eChapters

In this chapter of machine learning algorithms we will cover the basic algorithms of machine learning with their implementation using dataset and discuss their pro’s and cons of algorithms for better understanding and how to use them in any project or with any dataset to get an correct result which can useful for research or study.

Key Component

We defined a data set composed of audio, images and binary labels that provided an understanding of how we could train a model from snippets to classifications. This sort of problem is one of many kinds of machine learning problems when we try and predict a specified unknown label given known inputs, provided that a dataset of examples for which labels are known is called supervised learning. Let’s discus what are the major key components we have to get there

Agriculture 4.0, otherwise called “Digital Agricultural Revolution”, addresses a change in outlook in agriculture, utilizing state of the art innovations to improve different parts of cultivating tasks. These innovations include the Internet of Things (IoT), Artificial brainpower (Artificial intelligence), Huge Information, distributed computing, Cloud computing, Decision Support System (DSS), high level detecting innovation, and independent robots. Sensors and mechanical technology assume a urgent part in gathering fundamental field information, which is then sent to a neighborhood or cloud server by means of IoT innovation for capacity, handling, and examination. Large information and Artificial intelligence-based strategies can be utilized to change over this information into significant experiences. To work with client connection and informed independent direction, a DSS outfits clients with the fundamental devices to improve the agricultural framework and embrace proper activities.
AI (ML), a subset of Artificial intelligence, has shown extraordinary potential in upgrading different parts of agriculture 4.0. It very well may be characterized as a PC program or framework that can learn explicit errands without being expressly modified to do so. An interaction includes the utilization of a PC to go with choices in light of various information inputs. For this situation, information means a bunch of models. Marked information is frequently utilized for managed learning undertakings (where the model gains from named models), and unlabeled information may be utilized for solo learning assignments (where the model tracks down examples and designs in the information).
ML for sure advantage from a lot of information to accomplish significant exactness in their errands. With regards to farming, acquiring immense and various information can be at times testing yet crucial for the outcome of ML models. IoT sensors are instrumental in gathering a different scope of farming information as they can be decisively sent across fields to catch pertinent data with respect to, for example, soil conditions, Climate factors, crop wellbeing, and animals’ measurements. The inescapable reception of IoT innovation works with constant and continuous information procurement, empowering

As we know there are different branches of artificial Intelligence and machine Learning of one of them which has become a most advance and widely used branch of artificial Intelligence in every field of this generation whether it may be either electronics, civil, mechanics, Bioengineering, medical or agriculture etc., In this chapter we will study about machine learning and its types with the most common algorithms of machine learning which are being mostly used in different scenarios and their implementation using python language has been discussed in the implementation part of this book. Here we will also see where machine learning can be used.

i. Introduction

Machine learning is a subset of Artificial Intelligence and one of the most important part of artificial Intelligence. It is a field of Computer science which is totally differs from traditional computational approaches. In traditional approaches, a set of instruction is explicitly provided to the algorithm to solve any problem. Whereas in Machine Learning is a domain of an AI which uses a statistical analysis and can enables a system to always learn from its data without any external inputs in order to give an essential output. Due to this machine learning makes computers in advance to make model which provide decision based on input data.

Machine Learning involves a lot of algorithms which usually based on their requirements and functionalities to use where and when. To make a machine learning model we always require the data on which model can be made. Machine learning uses the algorithm which continuously learns from the data available as training data. A machine learning model is generated after the training of algorithm from the data. After the training completes, when trained model is provided with other real-life data then you can get an essential outcome. For example, in figure 3.1 Machine learning Model we use a predictive algorithm and make a model by providing it with some data, and later on we can receive prediction’s based on real data as input.

Nitrogen Content in Indian Soil Because of tropical and subtropical climates Indian soils have very low nitrogen content and in the majority of cultivated soil it varies from 0.02% to 0.13%. Low levels of N in Indian soil is primarily attributed to climate and secondarily to cropping. Climate plays the dominant role in determining the nitrogen content in soil through the effect of soil organic matter content. Generally heavy textured soils have higher N content than loamy or sandy soils. Other factors remaining the same, total N content in soil decreases with the increase in annual temperature at a given humidity level, on the other hand, it increases with increasing humidity level at a given annual temperature. Maximum N content in soil occurs in the surface layer of soils and decreases with depth in soil profile. The variation of distribution of N content in soil with depth is attributed by varying accumulation of organic matter in soil profile.

Introduction

 India, situated between the latitude of 08004’ and 37006’ N and longitudes of

68007’ to 97025’ E, has a geographical area of 329 Mha. Physiographically, it

can be divided into three broad regions, viz., Peninsula (a triangular Plateau in

the Deccan and South of the Vindhyas), Mountain region of the Himalalayas

(Extra-Penninsula), and the Indo-Gangetic Plain separating these two regions.

