Proposal for Establishment of Centre for Artificial

Proposal for Establishment of

Centre for Artificial Intelligence & Machine

Learning in Agriculture

(CAIML)

Taking a lead towards intelligent and customised solutions

for SMART agricultural practices in J & K

Directorate of Planning & Monitoring

SKUAST-Kashmir

Shalimar Srinagar- 190025 [email protected]

0194-2461493, 7780882991

mailto:[email protected]

Centre for Artificial Intelligence & Machine Learning in Agriculture (CAIML)

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Artificial intelligence is like the new electricity and it will transform every sector. NitiAayog CEO Amitabh Kant

NATIONAL STRATEGY FOR

ARTIFICIAL INTELLIGENCE 2018

NITI Aayog


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Preamble

Jammu and Kashmir has missed the first three industrial revolutions, that have shaped the

economies and the prosperity of the societies around the world. The outcome is an economically

fragile and technologically backward state. The first industrial revolution witnessed the

emergence of machines and mechanization, a process that replaced agriculture with industry as the

foundations of the economic structure of society since the 2nd half of 18th century (1765). This

revolution used water and steam to mechanize production. The 2ndindustrial revolution from

1870s used electric and chemical energy to create mass production. The 3rd revolution from 1969

used nuclear energy for production, and electronics and information technology to automate the

production. Besides the biotechnology emerging as the core sector that transformed health and

agriculture. We unfortunately continue to remain as the consumer based society than a contributory

one, and therefore a fragile economy.

The 4thindustrial revolution is unfolding before our eyes. It would be largely driven by artificial

intelligence, machine learning, robotics, 3-D printing, automation and bio-economy. With

exponential expansion, it is characterized by merging technology that blurs the lines between the

physical, digital and biological spheres to completely disrupt the way we think, communicate, act;

and also the entire production, management and governance systems. Envisaging AI as the single

largest technology revolution of our live times, with the potential to disrupt almost all aspects of

human existence, NITI Aayog in its policy document on National Strategy for Artificial

Intelligence in 2018, has identified 5 core areas viz Education, Agriculture, Healthcare, Smart

Cities and Smart Mobility.

The limiting factors of the state of J & K that kept the first three revolutions off its shores, have

become irrelevant in the global village swapped by 4thindustrial revolution, which is rooted in a new

technological phenomenon—digitalization, that enables us to build a new virtual world from which

we can steer the physical world. The Euro 92 billion agri-economy of the Netherlands is driven by

this technology, where the machines are trained to communicate with plants (Tulip) and soil and

weather to predict the most accurate requirements for plant growth in terms of the nutrients and the

pest management, and thereafter linking the processes from farm to fork.

SKUAST-Kashmir can be a path breaker by being bold and disruptive in venturing first in AI and

ML in the state and in the entire NARES. The proposal on establishment of the Center for Artificial

Intelligence and Machine Learning in Agriculture (CAIML) is therefore a strategic move to infuse

higher order skills of AI & ML in students and researchers to assure that we catch the bus of 4th

industrial revolution in right time, and secure and empower our future generations by compensating

the losses of the earlier opportunities.


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Mandate:

1. R &D : Develop AI driven customized Decision support systems for farmers, processors, markets

2. HRD : Develop Next Gen Human Capital equipped with 4th Generation skills who shall drive the technology based agri-economy in J&K

3. Collaborations: Explore and Establish Partnerships with Organizations having propensity with AI,

ML and IoTs in Agriculture.

Taking a lead towards intelligent and

customized solutions for SMART

agricultural practices in J & K V I S I O N

Intelligent IoT based Farm management System for Sheep

Intelligent IoT based Fertigation System for Apple orchards

e-linking farmers with DAH and SKUAST-K for service delivery


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Prelude

The world population by 2050 is projected to increase by over 2.3 billion and only 4% additional

land is expected to come under cultivation by then (FAO, 2009). Therefore, there is a need to double

food production by 2050 by intensifying production on almost the same amount of land (FAO,

2018). This is an enormous challenge since nearly 124 million people across 51 countries already

face acute food shortage. Other issues like the global climate change and dwindling labour force also

add substantially to the problem which our conventional approaches are unable to handle. There is a

need for an out of the box solution which could eventually lead to the 2nd Agricultural Revolution.

