With this presentation at RVSKVV, SBSF Consultancy introduced concept and applications of Artificial Intelligence and Internet-of-Things to support Agriculture and enhance sustainability. Practical use cases were discussed. Video: https://youtu.be/lZvjGZDhy7o

1. 1
Data can feed the World!
But do we have the right data?
Data, Artificial Intelligence & Internet of Things at the service of Agriculture
https://sbsf-consultancy.com
Faculty of Agriculture, RVSKVV, Gwalior
20/07/2020

2. • Science, Business & Sustainable
Futures
• Old and new challenges for Agriculture
• How data can support Agriculture?
• What is Artificial Intelligence?
• Data collection is the pain point!
• Careers in Digital Farming and
Precision Agriculture
2
Outline
Rajarshi MITRA

3. 3
Speakers
Chief Data Officer
HRS Group, Germany
Data Science & Data Strategy
Machine-Learning & Artificial Intelligence
Product & Software development
Dr. Sébastien FoucaudDr. Shravani Basu
Partner
SBSF Consultancy, Germany
Agri- & Bio-Technology
Business Strategy & Development

4. 4
Science, Business
& Sustainable Futures
Data & Artificial Intelligence
at the service of Agriculture & Agribusiness
https://sbsf-consultancy.com

5. At SBSF Consultancy we support all aspects of
Agriculture:
• Agricultural Production across various production
systems and crops, both conventional and organic;
• Crop Monitoring, Evaluation, Quality Management,
and implementation of Organic Adoption and
Certification;
• Agricultural Business, Market Development and
Financing;
• Food Policy, Regulations, and Compliance;
• All in multiple markets globally;
• And with a data-driven approach.
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A passion for Agriculture
U.S. Department of Agriculture

6. At SBSF Consultancy we understand the value of
Data!
Every company can benefit from access to vast
amounts of publicly available data and the development
in the Internet-of-Things domain, provided they
understand the actual value it brings to the Business.
At SBSF Consultancy we support companies
worldwide, particularly in the Agricultural sector in
developing:
• Sound Business Strategy & Planning based on Data
Science;
• Project developments based on Artificial Intelligence
(Machine-Learning and Data Engineering).
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Data Science and Artificial Intelligence
Javali Digital

7. SBSF Consultancy helps its clients by
providing the best scientific and business
expertise in Agriculture, Biotech, Food,
Data Science and Artificial Intelligence.
Our consultancy is based on a network of
renowned experts from diverse scientific &
technological fields, and based in India and
abroad. Our network can be extended to the
need of our clients, which allows us to
ensure that we provide the right expertise
with the highest degree of knowledge.
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A Professional Group of experts
U.S. Department of Agriculture

8. 8
Partners
Managing Director
Agricultural Research
Crop Production Systems
Agribusiness Development
Dr. Mukti Sadhan Basu
Partner
Agri- & Bio-Technology
Business Strategy & Development
Dr. Shravani Basu

