Agriculture big data
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The document discusses the role of big data analytics in agriculture, emphasizing the transition from precision agriculture to smart farming to enhance productivity, environmental sustainability, and food security. It outlines various data sources such as sensors, databases, and social media for large-scale data collection, and highlights the importance of descriptive, diagnostic, predictive, and prescriptive analytics in decision-making. Additionally, it advocates for a structured approach to utilizing big data through a blueprint that prioritizes problem-solving in agricultural practices.
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Agriculture big data
- 1. Big Data Analytic for Agriculture [email protected]
- 2. ⢠Dosen DTETI ugm ⢠Direktur DSSDI ugm ⢠co-founder datains.id ⢠anggota Forum Masyarakat Statistik ⢠S1 teknik elektro ugm ⢠S2 erasmus univ netherland ⢠S3 electronic, cork institute of tech, ireland ⢠https://acadstaff.ugm.ac.id/widyawan
- 3. âIn God we trust, all others must bring data.â W. Edwards Deming
- 4. Agenda ⢠Data and Analytic ⢠Big Data for Agriculture
- 5. Background ⢠From precession agriculture to smart farming ⢠Smart farming important to answer productivity, environmental impact, food security and sustainability ⢠Complex, multivariate and unpredictable agricultural ecosystem need to be better understand and analysed ⢠Need large-scale collection, pre-processing, modelling
- 8. Big Data source Data Source Availability Veracity Sensor Closed Medium - High Administrative Database Closed Medium - High App Database Closed Medium - High Social Media Open Low - Medium Online news Open Medium ⢠Sensor: Agriculture sensor, GPS, Cellular Network, CCTV, WiFi, IoT ⢠Admin/Transactional Database: Gov. Enterprise System, Marketplace, etc ⢠App Database: TaniHub, Desa App, etc ⢠Social Media: Twitter, Facebook, IG, etc ⢠Online news: detik.com, kompas.com, cnn.com, etc
- 10. current analytic technology data decision action descriptive what happened? diagnostic why did it happen? predictive What will happen? prescriptive what should I do? machine human human still doing most of the process Analyze ⢠Descriptive ⢠Predictive ⢠Prescriptive
- 11. descriptive analytics is still the most successful http://meetings2.informs.org/analytics2013/execforum.html
- 12. Weather and Climate Change Tesfaye, et.all., 2016, Climate change impacts and potential benefits of heat- tolerant maize in South Asia
- 13. Weather Data Source https://openweathermap.org/
- 15. Farmer Decision Making ⢠an assessment framework that empowers brands, retailers, suppliers and farmers at every stage in their sustainability journey, to measure the environmental impacts of commodity crop production and identify opportunities for continuous improvement. The FieldprintŽ Platform https://fieldtomarket.org/
- 17. Crop Analysis
- 19. Mobile Phone Data for Individual Mobility Network
- 20. Mobile Phone Data for population movement between regions Mobile Data for Tourism, Migration, Population and Transport in Korea, 6th International Conference on Big Data for Official Statistics, 31 August-2 Sept, 2020
- 22. Start from the end Problem/Case study Data Tools Analytic Story Telling
- 23. Big Data Blueprint ⢠Many possibilities, priorities ⢠Multiyear, but a living document
- 24. Take Out ⢠Make blueprint for big data in agriculture ⢠Start from use case or problem to be solved ⢠Data, analytic and IoT