The dawn of the 4th Industrial Revolution is already blurring the lines between the physical, digital

and biological spheres and this may prove to be the sine qua non of the next Agricultural Revolution

as well (World Economic Forum, 2016).

This collective revolution would largely be driven by Artificial Intelligence, Machine Learning, Big

Data Analytics, Heuristics, Fuzzy Logic Systems, Automation, Robotics and Bio-Economy, thus

leading to intensification of agriculture and smart interpretation of big data. Intelligent data driven

systems and digital fabrication technologies interacting with the biological world, would make it

possible to adopt new and innovative approaches to deliver food security, economic opportunity and

environmental sustainability.

Problem Statement

Jammu and Kashmir presents tremendous potential for agriculture and allied sectors. Despite this, the

economy of the State is currently import based. Traditional crops and indigenous breeds, the

products of which could fetch a high market price, are dying out slowly due to negligence and a lack

of technological intervention. Residential houses are rapidly erupting over farming areas and people

are moving out of agriculture in hoards. This is because agriculture no longer seems to be a viable

career choice. The agricultural land in the state has shrunk by more than 18,000 hectares in the last

two decades. The total agricultural area in 1998-99 was 878 thousand hectares, which is now 859.52

thousand hectares. To top it all, unprecedented weather patterns often wreak havoc and cause huge

loses to the State economy. Also, unemployment in the State is a major problem. The youth lack

exposure to latest ground breaking arenas of science and therefore graduates are often unemployable


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under the current breakneck competitive environment. Therefore, it may be said that the two most

powerful resources for any society viz. its youth and its agriculture, are underperforming.

Global Trends in Agriculture Using AI and Machine Learning

The emergence of new age technologies like Artificial Intelligence (AI), Cloud Machine Learning,

Satellite Imagery and advanced analytics is creating a global ecosystem for Smart or Precision

Farming. Such cognitive solutions are benefiting agriculture in a myriad of ways. Data on historical

weather pattern, soil reports, new research, rainfall, pest infestation, images from Drones and

cameras etc. is sensed by Cognitive IOT solutions to provide strong insights to improve yield by in-

depth field analysis, crop monitoring, scanning of fields, managing adverse weather conditions, crop

health management, and yield management.Drones are now being used to produce a 3-D field map of

detailed terrain, drainage, soil viability and aerial spraying of pods with seeds and plant nutrients into

the soil provides necessary supplements for plants.

Proximity Sensing, Hyper-spectral Imaging, 3d Laser Scanning and Remote Sensing are two

technologies are finding their use in soil testing, soil characterization and for building crop metrics

across thousands of acres. Hardware solutions pairing data-collecting software with robotics can

automate irrigation and prepare best fertilizers, herbicides etc. in addition to other activities to

maximize output while optimizing input. Self-driving tractors and combine harvesters trundle round

fields without human involvement.

Computer vision technology, IOT and drone data can be combined to ensure rapid futuristic actions

by farmers. Pre-processing of images is being used in pest identification, nutrient deficiency

recognition and disease diagnosis. Images of different crops under white/UV-A light are captured to

determine how ripe the green fruits are. Farmers can create different levels of readiness based on the

crop/fruit category and add them into separate stacks before sending them to the market.

Based on multiple parameters, cognitive solutions make personalized recommendations to farmers

on the best choice of crops and hybrid seeds. This technology also helps to monitor crops along their

entire lifecycle including report generation in case of anomalies.