9. Formerly held positions:
• Executive Director, URVARA Agro Biotech,
New Delhi
• Sr. Advisor (Seed) National Cooperative
Federation of India, New Delhi
• Consultant (Seed),
Hindustan Insecticides Ltd. Govt. of India
Enterprise, New Delhi
• Independent Consultant (Business Planning
& Development) NAIP-ICAR New Delhi
• Vice President (Product Research &
Development), KSL, Maharashtra
• UNIDO International Consultant on
Aflatoxin, Malawi, Africa
• Visiting Scientist, ICRISAT (CGIAR)
Hyderabad
• Director, National Research Centre for
Groundnut (ICAR), Gujarat
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Dr. Mukti Sadhan Basu, PhD
Crop Expertise:
• Groundnut , Soybean – as sole crop in
rainfed and irrigated production system
• Rice, Wheat – in major sequential cropping
system
• Green Gram, Black Gram, Chickpea – as
relay cropping
• Castor, Cotton, Pigeonpea, Sunflower,
Soybean–as intercrop in rainfed
• Maize, Sorghum, Spices – through network
project
Seed Production Experience:
• Large-scale seed production of high
volume crops
• Open pollinated crops (Groundnut,
Soybean, Chickpea, Pigeonpea, Wheat,
etc.) and Hybrids (Maize, Rice, Millets)
• Using both conventional and sterility
systems
Research Collaborations:
• Bhava Atomic Research Centre (BARC), Mumbai
• International Crop Research Instt. for Semi-Arid Tropics (ICRISAT),
India
• Asian Vegetable Research & Development Centre (AVRDC), Taiwan
• Dept. of Crop Physiology, University of Agril. Sciences (UAS),
Bangalore
• Central Agricultural Universities for NEH States, Manipur
• State Agricultural Universities (SAUs):
• Acharaya NG Ranga Agril. University (ANGRAU), Andhra Pradesh
• Mahatma Phule Krishi Vidyapith (MPKVV), Maharashtra
• Rajasthan Agril. University (RAU), Bikaner
• Maharana Pratap Univ. of Agril. & Tech. (MPAU&T), Rajasthan
• Punjab Agricultural University, Ludhiana
• Tamil Nadu Agril. University (TNAU), Coimbatore
• Gujarat Agril. University (GAU), Junagadh
• Orissa University of agriculture & Technology (OUA&T), Bhubaneswar
• Assam agricultural University (AAU), Jorhat
• Bidhan Chandra Krishi Viswa Vidyalaya (BCKVV), Mohanpur
• Birsa Agricultural University (BAU), Ranchi, Jharkhand
• ICAR Research Complex for North Eastern Hill Regions - Arunachal,
Manipur, Meghalaya, Mizoram, Nagaland, Sikkim and Tripura
• Indian Institute of Pulses Research
• Indian Institute of Vegetables Research (IIVR), Varanasi, Uttar Pradesh
• Indian Institute of Spices Research (IISR), Calicut, Kerala
• Central Rice Research Institute (CRRI), Cuttack, Orissa
• Directorate of Rice Research (DRR), Hyderabad, Andhra Pradesh
• Directorate of Maize Research (DMR), Pusa, New Delhi
• National Research Centre for Soybean, Indore, Madhya Pradesh
https://www.linkedin.com/in/mukti-sadhan-basu-ph-d-30769459

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Dr. Shravani Basu, PhD, MBA
Business, Strategy & Marketing:
• Broad expertise across many different industries: agriculture, pharmaceuticals, fine chemicals, financial technology and connected devices;
• Marketing, strategy for business development around existing products, market & customer targeting, strategic partnership;
• Identifying new products/projects for future growth (new business line) either standalone or part of ecosystem;
• Revenue modeling, forecasting, along with P&L responsibilities of several million USD.
Biotech and Agriculture:
• Experience in seed and commodities business (field and vegetable seed crops along with black pepper, coffee Arabica, ginger, turmeric);
• Research experience on conservation, plant genetic improvement and sustainable product development in indigenous food crops;
• Built and analyzed large scale genomic libraries, database mining, gene/QTL mapping using phenotypic and genotypic data through SaaS.
Data Strategy:
• Build with clients’ successful use cases demonstrating the power and lasting impact of a well formulated, business driven data strategy on
performance, growth, profitability and sustainability of business operations.
• Road-mapping MVPs for data driven products and solutions that are practical and scalable.
Clients comprise: Urvara Agro Biotech Pvt. Ltd. (India), Jiangsu Jiaerke Pharmaceutical Group Corp. Ltd. (China), Suzhou Kaiyuan Minsheng
Sci&Tech Corp. Ltd. (China), IconMobile GmbH (Germany), CrossLend GmbH (Germany), BASF Crop Sciences (Germany), Shanghai Shyndec
Pharmaceutical Co. Ltd. (China), Pfizer (United Kingdom), Formosa Laboratories Inc. (Taiwan), certace (Germany), Naukri.com (India), InfoJobs
(Spain), JobCloud (Switzerland), among others.
https://www.linkedin.com/in/shravanibasu

