BIG DATA ANALYSIS IN AGRICULTURE
THE SMART FARM ANALYTICS
DRIVERS FOR SMART FARMING
SMART FARMING FUNDAMENTALS
Cycle of Smart Farming
- Smart sensing and monitoring
- Smart analysis and planning
- Smart control
- Big Data in the cloud
Arable
- Robotics and Sensors
- Seed/Planting, Soil typing, Crop health, Yield modelling
- Precision farming
- Weather/climate data, Yield data, Soil types, Market information, agricultural census data
Livestock
- Biometric sensing, GPS tracking
- Breeding, monitoring
- Milk robots
- Livestock movements
Horticulture
- Robotics and sensors greenhouse computers
- Lighting, energy management
- Climate control, Precision control
- Weather/climate, market information, social media
Fishery
- Automated Identification Systems
- Surveillance, monitoring
- Market data
- Satellite data
STATE OF THE SMART FARMING & KEY ISSUES
States of the Data Chain
- Data Capture
- Data Storage
- Data Transfer
- Data Transformation
- Data Analytics
- Data Marketing
State of the Art
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Sensors, Open data, data captured by UAVs, Biometric sensing, Genotype information, Reciprocal data
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Cloud-based platform, Hadoop Distributed File System (HDFS), hybrid storage systems, cloud-based data warehouse
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Wireless, cloud-based platform, Linked Open Data
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Machine Learning algorithms, normalise, visualise, anonymise
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Yield models, Planting instructions, Benchmarking, Decision ontologies, Cognitive computing
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Data visualisation
Key Issues
- Availability, quality, formats
- Quick and safe data access , costs
- Safety, agreements on responsibilities and liabilities
- Heterogeneity of data sources, automation of data cleansing and preparation
- Semantic heterogeneity, real-time analytics, scalability, Semantic Community,
- Ownership, privacy, new business models
DATA ANALYTICS PROCESS