Neural network-backed computer vision, wearable devices, and predictive analytics algorithms are

helping scientists reimagine animal farming too. Animal identification and monitoring is now

possible using intelligent integral control systems. Tags, implantable transmitters and receivers for

low level signals from animals by telemetry are now commonly used (Puers et al., 1988). AI is also


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making it possible to analyse animal behaviour for animal health management, pregnancy diagnosis,

heat identification and early disease diagnosis. Intelligent animal identification systems identifying

animals automatically from their sounds (Chesmore, 2001) have also been developed. Robots are

now being used for scientific feed management, auto drafting, auto floor cleaning, high-tech milking

and robots to diagnose disease and administer remedies/cures to the affected animal (Ishaq, 2018).

AI is also being used for the prediction of the genetic merit and futuristic projections of the animal’s

production potential (Ghazanfari et al, 2011). Machine learning algorithms are also actively being

used for gnomic selection of animals. Data-centric farming powered by AI algorithms collect and

analyses massive farm data, regarding humidity, temperature, and food requirements.

All these technologies are producing a giant leap in production and this may help farmers produce 70

percent more food by 2050 (Jones, 2017). It may thus be safely concluded that AI promises a food

revolution, from farm to supermarket.


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Primary Objectives of the Centre

• Make the study of AI and ML an integral part of agricultural education curriculum at all

levels.

• Creation of an education, research and extension hub at SKUAST-K to lay the foundation of

AI and ML programs and initiatives in the State.

• Attract top AI& ML education, research and entrepreneurial talent to the University for

improvement of Agriculture.

• Develop organic home-grown personalized AI solutions for temperate agriculture.

• Promote the use of AI& ML as part of an integrative solution for skill development,

upgradation of living-standards and agricultural transformation in the State and

consequently in the entire country.

Immediate/ Short Term Applications

The primary goal of the University is promotion of agriculture in all forms to improve food security

and transform lives. Therefore, the establishment of an Artificial Intelligence cum Machine Learning

Center would be strengthening all primary mandates of the University viz. education, research and

extension. It would be of both long and short term benefit. The immediate implications of the

AI&MLCenter would include the following:

• Provide support to all research activities at the University and increase technology transfer

ability towards major stakeholders (farmers, entrepreneurs etc.)

Keeping all this in view, the establishment of the Centre for Artificial

Intelligence (AI) & Machine Learning(ML) in Agriculture at SKUAST-

Kashmir (CAIML) is proposed, to build and sustain AI-based,

innovation, growth and productivity in Agriculture and Allied Sectors.


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• Collaboration with internationally renounced institutes (MIT, IISc and IITs, etc) and

industries (Microsoft, Google, Amazon etc) through MOUsthereby opening up new

opportunities to the enrolled students and youth in general.

• Development and testing of cutting-edge tools and technologies to coordinate and monitor the

work force activities across diverse verticals(keeping the unique conditions and challenges of

temperate agriculture in view).

• Creation of Early Warning Systems for weather changes, epidemicsetc.

• Use of Farm Management Information Systems for planning, capture of thousands of farm

variables and recording of the workforce activities across diverse fields

• Creation of separate, centralized databases for crops, fruits and animals for the State.

• Use Automatic Learning Systems like Artificial Neural Networks for data analysis for

personalized farm projections and advisories.

• Modification of available technologiesto better suit the temperate agro climatic conditions.

• Enhance production per plant though micro-climatic data, soil condition data and real-time

controls for irrigation, seed behavior to make it conducive for a better crop growth for sustainable

agriculture.

• Upgrade traditional farming equipment (tractors, tillers, milking machines etc.) for

automatizing mundane tasks while improving precision and minimizing losses.

• Utilize smart sensors and software to monitor diseases and pest and also to track and trace a

perishable’s condition for supervision of the supply chain to minimize spoilage.

• Create an online intelligent, integrated demand-supply based market place with customized

projections regarding the rise or fall of demand in the future

• Accurate identification of animals and tracking and monitoringof feed intake, growth,

pregnancy diagnosis, heath through readily available devices and sensors like RFID tags,

pedometers, smart cameras etc.

• Automatic cloud-based data capture and analysis through automatic milking machines, smart

on-body devices, automatic weighing and drafting systems for animal farms.

• Providing better farming conditions and controls through the application of Internet of Things

(IoT) in animal farms for temperature control, humidity condition sensors, automated feeders,

waters and automated milking machines (cows) in animal and poultry houses.