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Network of professionals
Manan Arora
Analytics & Data Science
Dr. Manojit Basu
Food, Crop and Regulatory
Ángel de Jaén Gotarredona
Data Science
Dr. Sébastien Foucaud
Data & AI Strategy
Jonathan Greve
Machine Learning & AI
Dr. Asitava Basu
Biochemistry & Plant Biotech
Dr. Mrinmoy Datta
Soil Science
Mallikarjun Kukunuri
AgTech
Devendra Kumar
Agricultural Finance
Dr. Bishwanath Mazumdar
BioTech & Bio-safety
Dr. Ashok Mishra
Plant Virology
Dr K. S. Murthy
Animal Science
Mukesh Varma
Organic Agribusiness
Dr S. K. Naskar
Plant Science
Dr. T. P. Rajendran
Crop Protection
Dr. Y. S. Ramakrishna
Agriculture Meteorology
Dr. K. K. Satapathy
Land & Water Management
Florents Tselai
Data Science & Engineering

12. Old and new
challenges for
Agriculture
Food insecurity puts millions
at risk of starvation
while farmers face existential risk
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13. Fragmented land
and small holdings
(difficult to mechanize)
13
Old challenges
Subsistence farming
with little or no crop
rotation
Lack of
post harvest storage
and processing facilities
High input use
& soil deterioration
Ballooning
cultivation costs and
extreme price volatility
Predatory financing

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New challenges
Shifting climate pattern
Flood or water scarcity
Trade & geopolitical instability
New and known
pest & disease outbreaks
Intensive mechanization and
chemistry solutions backfiring
(resistance build-ups, irreversible change to the soil profile, etc.)

15. Digital Agriculture &
Precision Farming, powered
with advanced Analytics
and Data Science seems
to hold much promise in
building sustainable
systems with reduced
environmental impact.
15
Data is key for the future of Agriculture

16. How data can
support
Agriculture?
Some use cases tackled by
SBSF Consultancy
16

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Pest outbreak prediction
Goals:
• Shed light on the conditions favouring the spread of FAWs by combining soil (HWSD), weather (FLDAS) and past pest
outbreak (FAMEWS) datasets.
• Using complementary information predict the potential outbreaks of FAW.
Approach:
• Two different methods of inspection (Scouting and Pheromone traps) have been used in the FAMEWS dataset. To remove
unforeseen biases we limited the study to samples detected using Scouting as it represents the largest sample.
• Extreme Gradient Boosting (XGBoost) was optimised to predict, as a target variable, the percentage of plants infested by
FAW at a given inspection point.
Impact:
• A defined set of important features (14 features) were identified, which can be used to better understand the drivers
behind the spread of FAW.
• Besides providing actionable insights, the study demonstrates the significance of taking a data science based approach
to use various sources of information, beyond the scope of restricted surveys, to support the development of
comprehensive and result oriented agricultural projects.
Fall Armyworms has a complex life cycle, depending on crop development,
challenging traditional ways of forecasting.

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Pest outbreak prediction
https://towardsdatascience.com/critical-factors-contributing-to-the-spread-of-fall-armyworms-from-a-data-science-perspective-a1fec38cff32

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Landscouting for Ideal Crop Growing Regions
Goals:
• Looking holistically at supply chains to identify which product categories and geographies represent the greatest opportunity for
developing new or adaptive capacity, while also meeting carbon emission targets;
• Identify new production sites that are less sensitive to shifts in ecological systems, especially in the face of climate change, to
ensure interrupted supply of crop based inputs/raw materials for the processor.
Approach:
• Mining vast amounts of existing crop, soil and climatic data, and analysing new, non-experimental data to develop new
production target areas of select crops and make it more resilient to climatic change.
Impact:
• Identified agricultural lands presenting similar characteristics, by gathering data that globally maps the climate (historical
data and forecasts, satellite imaging, etc.) and soil compositions (limitations: coverage, granularity), and using
unsupervised machine learning techniques (clustering, kNN) to identify similar zones (surface of land plot, etc.);
• Use GPS coordinates of land, processing facilities, end-user locations, modes of transportation, and other parameters to
evaluate Carbon footprint;
• Predict yield of given crops (or more broadly species) for each cluster of land area (provided yield is known for some of
the cluster members).
Soil, climate & plant passport mapping to identify suitable alternate agro-ecosystems
for crops that are at risk.

