Data science and precision farming Agriculture and Earth Observation workshop 4 5 July 2017 Dutch Ministry of Economic Affairs The Hague, The Netherlands Prof. Jakob de Vlieg Applied Data Science, JADS
A global challenge to meet food supply Precision farming as part of the solution (Remote) sensors, satellites, drones to monitor plant health, disease, soil condition, humidity, environment Fleet of small agri-robots to fertilize, mulch, weed, etc How to Ensure Big Data Brings Value to Farmers? Several Data Challenges -Data interpretation -Disconnected data sources Inability to translate data in agricultural solutions Photo by Fang Chen of Beijing Genomics Institute Integrate agronomy with digital technologies to interpret the data from a farmer s perspective
Bridge AgriFood and Engineering/Data Knowledge Computers are useless. They can only give you answers. Picasso Ability to ask the right technological and business questions to ensure Big Data & Technology can create value to farmers
Domain knowledge essential for Big Data Statistics Source Spurious Correlations (http://www.tylervigen.com)
Jheronimus Academy of Data Science (JADS) Education and Research
JADS+ unique combination of a technical university, a business university and translating basic data science into PoC (Mariënburg Den Bosch) Scientists from various academic groups e.g. from electrical engineering, mechanical engineering, mathematics & computer science, industrial design, business model research, ethics, etc.
JADS Mariënburg to translate basic data science into PoC/products Applied data science group for AgriFood Eco system of researchers, entrepreneurs and students
Precision farming: link environmental variability with crop protection and seed products Accurate field and environmental variability analysis e.g. Improve granularity and timeliness of data sets to measure simultaneously amount of vegetation & disease Translate data into specific treatment actions: e.g. spreading fertilizers, spraying crop protection (chemicals or mechanical), optimizing harvesting time and so on application map plant biomass & health Swarm of agri-robots, e.g. remove weeds mechanically by computer vision & A.I. elevation data
Integrative Digital Architecture: extract relevant information from heterogenous data sets Big Data Layers Omics Data Phenomics Data Environment Data Image/Satellite Data Sensor Data Predictive Analytics Solutions to Farmers Seed Bank Environment Phenomics Omics Chemistry Germplasm Breeding Pops & Materials Pedigree Weather Soil Type Crop Modeling Yield & Components Images Plant Health Genomics Transcriptomics Proteomics Metabolomics Epigenomics herbicides Fungicides Fertilizers
Precision Farming: Maximize scientific & economic value from laboratory, greenhouse, field study and real farm data Database Drip irrigation Soil monitoring Pheno mobile Precision spraying Climate monitoring Plot combine Integrative digital architectures to develop predictive algorithms
Data Hub for Agri&Food chain Access point for public/private datasets & tools for research & innovation Data integration, data stewardship, data analytics, computer simulation methods (e.g. agronomic schemes & environments) based on FAIR principals (privacy, authorization, etc.) Systems-level approach by integrating data from several disciplines: agronomy, pathology, physiology, images, genomics, genetics, breeding, physics, synthetic biology, modeling, meteorology, consumer behavior and engineering. Linking Data and Minds
Stimulating cross-overs between high tech and agri-food Smart farming: to increase food production by 70% to meet demand by 2050 Smart food processing: to provide sufficient nutritious food while reducing the footprint Education: to develop new talent in era of digitalization and miniaturization And more. NSO/ESA/Aerovision?