• Integrate the breeding process with modern genomic tools to organize the hybridization of

desirable characteristics in the crop. Utilize bioinformatics and AI for making superior breeding


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decisions both for animals and plants. Coordinate plant and animal breeding process from pre-

breeding to commercialization phase.

• Boost conservation efforts for domestic and wildlife species especially Hangul (the State

Animal) through aerial monitoring systems, real-time sensor technologies etc.

• Creation of an innovation driven ecosystem by the provision of state of the art IoT labs and

facilities for leveraging start-ups, enterprises, institution and individuals for testing and

developing the technology solutions.


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IOT/ AI in Course Curriculum

Program Courses to be Offered Duration

UG Courses

(for BSc/ BVSc & AH/

BFSc/ BTech)

• Update the Course on “ Biostatistics and

Computer Science 3+1” with chapters on AI

and ML, The revised structure could be: o Elementary Statistics.

o Concepts and Techniques of Data Science /

Data Mining

o Introduction to AI and ML techniques

1 Semester

Elective and Certificate

Courses

(for UG, PG, PhD

Outside students)

• Data Science in Agriculture

• IoT and Cloud Computing

• Crop and animal Modelling

• Automation and robotics in agri-operations

• Machine Learning

• Block Chain Technology

1 month, 2 month,

and 3 months

PG Course

(forMSc/ MVSc/ MFSc/

MTech)

Machine Learning and Artificial Intelligence and

its Applications in Agriculture 1 Semester

Diploma (AI and

Machine Learning)

Post Graduate Diploma in Machine Learning and

Artificial Intelligence in Agriculture 1 Year


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Course Syllabus

Data Science Tool Kit Python: Basics Data Types, Conditional Statements, Looping, Control

Statements,String, List and Dictionary Manipulations, Python

Functions, Modules and Packages, Object Oriented Programming in

Python,Regular Expressions, Exception Handling.

Introduction to Database Management System & SQL, Database

Interaction in Python.

Data Analysis & visualization – using numpy, matplotlib, scipy

R Programming: Basics - Vectors, Factors, Lists, Matrices, Arrays, Data

Frames, Reading data.

Data visualization - barplot, pie, scatterplot, histogram, scatter matrix

Statistics and

Exploratory Data

Analytics

Analytics problem solving, Inferential statistics, Hypothesis testing,

Exploratory data analysis, Case study

Machine Learning Supervised and Unsupervised Learning, Classification and Regression,

Linear Regression, KNN, K Means, Logistic Regression, Support

Vector Machines (SVM), Decision Tree, Naïve Bayes, Ensemble

Methods, Random Forest, Boosting and Optimization

Natural Language

Processing

Basics of text processing, Lexical processing, Syntax and Semantics,

Other problems in text analytics

Deep Learning Deep Learning Concepts, Basics of Artificial Neural Network, Deep

Neural Networks, Convolutional Neural Network (CNN), Recurrent

Neural Network (RNN), Tensorflow, Keras, Introduction to Generative

Adversarial Networks(GAN),OpenCV

Reinforcement

Learning

Introduction to AI/Cognitive platforms and Understand the basics of

Reinforcement Learning and its applications in AI

Graphical Model Introduction to Bayesian Methods, Graphical Methods, Learning and

Inference

Computational Biology sequencing and assembling of the genomes of the niche crops like

saffron, kala zera, Mushkbudgi, Red rice varieties, Ambri apple, Sea

Buck thorn, Apricot, Pashmina goat, Bakerwal goat, local poultry, and

end number of the medicinal and aromatic plants. This shall include

gene expression and regulation •DNA, RNA, and protein sequence,

structure, and interactions • molecular evolution • protein design •

network and systems biology • cell and tissue form and function •

disease gene mapping • machine learning • quantitative and analytical

modeling

Mini Project


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Staff Requirements

Area Faculty Position Expertise

Machine Learning

(Machine learning (ML) is a category of

algorithm that allows software

applications to become more accurate in

predicting outcomes without being

explicitly programmed)

