Agriculture and Livestock Informatics Research

Malik Jahan Khan, PhD

Associate Professor (Computer Science)

Namal College, Mianwali

Namal’s Vision

• To become center of academic excellence for rural uplift through:

• Educating bright youth

• Solving rural problems through innovation

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Some Facts

• ~70% of Pakistan is rural

• ~24% of Gross Domestic Product (GDP) comes from agriculture sector

• ~49% of total workforce in Pakistan is employed by agriculture sector

• ~65% of population is directly or indirectly linked with agriculture

• ~27% of total area of the country is under cultivation

Source: Economic survey: Govt. of Pakistan3

Some Facts

• Pakistan is:• 4th largest cotton producing country in the world

• 7th largest wheat producing country in the world

• Agriculture GDP has declined over the last decade

• Total agriculture production is almost 50% of its potential

Sources: FAO of United Nations, Ministry of Finance (Govt. of Pakistan) 4

Intelligence Disease Diagnosis of Crops

• Lack of availability of expert opinion in rural areas

• Lack of outreach to the farmers

• Significant economic dependence on crops health

• Timely and accurate diagnosis of crops diseases is required with minimal dependence on experts

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Proposed Model

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Disease Diagnosis for Crops in MianwaliRegion

• Crops: Wheat and cotton

• Number of diseases: 21

• Number of symptoms in raw data: 50+

• Number of instances: 160

• Source: Field surveys (farmers) + online raw data + District Agriculture Department

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Proposed Model

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Fuzzification

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Implication

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Defuzzification

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Disease Inference

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Snapshot of Domain Data

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Proposed Model

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jfuzzylite: Library being used for end-product

• Free and open-source

• Fuzzy logic control library

• Programmed in Java

• Equipped with a wide variety of controllers, membership functions, aggregation and defuzzification methods

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Results

• Test data of 100 instances collected from surveys

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Outcome

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MeraMaweshi

Intelligent Disease Diagnosis System for Livestock in Rural Pakistan

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The Problem• About 40 millions population of the country relies upon livestock.

• This sub-sector contributes about 55% of agriculture and 12% of overall GDP.

• Pakistan is the 4th largest producer of milk in the world.

• Domain experts are not easily available for helping the farmers as the ratio of available domain experts to number of cattle is very low.

• Remoteness and sparse rural populations are the main factors.

• Average price of a cow or buffalo is about Rs. 150,000.

• Their health is the key challenge faced by the farmers.

• The farmers of rural areas in Pakistan have weak access to veterinary experts to diagnose the disease of their cattle resulting into significant financial loss every year.

Findings of Literature Review

• Thin amount of work has been done in most of the systems• Data is either small or unreal• Most of the published work is at proposal stage and position papers have

been published• Most of the system have not been deployed for real users• Some developed systems are commercial and a common farmer has to pay

for subscription• Validation of the proposed model is weak • Information about the collected data and methodology is vague• There is no work (to the best of our knowledge) contextualized for

Pakistani farmer community

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How it works!

• Identification of Research Problem

• Literature Survey

• Data Collection• District Livestock Office Mianwali ( 7 vet hospitals in 3 tehsils)

• UVAS Pattoki Campus

• Intelligent Algorithm Design

• Implementation

• Deployment

• 89 clinical symptoms

• 33 diseases

• 2000+ real data points

• UVAS – Ravi Campus

• District Livestock Office Mianwali

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Disease Coverage

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Information Architecture

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The Solution to the Problem

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The Solution to the Problem

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Comparison MatrixSolution 1Best Options Solution 3Solution 2

OurSolution

Availability for Farmer No No No Yes

Language Localization No No No Yes

Confidence Level No No No Yes

Medicine Suggestion No No No Yes

Connect to Doctor No No No Yes

Validation No No No Yes

Solution 1: Cow Disease Diagnosis System in China Solution 2: Egyptian Bovine Clinical Knowledge for Newly Born Cows and BuffalosSolution 3: CattleToday: A web-based information system for cattle disease management

Validation Results

• www.meramaweshi.com (Urdu/English)

• Google Play Store: Mera Maweshi (Urdu/English)

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Availability

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Market / OpportunityTHE TARGET MARKETFarmers, Veterinary Practitioners, Pharmaceutical Industry.

TOTAL TARGET MARKET SIZE7~8 Million Farmers, 5,000 Veterinary Practitioners, 50+ Pharmaceutical Companies

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Marketing Plan• Aggressive Outreach to Farmers

• Road Shows at Mela Maweshi• Outreach to Rural Areas • …

• Aggressive outreach to Veterinary Practitioners• Aggressive Outreach to Pharmaceutical Companies• Social Media• SMS Alerts• Public seminars• Conventional media• …

Way Forward…

• Sophisticated HCI for illiterate farmers

• Handling complicated situations

• Involving more stakeholders…

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Funding

• Rs. 4.39 million research grant for Mera Maweshi

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Ministry of IT & Telecom

Government of Pakistan

Collaborations

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Conclusion

• ICT has positive impact in many disciplines of our daily life including healthcare, business, education, communication etc.

• Pakistan is an agricultural country with rich potential

• ICT has immense scope in bringing deeper impact on our agricultural economy

• Need: Inter-disciplinary and integrated teaching, research & collaborations amongst all stakeholders

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Example of Netherlands

• Total cultivable area of Netherlands: ~2 million hectors

• Total cultivable area of Pakistan: ~20 million hectors

• Agri & food export of Netherlands in 2016: ~94 billion EUR

• Agri & food export of Pakistan in 2016 : ~3.3 billion EUR

• Unfortunately, our agriculture export declined by ~14% over the last two years

• Netherlands is the 2nd largest exporter of agri & food products after US

• Germany, Brazil and China take 3rd, 4th and 5th positions

• Why is it so?

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52Thank You for Your Attention

References

[1] Economic Survey of Government of Pakistan (2010-11)

[2] Food, A. O. of the United Nations, FAO Statistical Year Book 2013, FAO Publishers, 2013.

[3] S. R. Sindhu, F. H. Shah, N. A. Khan, A. M. Jatt, S. Fazlani, A. Memon, Tax to GDP Ratio: Measures for improvement, Technical Report, Directorate of Training and Research (Inland Revenue Service) Lahore, Pakistan, 2010.

[4] G. of Pakistan, Overview of The Economy, Technical Report, Ministry of Finance, Government of Pakistan, 2014.

[5] M. El-Telbany, M. Warda, M. El-Borahy, Mining the classification rules for egyptian rice diseases, The International Arab Journal of Information Technology (2006).

[6] M. Ilic, P. Spalevic, M. Veinovic, A. A. M. Ennaas, Data mining model for early fruit diseases detection, in: 23rd Telecommunications Forum (TELFOR), 2015.

[7] J. Huang, Y. Yuan, W. Cui, Y. Zhan, Development of a data mining application for agriculture based on bayesian networks, in: Computer And Computing Technologies In Agriculture, volume 258, 2008.

[8] H. Li, R. Ji, J. Zhang, X. Yuan, K. Hu, L. Qi, Web-based intelligent diagnosis system for cotton diseases control, International Federation for Information Processing (2011).

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