Machine Learning in Agriculture Module 1
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The document discusses the critical role of machine learning in enhancing agricultural productivity to meet the rising global food demand due to an increasing population. It highlights applications such as crop yield estimation, weather prediction, and pest control, along with the importance of data quality and availability. Additionally, it notes that machine learning technologies could potentially increase agricultural productivity by 70% by 2050 while also presenting challenges such as data usability and regulations.
Machine Learning in Agriculture Module 1
- 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