Application: yield prediction, disease

detection, weed detection crop quality,

and species recognition; animal welfare

and livestock production)

Assistant Professor

(Computer Science)

Expertise in the following domains and

teach the relevant courses in the same.

a) Machine Learning

b)Computer Vision

c)Natural Language Processing

d) Deep Learning

e) Reinforcement Learning

Expertise in following tools/ languages

a) Programming Languages like C,

C++, Python, JAVA

b) Tools like MATLAB/ OCTAVE/

PYTORCH/ TENSOR FLOW

Data Optimization / Deep Learning

(Data Optimization is a process that

prepares the logical schema from the data

view schema. involves maximizing the

speed and efficiency with which data is

retrieved)

Assistant Professor

(Computer Science)

Expertise in the following domains.

a) Linear Optimization, Convex

Optimization, Integer Optimization

b) Optimization problems and Algorithms

c) Improving Deep Neural Networks

d)Multi objective Optimization Problems

and Algorithms.

e) Bayesian Statistics

Further expected to teach the

mathematical courses like:

a)Statistics and Exploratory Data

Analytics

b) Applied Mathematics

c) Operational Research etc.

Expertise on implementing the algorithms

in the following tools:

MATLAB/ OCTAVE

Lab Courses like

Optimization with Metaheuristics in

Python, Improving algorithms and

implementation.

IoT

(Internet of Things (IoT) is an ecosystem

of connected physical objects that are

accessible through the internet.)

Collar tag sensor deliver temperature,

health, activity, and nutrition insights on

each individual cow as well as collective

information about the herd.

Assistant Professor

(E&C)

Expertise in the following domains and

teach courses like:

a)Programming the Internet of things

b)Wireless and cloud Emerging

Technologies

c)Architecting smart Devices

d)Fog Computing

e)Data Analytics

Expertise in following tools/ languages

a) Programming Languages like C,

C++, Python, JAVA,

b) Tools like MATLAB/ OCTAVE/

PYTORCH/ TENSOR FLOW

Big data/

Computational biology/

Data Analytics

Assistant Professor

Expertise in

Computer programming

Bioinformatics and computational biology

Algorithm development


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(Big data analytics examines large

amounts of data to uncover hidden

patterns, correlations and other insights.)

Big Data and analytics enable monitoring

and supervision for growth rate and

nutrient requirements on a plant-by-plant

basis.

Pipeline development for managing and

analysis of big data

Database development and applied

statistics

Automation (Automation is the creation of technology

and its application in order to control and

monitor the production and delivery of

various goods and services.)

Remote-piloted Drones that can spray

fungicide, Harvesting fruit and vegetable

robots

Assistant Professor

(Mechatronics)

Expertise in

Mechanics

Mechatronics

Automation

Designing and manufacturing of

machines/ devices

Subject Expert

Remote Sensing & GIS

(Technique of obtaining geographical

informationof any part of surface(land)

from space using Satellite Technology,

drones, aircrafts, etc. and processing of

data into useful information by using

computer-based tools.)

Fire detection, flood prediction, soil

properties prediction.

Assistant Professor

(RS & GIS)

Expertise in image processing, remote

sensing, pattern recognition, gig Geo-data

processing, and machine learning

Crop and Animal Modeling

1. Simulation modelling describes

processes of crop growth and

development as a function of weather

conditions, soil conditions, and crop

management.

2. Animal Modeling is the use of

advanced biometrical tools like

BLUP technology for predicting the

true genetic worth, alongwith the

algorithm writing languages like R

and Python

Assistant Professor

(Agronomy/Soil

Science/ Environmental

Science)

Assistan-*t Professor

Animal Breeding &

Genetics

Expertise in

Data and input file preparation for crop

models, assessing crop growth and yield

using crop simulation models (InfoCrop,

DSSAT and other models): Experiment

setup and running simulations, simulating

climate impacts, simulating adaptation

strategies and interpretation, Soil carbon

balance model, Pest and disease

modelling, Simulating GHG mitigation

options, Yield forecast using simulation

models


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Staff Rationalisation

Area Faculty Position No of

positions

Availability through

Rationalization / up-grading

Machine Learning

Asstt Prof

(Computer Science )