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Landscouting for Ideal Crop Growing Regions
Climate Data
Land data
(soil, geography)
Crop statistics
(yield)
Land utilisation
type &
Cultural
practices
Yield prediction Land scouting
Model principles Map generated from input data

21. `
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Landscouting for Ideal Crop Growing Regions
7.0<pH<7.5
21℃<T<44.5℃
1000mm<Pp<2500mm
Combined output map generated

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Yield Prediction and Input-Output Optimisation
Goals:
• Develop a model to predict yield performance to aid farmers to make informed management and financial decisions, and
for policy makers to make timely import and export decisions to strengthen national food security.
Approach:
• Datasets comprised of crop genotype, yield performance, and environment (weather and soil). The genotype dataset
contained genetic information. The yield performance dataset contained the observed yield, check yield (average yield
across all genotypes of the same location), and yield difference of samples for different genotypes planted in different
years and locations. Yield difference is the difference between yield and check yield.
• Two Deep neural network (DNN) approach were used; one for yield and the other for check yield, and then used the
difference of their outputs as the prediction for yield difference.
Impact:
• The results revealed that environmental factors had a greater effect on the crop yield than genotype. DNNs were able to
learn nonlinear and complex relationships between genes, environmental conditions, as well as their interactions from
historical data and make reasonably accurate predictions of yields for new genotypes planted in new locations with
known weather conditions. Performance of the model was found to be relatively sensitive to the quality of weather
prediction, which suggested the importance of weather prediction techniques.
Crop yield prediction is extremely challenging due to numerous complex factors, but is
of great importance to global food production.

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Yield Prediction and Input-Output Optimisation
Reference: Saeed Khaki & Lizhi Wang, 2019, "Crop Yield Prediction Using Deep Neural Networks"

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Additional use cases

25. 25
Predicting Algal Performance for Biofuel Production
Goals:
• Predict top (25-50%) performing transgenic algal strains using environmental, phenotypic, and genotypic data;
• Predict phenotypic responses of algal strains under different environmental conditions.
Data availability for different strains of algae:
• Environmental data;
• Phenotypic data (Photosynthetic, physiological responses);
• Omics data (Genomics, Metabolomics, Proteomics).
Approach:
• Classification of yield performance based on environmental, phenotypic, and genotypic data for each strains (large
input data set requiring Machine-Learning based modelling).
Impact:
• Initial specification set-up from overview of existing data landscape and testing pipelines;
• Prototyped model for a given strain;
• Scaled and built screening process.

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Optimization of Procurement of Agricultural Produce as Raw Materials
Goals:
• Building a Risk Analysis Framework to predict supply and demand of raw materials for optimising procurement
activities.
Approach:
• Demand and supply depends on a variety of complex factors. In addition to historical data, taking these factors
into consideration could improve the precision of prediction to ensure product success.
• As a first step, all data needs to be standardised. The second step is to reduce the number of variables.
• The best performing model can then be used to identify the dominant variables and reduce the overall number.
• Finally, a second-path regression model can be built based on the reduced number of variables.
Impact:
• With a robust Risk Assessment Model based on forecasting demand and supply, value is derived from better
payment terms, cost savings, higher raw material and component quality, and lower supplier risk, among others.
• Procurement optimisation strategies to unlock most value from geographically dispersed smaller suppliers and
contracts.