Geologically, a great part of the Penninsula is occupied by the gneiss, schists

and other rocks of diverse nature. Red soils (Alfisols) generally predominate in

this region. The western and central parts predominate in basaltic rock having

Black cotton soils of different thickness (Vertisols, Inceptisols). The mountain

region shows the development of marine sediments of all ages, especially, in

north of the Himalayas. The vast Indo-Gangetic and other plains of Pleistocene

origin are composed of alluvium of the great river systems flowing in this

region. The alluvial soils of this region depend on the age of alluvia and degree

of development.

Climatically, India shows three distinct seasons:

 i) Cool and mainly dry winters from November to February

ii) Hot and mainly dry summers from March to June

iii) Monsoon rainy season from mid-June to September.

Fertilizer recommendations are generally made on the basis of soil test. Recommendations of fertilizer for top-dressing are best made on tissue testing.

SOIL TEST

A number of approaches have been adopted to increase the precision of soil-test values in making fertilizer recommendations. Four most important ones are discussed.

Low-Medium-High Approach

Making fertilizer recommendations based on soil test for a nutrient involves conducting a large number of ?eld experiments on soils similar to those which are intended for making recommendations. For an individual nutrient (say P for example) experiments are conducted with graded doses of P on a crop for which the fertilizer recommendations are to be made. Thus, agronomic experiments are to be conducted for each crop on each kind of soil. The response curve can then be divided into three sections: a, low soil-test value and high response to fertilizer zone; b, medium soil-test value and medium response to fertilizer zone; and c, high soil-test values and no response to fertilizer zone. In India, this was done

In his classic book ‘The Ascent of Man’, Bronowski has written, “When I was a young man, we all thought that mastery came from man’s domination of his physical environment. Now we have learned that real mastery comes from understanding and moulding the living environment.” Our Biosphere, the living environment that supports us all, has problems and we humans are mainly responsible. We do not live ‘gently’ on the earth. The seven billion of us require air, water, food, energy, housing and amenities and we have developed the earth to provide them and have also created problems. That the problems exist are serious is the bad news. That we are recognizing them and that man has shown ability to organize and grow in ascending order from time to time is the good news.

1.1 The relation between man and the earth’s resources

The balance between the demand which a community of plants or animals makes on its environment and the ability to satisfy those demands is not the static equilibrium of a laboratory balance with equal weights on either side. It is more like the unstable balancing of a circus acrobat - a series of swings and overcorrections, resulting in an oscillation on either side of the true balance point. Thus a species of wild life increases in number according to the availability of its food supplies, but then goes beyond the optimum number. The natural correcting factors of starvation or migration reduce the population, but there is an overcorrection, and soon the cycle starts again with increasing numbers.

1.1.1 The Malthusian Thesis

In primitive societies the human population also oscillates about a mean as the limiting factors of starvation, disease, and war, maintain an uneasy balance against the natural tendency to increase. A serious imbalance arises when man learns how to modify the limiting factors, but allows the natural increase to go unchecked. The essence of the Malthusian Thesis is that the resulting instability is the inevitable order for mankind. The thesis is named after the English political economist Thomas Malthus who first gave it formal expression in MALTHUS (1966). In an age when significant improvements in health, hygiene, and the standard of living were just becoming possible, Malthus aroused great argument when he pointed out that these very improvements could lead to mi sery through overpopulation. The key point of Malthus’s argument is that population increases in what he called geometric ratio, what today we would call an exponential curve, because whenever there is an increase in population there are more people to produce the next generation, so the rate of population growth accelerates - in modelling terms a positive feedback loop. On the other hand, when we increase food production there is no feed-back loop, the best we can hope for is for a constant linear increase. The natural sequence is therefore for food demand to increase faster than food supply, leading at some point to food shortage.

Introduction

India is a second largest producer of fruits after China contributing about 11 per cent to the global production. India leads the world in productivity of papaya, grapes and banana but average productivity of country is much below as compared to other leading countries, which is merely 11.6 t/ha. Abiotic stress is the primary cause of crop loss worldwide, reducing average yields for most major crop plants by more than 50%. Drought and salinity are becoming particularly widespread in many regions, and may cause serious salinization of more than 50% of all arable lands by the year 2050 (Wang, 2003). This leads to a series of morphological, physiological, biochemical and molecular changes that adversely affect plant growth and productivity. There are various factors, which contributes to low productivity like limited availability of quality planting materials, old and senile orchards, poor orchard management practices, insect pest and diseases, poor post-harvest management practices etc. There is a much scope in expansion of horticultural crops in arid region and it has vast potential for changing scenario of horticulture of the country. Land resource, surplus family labors, increasing canal irrigated area, developing infrastructural facilities, plenty of solar and wind energy, etc. are the strength in the region for research and development of arid horticulture. Further, low incidence of diseases/insects in the region provides good scope for production of quality seed and planting material of horticultural crops (Sharma et al., 2013).