1

Upgradation of 1 post of STA

( Statistics)

Data Optimization /

Deep Learning

Asstt Prof

(Computer Science ) 1 Upgradation of 1 post of computer

programer

IoT Asstt Prof

E & C engineering

1 Upgradation of 1 post of STA

Automation

(Drones / robotics)

Asstt Prof

Mechatronics

1 Upgradation of 1 post of STA

Data Analytics /

Computational

biology

Asstt Prof

Computational Biology

2 Rationalization: Asstt professor

Plant Genetics & Breeding

Agri-Statistics

Remote Sensing and

GIS

Asstt Prof

RS & GIS 1 Rationalization: Asstt professor

Soil Science

Crop and Animal

Modeling

Asstt Prof

Modeling 2 Rationalization: Asstt professor

Agronomy

Animal Genetics & Breeding

Supporting Staff

Position No Availability through

Rationalization

Stenographer 1 out of 72 available posts

ACT 1 out of 145 available posts

Accounts Asstt 1 out of 38 available posts

Lab Astt 2 out of 221 FCLA available posts

Computer Assts 2 out of 12 available posts

OCC 2 out of 301 available posts


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Literature Cited

Chesmore E.D. Application of time domain signal coding and artificial neural networks to passive

acoustical identification of animals. Applied Acoustics 62 (2001) 1359–1374

FAO. 2018. Food Security & Nutrition around the World. http://www.fao.org/state-of-food-

security-nutrition/en/

FAO. 2009. Global agriculture towards 2050.How to Feed the World 2050; High Level Expert

Forum. Rome, 12-13 Oct, 2009.

http://www.fao.org/fileadmin/templates/wsfs/docs/Issues_papers/HLEF2050_Global_Agriculture.pdf

Ghazanfari S, Nobari K and Tahmoorespur M. 2011. Prediction of egg production using artificial

neural network. Iranian Journal of Animal Science 1: 11–16.

ICAR. 2015. Vision 2050. Indian Council of Agricultural Research, New Delhi, India.

Ishaq M. A. 2018. Artificial Intelligence in animal health and veterinary sciences. vHive.

https://vhive.buzz/artificial-intelligence-animal-veterinary-sciences/

Jones C.O. 2017. AI promises a food revolution, from farm to supermarket. Raconteur.

https://www.raconteur.net/sustainability/ai-promises-a-food-revolution-from-farm-to-

supermarket

NITI Aayog. 2018. Discussion Paper National Strategy for Artificial Intelligence. NITI Aayog, New

Delhi. India

Puers, B, Michiels, L., Sansen, W., Geers, R., Goedseels, V. 1988.Temperature telemetry for

domestic animals. 10th International Symposium on Biotelemetry, Jury 31 - August 5,

Fayetteville.

World Economic Forum. 2016. The Fourth Industrial Revolution: what it means, how to respond.

https://www.weforum.org/agenda/2016/01/the-fourth-industrial-revolution-what-it-means-

and-how-to-respond/

http://www.fao.org/fileadmin/templates/wsfs/docs/Issues_papers/HLEF2050_Global_Agriculture.pdf

https://vhive.buzz/artificial-intelligence-animal-veterinary-sciences/

https://www.raconteur.net/sustainability/ai-promises-a-food-revolution-from-farm-to-supermarket

https://www.raconteur.net/sustainability/ai-promises-a-food-revolution-from-farm-to-supermarket

https://www.weforum.org/agenda/2016/01/the-fourth-industrial-revolution-what-it-means-and-how-to-respond/

https://www.weforum.org/agenda/2016/01/the-fourth-industrial-revolution-what-it-means-and-how-to-respond/


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ARTIFICIAL INTELLIGENCE IN AGRICULTURE