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Modelling manufacturing costs
Goals:
• Achieving accuracy, consistency and efficiency in cost estimation during early design phases of a product and (its)
manufacturing process that would generate an acceptable profit margin for a OEM.
Approach:
• The estimation algorithm that most closely follows the machining processes used to manufacture the part will be
feature-based cost estimators.
• Recent techniques such as Gradient Boosted Trees and Support Vector Regression are more efficient than the
Multiple Linear Regression and Artificial Neural Networks.
Impact:
• The ranking and quantification of most important cost drivers
• The estimate of the economic production function of component cost according to accumulated production
volume.
• A different view on the traditional breakdown of manufacturing cost of some component parts.

28. What is Artificial
Intelligence?
How does it work?
28

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Machine Learning has penetrated
every aspect of our life!

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We have entered the age of Data
Descriptive
Analytics
Predictive
Analytics
Prescriptive
Analytics
What has happened?
What will happen?
How to influence
what will happen?
Business
impact

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Computers are better than human at…
… recognising patterns.
… optimising.
Wikimedia Commons
ARUPCourtesy:

32. 32
Here comes the buzzwords!

33. • Essentially it is Software 2.0!
• Traditional software development is based
on encoding human-developed rules
(if, else, then)
• Supervised Machine-Learning is about
encoding algorithms which learn by
showing examples
(this is a cat, this is a dog…)
• It’s in fact all about data, and our ability to
identify the right data at the right quality to
feed the right algorithm!
33
So what is Machine-Learning

34. Data collection is
the painpoint,
not technology!
How Internet of Things can help?
Why agricultural ecosystem is critical?
34

35. The time and cost of acquiring the right data
using exhaustive surveys, trials and other
standard approaches can be overcome or
complemented by deploying several key
strategies like using:
• Optical Character Recognition technologies
to digitalize farming logs
• IoT stations to collect local weather/soil data
• Satellite data and images
• App-based data collection from farmer’s fields
or crowdsourcing data using online
communication platforms
35
Technology is not the limiting factor, but data is!

36. • Once data is acquired this doesn’t mean it
is ready to use, it needs to be cleaned
(remove outliers, noisy pattern etc.),
validated (is it representative of the case)
and labeled (“this a case of diseased crop”)
• Hands-on expertise in data technology is
not enough, sound domain knowledge is
essential
• Close collaboration with the experts
(farmers) is critical as only them can validate
the data. Training extension workers to the
data field is vital for the future of Agriculture.
36
Data, data, data
One World Foundation

37. Careers in
Digital Farming
and Precision
Agriculture
Best of both worlds:
where Data Science
meets Agriculture
37

38. 38
Data Science requires complex skillset to acquire
Domain
Knowledge
Statistics
Computer
Science

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Data Science requires complex skillset to acquire
Domain
Knowledge
Visualisation
Communication
Statistics
Computer
Science
Business
Acumen
Software
Engineering
DevOps
Machine
Learning Data
Engineering
Data
Management
Data
Wisdom
Context
Understanding

40. 40
Data Scientists
Business
Software
Engineering
Data Analyst
(textbook)
Data Scientist
Machine
Learning
Engineer
Data Engineer

41. • The challenge ahead is not about the volume
of data, it is about the right data!
• Identifying the right data to collect and be
able to analyse it requires a deep domain
knowledge
• Therefore it is to important to:
1. Establish strong collaboration between
Domain Experts and Data Experts
2. Leverage existing networks such as
extension workers
3. Deepen knowledge in Data Technology
at all level, by strengthening mixed
eduction programs
41
On the importance of Domain Knowledge

42. The Future of
Agriculture is you!
42
Don’t be scared of technology
Don’t be scared of math & statistics
Embrace the data revolution
Build from your strength:
a sound knowledge in Agriculture
Partner with the experts!

43. 43
Dr. Shravani Basu
shravani@sbsf-consultancy.com
Dr. Mukti Sadhan Basu
muktisadhan@sbsf-consultancy.com
https://sbsf-consultancy.com
SBSF Consultancy Pvt. Ltd.
Company Identity Number (CIN): U74999WB2018PTC229097
Company registered address: 7/B Nimchand Karar Street, Adriadaha, Kolkata - 700057, India