Discuss the opportunities of incorporation of machine learning in agriculture. Briefly discuss different machine learning strategies. Briefly discuss the ways of machine learning can be used

1. Machine Learning in Agriculture
Module 1
Presented By
Dr. Prasenjit Dey,
Assistant Professor,
Coochbehar Government Engineering College,
West Bengal, India

2. AGRICUL
TURALDEMAND
 Currant world population is approximately
7.9 Billion
 The population of the earth is expected to
be around 10 Billion by 2050
 More population
 More per capita consumption
 Higher per capita demand for food
Alexandratos N, Bruinsma J
.2012. World agriculture towards 2030/2050, the 2012
revision. ESAWorking Paper No. 12-03, June 2012. Rome: Food and Agriculture
Organization of the United Nations (FAO)

3. YIELD INCREASES
 Current growth of the yield is not
sufficient
 Crop yield must increase 60% to
meet the demand by 2050
 Urgent need to increase the yield
RayDK, Mueller ND, West PC, Foley JA. 2013.Yield Trends Are Insufficient to
Double Global Crop Production by 2050. PLoS ONE 8(6),
doi:10.1371/journal.pone.0066428.

4. Importance of Agricultural Sector in INDIA
 66% of Indians are employed with agriculture directly or indirectly
 In 2020-21, 19.9% of India’s GDP depends of agriculture
 Influences the growth of socio-economic sector in India
 Occupied almost 43% of India's geographical area
 Huge investment made for Irrigation facilities etc. in 11th five year plan

5. Areas for enhancing Agriculture Sector
 Monitoring the crop conditions
 Predicting the Weather and climate
 Decision support for agricultural planning and policy-making
 Artificial neural network for plant classification using computer vision
 Intelligent environment control for plant production systems
 Intelligent robots in agriculture
 An expert geographical information system for land evaluation

6. Growth of Internet and Possibilities

7. Opportunities from Deploying Advanced
Technologies On Farm

8. Why Machine Learning
 Machine learning is used in regression tasks
 Approximation of crop yield of an agricultural land
 Machine learning is used in classification task
 Classify a cat or a dog from a group of cats and dogs
 Machine learning is used in forecasting task
 Weather prediction
 Machine learning is used in decision making
 Similar to if else scenario but conditions are changes by
itself

9. MACHINE LEARNING
 Ability to automatically learn to recognize complex patterns and make intelligent
decisions based on data
https://analyticsindiamag.com/wp-content/uploads/2018/05/nural-network_3.gif

10. Use of Machine Learning in Agriculture
 To estimate the crop yield of an agricultural land by using regression algorithm
 Helps in increasing the productivity of crops in future
 To identify different types of crops’ species by using classification algorithm
 Helps in crop plantation decision making
 To Differentiate crops and weeds by using classification algorithm
 Helps in crop plantation decision making
 To perform low cost pest control by using classification algorithm
 Helps in increasing the productivity of crops
 To forecast the weather condition by using forecasting algorithm
 Helps to take precaution against natural calamity
 To develop better decision making support
 crops management, soil management etc.

11. Components of Machine Learning
 Training algorithms
 Classification Algorithm
 Regression Algorithm
 Forecasting Algorithm
 Decision Making Algorithms
 Data
 Textual Data
 Image data
 Numerical Data
Data science is a domain which retrieves useful information from the data

12. Data Science in Agriculture
 A sub domain of machine learning
 According to Normal Borlaug, with the help of AI and machine learning it is possible to
feed on a sustainable basis a population of 10 billion people
 GREEN REVOLUTION(1960 –)
 INTENSIFY
 Apply breeding, fertilization to increase yields
 BIOTECH REVOLUTION(1980 –)
 BIOTECH
 Marker assisted selection
 GREEN DATA REVOLUTION(2010 –)
 OPTIMIZE
 Apply machine learning/data science to optimize management

13. Data in Agriculture
 Numerical values of crop yields
 Predict crop yield in future
 Soil quality estimation
 Crop Images
 Diagnosis any crop disease
 Identity different types of crop’s species
 Bug detection

14. Machine Learning: Data Mining
Machine learning
Data Mining Statistics
What is important?
How can it be built?
How can predictions be made?

15. YIELD OPTIMIZATION
OPTIMIZED YIELD
Yield optimization by using machine
learningbasedpredictivemodel
YIELD
Yield optimization by traditional
approaches

16. Plant Growth Optimization Problem
 In plant production, good fruit yield requires an optimal balance between
 Vegetative growth (e.g. root, stem, leaf growth)
 Reproductive growth (e.g. flower and fruit growth)
 The ratio of total leaf length (TLL) to stem diameter (SD) defines as a predictor for
plant production growth
 Machine learning can be used to predict crop yield(Y) from total leaf length (TLL)
to stem diameter (SD)
 Y = f(TLL, SD)

17. Effect of Machine learning on Agriculture
 It is estimated that with the help of new technologies like machine learning has the
potential to increase the agricultural productivity by 70% by 2050
 90% of all crop losses are due to weather, with weather predictive machine
learning algorithms, this damage can be decreased by 25%.
 There will be 27billion connected devices in 2025 among them 225 Million will be
used in agriculture.

18. Machine learning based Available
Technologies in Agriculture
 iCow:
 It is a mobile application used in dairy farms to perform 24x7 surveillance on the cows. It
tracks the vital days of cows gestation period and suggest nearest vet centers.
 FarmDrive:
 It is used for record keeping propose. It stores the information like farmers’ expenses,
revenues, and yields etc. Farmers can apply for a loan. Based on the farmers’ track
record they can get loan approvals and receive loans .
 Weed-killing AI robot by ecoRobotix’s :
 It is used to separate out weeds from wheat and to kill weeds. It also minimizes the use
of pesticides.

19. Challenges
 Availability of data
 No input, no output
 Satellite, drones, tractors, sensors, smartphones, data entry, historical data, robots
 Quality of data
 Usability
 Interoperability
 Same format
 Clear ROI
 motivation
 Policies & Regulations
 Cognitive computing (machine gives solutions, human takes decision)
 AI (machine takes decision)
 Social Awareness

20. Thank You