Introduction

The world of Artificial Intelligence (AI) is quickly on the rise as it makes its way into many different

industries. From manufacturing to automotive, we can likely see AI used for many different purposes;

and as time goes on, we will only see it even more. One of the most interesting industries that AI is

breaking into is agriculture. Agriculture is a major industry and a huge part of the foundation of the

economy of various countries especially ours. In India, around 60–70% of population (directly or

indirectly) depends upon Agriculture sector and currently it contributes to 16–17% of the GDP. The

productivity in Indian Agriculture sector has increased over the years but the Growth in productivity

is still slower than Manufacturing/Services. On global level also, our productivity is much lower in

Agriculture sector because of which is reflected in 50% work force contributing only around 17% of

GDP. This is largely because of the fact that farmers still stick to traditional ways which leads to

lesser productivity. The traditional ways need to be replaced by a more efficient and stable way of

farming and one of the ways to do so is the introduction of AI.

What is AI?

Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software

think intelligently, in the similar manner the intelligent humans think.AI is accomplished by studying

how human brain thinks and how humans learn, decide, and work while trying to solve a problem,

and then using the outcomes of this study as a basis of developing intelligent software and systems.

The philosophy of AI is to make the machines do what humans door in other words Artificial

Intelligence pursues creating the computers or machines as intelligent as human beings.

Goals of AI

i) To Create Expert Systems -The systems which exhibit intelligent behavior, learn,

demonstrate, explain, and advice its users.

ii) To Implement Human Intelligence in Machines- Creating systems that understand, think,

learn, and behave like humans.

AI in Agriculture

i) Agricultural Robots

Companies are developing and programming autonomous robots to handle essential agricultural tasks

such as harvesting crops at a higher volume and faster pace than human laborers. AI companies are

focusing much of their efforts on developing autonomous robots that can easily handle multiple

agricultural tasks. These robots are capable of harvesting crops at a much faster pace and higher

volume than human workers. The robots are designed to assist in picking and packing crops while

also combating other challenges within the agricultural labor force. Additionally, agricultural robots


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have the ability to protect crops from harmful weeds that may be resistant to herbicide chemicals that

are meant to eliminate them.

ii) Image-based Insights for Crop

Many farms are taking advantage of drone technology to provide high-quality imaging that can help

monitor crops while scanning and analyzing fields to collect necessary agricultural data. This imaging

technology can also assist in the identification of crops and their progress, including their health, and

the determination of their readiness. For example, these images can provide farmers with the ability to

determine how ripe their crops are, and if and when they will be ready for harvest. Additionally,

imaging technology can assist with overall field management, providing estimates in real-time

identifying where specific crops may require more water, fertilizer, soil or pesticides. Machine

learning is also used to provide an analysis of crop or soil health. Innovative AI and machine learning

companies have developed technologies that use machine learning to provide farmers and laborers

with insights about the strengths and weaknesses of their soil. This is done with the intention of

preventing and eliminating bad crops and increasing the potential for healthy crops to grow.

iii) Precision Farming

Precision farming uses AI to generate accurate and controlled techniques that help provide guidance

and understanding about water and nutrient management, optimal harvesting and planting times as

well as when the right times for crop rotation would be. These processes make farming more efficient,

and can even help predict ROI on specific crops based on their costs and margin within the market.

https://www.mindtree.com/sites/default/files/2018-04/Artificial%20Intelligence%20in%20Agriculture.pdf


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iv) Sowing the Seeds

High-tech agriculture starts at the very second that the seed is first placed in the ground. Experts in

the field are familiar with “variable rate planting equipment” that does more than just planting a seed

down into the dirt somewhere.

All sorts of artificial intelligence work is being done behind the scenes on predictions – where a seed

will grow best, what soil conditions are likely to be, etc. The power of artificial intelligence is being

applied to agricultural big data in order to make farming much more efficient – and that's only the

beginning.

v) Pest and Weed Control

Farmers are quickly adopting new high-tech ways of protecting plants against weeds and various

kinds of pests outdoors. Another alternative is to grow in greenhouses, which is being done as well,

but some of the most amazing farming technology is being deployed outside. Airborne surveillance

engines can look for stunted crops, signs of pest or weed damage, dryness and many other variables

that are part of the difficulty of farming in general. With all of this data in hand, farmers can enhance

their production models and their strategies across the lay of the land to decrease risk, waste and

liability.The “See and Spray” model acquired by John Deere recently is an excellent example of

harnessing the power of artificial intelligence and computer vision.

