-degree polynomials to successive subsets of data points using linear least squares, effectively preserving the signal’s original shape and features, making it ideal for spectroscopy. Moving Average:Moving Average is a simple method that reduces random noise by averaging neighboring data points. While effective, it can sometimes blur sharp features.
Wavelet Transform: Wavelet transform is effective for denoising spectral data, especially when dealing with non-stationary signals.Wavelet Transform (WT) decomposes spectral data into multiple scales, providing both time and frequency information. It is particularly useful for identifying features like peaks and trends. WT includes Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT), and Maximal Overlap Discrete Wavelet Transform (MODWT). Fourier Transform Filtering: Fourier transform can be used to remove highfrequency noise components from the spectra.
Median Filter: Median filtering is useful for removing outliers and preserving edges in the spectral data.
Fractional derivative:Fractional derivative generalizes traditional derivatives to non-integer (fractional) orders, allowing for arbitrary order derivatives. This provides a flexible, nuanced approach to modeling and analyzing complex, nonlinear, or memory-dependent behaviors.

Introduction

Soil is one of the vital resource, and is the basis of the existence of mankind. Management of this resource including its conservation and utilization is of crucial importance. It is a base on which all life depends. In the recent past, with burgeoning populations and the national goals of seeking self-sufficiency in food and fibre production, this resource base is slowly being stripped. While natural systems often adapt to stress in a remarkable fashion, some relationships - once destroyed - can never be restored. Soil resources can be assessed and monitored through its spectral signatures gathered through remote sensing.

The term ‘remote sensing’ is commonly restricted to methods that employ electromagnetic energy (such as lights, heat, micro wave) as means of detecting and measuring target characteristics. Sensors acquire data as various earth surface features reflect or e mit electromagnetic energy (EM energy). Remote sensing is the technology that is now the principal method (tool) by which the earth’s surface and atmosphere (as targets or objects of surveillance) are being observed, measured, and interpreted from vantage points. Remote sensing of the earth traditionally has used reflected energy in the visible and infrared, and emitted energy in the thermal infrared and microwave regions to gather radiation that can be analyzed numerically or used to generate images whose tonal variations represent different intensities of photons associated with a range of wavelengths that are received at the sensor.

Interaction of radiant energy, exhibiting the properties of both waves and particles, with matter is dealt in spectroscopy. Matter may be defined as material, existing in solid or liquid or gaseous phase, which is composed of molecules, atoms, or ions. Atoms, molecules, and ions, irrespective of their physical state, remain in constant motion in a matter. Molecules may rotate, vibrate, and move from place to place in space. While radiant energy interacts with matter, these molecular motions are influenced to some extent. It is observed that radiations from ultraviolet and visible range of electromagnetic spectrum lead to the movement of bonding electrons from lowest energy state (ground state) to higher energy state (excited state) in the molecules, while absorption of IR radiation causes the molecule to vibrate more. Such changes, referred to as transition, are responsible for changing the molecular energy level.

Speculation and hedging are important ways of minimizing price risk in business.

Speculation: Risk is taken by person specializing in business without much consideration of business trends.

Hedging: A calculated risk is taken.

The spermosphere represents a short-lived, rapidly changing, and microbiologically dynamic zone of soil surrounding a germinating seed. It is analogous to the rhizosphere, being established largely by the carbon compounds released into the soil once the seed begins to hydrate. These seed exudations drive the microbial activities that take place in the spermosphere, many of which can have long-lasting impacts on plant growth and development as well as on plant health.

Introduction

According to FAO estimates, about 50% total geographical area in India is under various degraded hazards and about 2.1 million ha of land is getting degraded and deforested annually (Sehgal et al. 1998). Out of that underground water in arid and semi-arid areas is saline or sodic in nature. While spices, medicinal and aromatic plants may be a boon for these areas, which not only withstand under adverse conditions but also could be productive and helps in mitigating these problems. However, other crops are remain less or unproductive in these areas.

Spices are low volume and high value crops and have tremendous export potential, which is witnessed by several invasions faced by this Indian peninsula during ancient time. Medicinal and aromatic plants have unequally market potential with the world demand of herbal products growing at the rate of 7 percent per annum (Aishwath and Tarafdar, 2007). Seed spices and aromatic plants are the natural source of flavour, perfumes and fragrance widely exploited by essential oil industries in the world. Owing to considerably diversity of edaphoclimatic conditions ranging from temperate to tropical and arid to sub-humid that prevails in India and an array of medicinal and aromatic flora occurs in wild state. Many exotic essential oil bearing plants, including mints, citronella, geranium, patchouli, lavender and chamomile have been naturalized in India. As the country has emerged as one of the main producers of seed spices and some of the essential oils, theses plants are significant foreign exchange earners. Some of the aromatic plants especially grasses can sustain both biotic as well as abiotc stresses, even in the areas unsuitable for conventional crops (Aishwath et al., 2008). Most of the seed spices crops are seasonal crops and comes up well with limited moisture and nutrients. Therefore, underutilized natural resources can be utilized profitably by crop diversification.

1. Introduction
Good Laboratory Practice (GLP) is a systematic quality system essential for
ensuring the reliability, consistency, and integrity of laboratory data, particularly when the findings are used for regulatory decision-making. GLP ensures that laboratory studies are conducted in a scientific manner, maintaining high standards of ethical practice, precision, and reproducibility, with a primary focus on ensuring public and environmental safety. Introduced by the U.S. Food and Drug Administration (FDA) in 1978, GLP gained global recognition through the Organisation for Economic Cooperation and Development (OECD), which established guidelines for its implementation. GLP principles are now widely adopted in regulatory testing for pharmaceuticals, chemicals, food safety, and environmental testing. The OECD’s guidance on GLP has been instrumental in developing standards across the globe, ensuring that test results are scientifically valid and can be accepted internationally. In Asia, particularly in India and other developing countries, GLP compliance has gained increasing importance as pharmaceutical, environmental, and chemical industries expand. Regulatory bodies in these regions have gradually aligned their standards with international norms to ensure their competitiveness in global markets. This chapter discusses the key SOPs necessary for GLP compliance and provides a regional perspective, focusing on India and other Asian nations, where GLP frameworks are evolving to meet international standards.
2. Objective of GLP
The overarching objective of GLP is to ensure that laboratory data is accurate, reliable, and reproducible across different studies. The scientific and regulatory communities rely on GLP to ensure that findings from preclinical studies and safety evaluations can be trusted and utilized for regulatory approval.
The key objectives of GLP include
• Accuracy and Integrity of Data: GLP ensures that every aspect of data collection, analysis, and reporting is conducted with integrity and accuracy, preventing errors or falsification that could affect regulatory decisions.

Rainfall, runoff, evaporation, and other hydrological variables exhibit considerable natural uncertainty and therefore cannot be treated as entirely deterministic phenomena. Statistical and probability methods make it possible to organize observations, describe their variability, identify relationships, estimate extreme events, and predict values associated with selected probabilities. Hydrological data may be analysed daily, weekly, monthly, seasonally, or annually and expressed through averages, maxima, minima, dispersion, skewness, and frequency. This chapter introduces statistical terminology, correlation, regression, probability distributions, plotting positions, goodness-of-fit tests, model-evaluation tools, reliability, standard error, and risk analysis. These techniques are essential for flood-frequency analysis, planning, and hydraulic design.

11.1. Storage fungi

Store fungi include all species of Aspergillus, Fusarium and Penicillium.

Aspergillus flavus, A. candidus, A. niger, A. parasiticus, A. repens, A. rubber, A. versicolor, etc. Penicillia etc

Penicillia are more prevalent in cooler climate.