Calcareous soils

In arid regions, calcareous soils are developed from limestone or marl parent materials. The content of calcium and magnesium carbonate may be as high as 60 to 70 per cent of the total soil mass. In calcareous soil lime content may vary from a small amount in some places in the profile to appreciable amounts throughout the profile.

Calcium carbonate coatings occur in sand, silt and clay size fractions of calcareous soils. In sub soil, large amounts of lime forms hardpan under high water tables.

In general, pH of the soil increases to alkaline pH with increasing the proportion of water to soil ratio. This may happen in part from a reduction of CO₂ pressure. The dilution of salts in soil solution also permits greater hydrolysis of calcium clay. The dilution of calcium carbonate with production of hydroxyl ions may lead to higher pH as:

Introduction

In classification of irrigation water, it is assumed that the water will be used for average conditions with respect to soil texture, infiltration rate, drainage, quantity of water used, climate and salt tolerance of the crop. Large deviations from the average for one or more of these variables may make it unsafe to use what, under average conditions, would be a good water; or may make it safe to use what, under average conditions, would be a water of doubtful quality. The relationship to average conditions must be kept in mind in connection with the use of any general method for the classification of irrigation water.

However, the quality of irrigation water is evaluated, mainly by the quantity of dissolved salts, proportion of anions, monovalent and divalent cations, sodium adsorption ratio, residual sodium carbonate, potential salinity, specific ion toxicity, proportion of magnesium, calcium in water etc.

6.1. Management of important farm resources

Factors of production are land, labour, machinery, equipments and buildings.

Managing soil organic matter is of paramount importance in sustainable crop production. Human interventions always lead to a decrease in soil organic matter content and biological activity. Therefore, even for maintaining moderate levels of organic matter in soils, sustained efforts should be in place. Such sustained efforts should include adopting high-residue crops in rotation and ensuring regular return of residues to soils.

It is all the more difficult to maintain the organic matter content in well aerated soils and soils of hot and arid regions, because in these soils added materials decompose rapidly. However, in comparison, it may not be so difficult to maintain the organic matter levels in fine-textured soils with restricted aeration and in soils of cold temperate regions.

19.1. Measures that Decrease Soil Organic Matter Content

Introduction

Increasing fresh water demands for ever growing population and it has increased by 600% over the past 100 year (Wada, et. Al, 2016) the annual increment rate 1.8 % whereas the present growth rate is less, only 1 % but this figure may be optimistic. The global water demands will grow significantly over the next two decades in all three components as: Industry, domestic and agriculture (U.N, 2018) industry and domestic demand will grow faster than agriculture, but agriculture demands will remain largest (U.N, 2018). The global water demands for all purpose will increase 20% to 30% by 2050, up to 5,500 to 6,000 km3 per year (Burek et.al, 2016), whereas, agricultural demands will increase by 60% by 2025 (Alexandrators and Bruinsma, 2012).

Introduction

Manures are obtained from the plant residues, green manure, compost and animal wastes that are used as source of plant nutrients. They release nutrients after their decomposition. They essentially function as sustenance to the crops. They help in the proper cultivation of crops. The addition of manure enables the soil to hold more water. Organic manures have acids that dissolve the nutrients in the soil and make them available to the plants. manures can be grouped into bulky organic manures and concentrated organic manures. Brief dissection about manure given blow flow chart.

Bulky Organic Manure

Bulky organic manures are those materials of plant and animal origin, which contain small percentage of nutrients when added to the soil have tendency to decrease bulk density and to increase soil volume, thus providing better physical conditions for plant growth especially in coarse textured soils and also provide essential nutrients in smaller quantities than the chemical fertilizers. Characteristics of bulky organic manures:

The word “Manure” is originated from the French word “Manoeuvrer” which refers to “work with soil”. The word Manure is also originate from Latin word “Manu” (hand) and operate (to work). Manures are the substances which are organic in nature, capable of supplying plant nutrients in available form, bulky in nature having low analytical value and having no definite composition and most of them are obtained from animal and plant waste products. Formerly, the word in use was “Dung ”in English (at present fertilizer).

7.1. Marketing institutions

Business organizations which have come up to operate the marketing machinery.

Individuals, corporate, co-operative and government institutions are operating in agricultural marketing.

4.1. Definition

Kohls and Uhl refer to expansion of firms by consolidating additional marketing functions and activities under a single management.

Establishment of wholesaling facilities by food retailers and setting up of another plant by a milk processor.

2.1. Market structure

Refers to those organizational characteristics of a market which influence nature of competition and pricing, and affect the conduct of business firms.

Those characteristics of market which affect the traders behavior and their performances.

Formal organization of functional activity of a marketing institution.

A knowledge of structure is essential to identify imperfections in performance of a market.

1.1. Concept and definition

1.1.1. Marketing

Process of getting right goods or services or ideas to right people at right place, time, and price using right promotion techniques and utilizing appropriate people to provide customer service associated with those goods, services, or ideas.