Further, researchers have developed an artificial intelligence (AI) capable of identifying diseases in

plants; specifically, the cassava plant, which is the most widely grown root on the planet and a decent

source of carbohydrates. In their research published to arXiv, the team explains how they used a

technique known as transfer learning to teach the AI how to recognize crop diseases and pest damage. As

reported by Wired, they utilized TensorFlow, Google’s open source library, to build and train a neural

network of their own, which involved showing the AI 2,756 images of cassava leaves from plants in

Tanzania. Their efforts were a success, as the AI was able to correctly identify brown leaf spot disease

with 98 percent accuracy. Best of all, the AI can be easily loaded onto a smartphone, and operate without

accessing the cloud.

https://www.techopedia.com/definition/27745/big-data

https://arxiv.org/abs/1707.03717

https://www.wired.com/story/plant-ai/




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vi) Yield Boosting Algorithms

When we talk about machine learning and artificial intelligence, we often talk about algorithms. The

mathematical models behind the computer science are the fundamental basis for how we deal with

big data to make decisions.

Companies are now quickly developing agricultural yield boosting algorithms that can show farmers

what's going to be best for a crop. Despite some concerns about the difficulty of doing this type of

analysis in nature, farmers and others have been able to make quite a lot of headway in maximizing

crop yield, simply by applying the algorithms and intelligence generators that we've built to help

computers imitate our own cognitive abilities.


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Likely impact of AI on different areas of Agriculture

Agriculture and Artificial intelligence is a growing collaboration and will highly optimize the

way the things are conventionally done in the field of agriculture. There is immense potential of

this collaboration between the two fields. Some of the likely impact of AI on agriculture can be

best realized by analyzing how AI based systems can be applied to optimize the processes in the

field of Agriculture. Some of these are included as:

1. Using GIS (Geographic Information System) based visual precision agriculture to use

agricultural pesticides and fertilizers more efficiently

2. Using neural networks for improving agricultural processing output (analyzing and

predicting mechanical parameters)

3. Using CAD (Computer Aided Design) for improving agricultural equipment design and

performance

4. Using hybrid network to build information sharing platforms (e-governance, climate,

agricultural practices, expert pools, etc.)

5. Using laser induced imaging for detection of contamination of agricultural products

6. Using sequence alignment in molecular biology to improve crop quality

7. Developing e-commerce (seed sales, produce sale, prices info, etc.) platforms to boost

agricultural economy

8. Working towards improvement in low altitude image acquisition systems

9. Optimization of irrigation models using genetic algorithms

10. Developing design for automated sorting systems of farm produce

11. Building GIS and MCE (Multi-Criteria Evaluation) mapping for land quality evaluation

12. Using Support Vector Machine (SVM) technique for soil water content forecasting

13. Using 3S (Remote Sensing, GPS, and GIS) techniques for predicting landforms and

climatic changes

14. Building simulation algorithms for optimization of pesticides and fertilizers spray methods

15. Helping development of sustainable wireless monitoring systems (long-range, automatic,

etc.)

16. Developing spatial distribution maps of soil nutrients

17. Developing geospatial computation grid for digital agricultural experiments

18. Creating crop disease leaf image segmentation using imaging analysis techniques


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Major sub disciplines of AI and Agriculture

1. Computer vision

2. Image Processing

3. IoT

4. Robotics

5. Computational biology

6. Machine Learning

7. Data mining

8. Data science

9. IT for Agricultural systems

10. Pattern Classification

11. System Biology

12. Bioinformatics

13. Computer Programming

14. Applied Mathematics

15. Algorithms

Documents

Proposal for Establishment of Centre for Artificial