UAS as a platform for integrated sensing and big data

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1 Unmanned Aerial Systems in Precision Agriculture Thursday 30 th January 2014 UAS as a platform for integrated sensing and big data Prof Anthony Furness Visiting Professor Dr Tomas Norton, Senior Lecturer Department of Agricultural Engineering

2 Agenda The Green Revolution origins of data developments in agriculture Precision the nature of precision Precision Agriculture defining precision agriculture Big Data the features of big data Big Data Analytics the analytical counterpart of big data Platforms for Data Acquisition the available platforms and sources of data for agricultural big data development Farming Data Hubs potential role of farming data hubs in big data development The UAS as a platform for data acquisition specific attention to UAS as a platform for data acquisition Big Data and extending UAS Capability considering how big data can extend the capability of UASs Importance of such Developments importance in relation to national, European and Global needs

3 The Green Revolution 1940s scientific breakthroughs in plant genetics stimulated innovation in agricultural mechanisation, demonstrating the power of integrating science and engineering for the benefit of society So-called Green Revolution this era not only provided precedence for innovations in food production but also demonstrated the power of datadriven decision support as farm extension services rapidly expanded. Services exploited freely available Data (i.e. Open Data) in the form of weather and crop-growth forecasts to provide farmers with the capacity to benchmark and continuously improve in their capabilities to grow food. (Benor, D., Harrison, J. Q., Baxter, M. (1984). Agricultural extension - the training and visit system. A Worldbank publication) More recent advances in computer science and technology during the past two decades has fuelled the development of Precision Agriculture More Data, more precise data becoming increasingly important

4 PRECISION is about more precise (and accurate) measurement across all farming modalities and their use in developing more efficient and effective processes, practices and services, including the management of those process; it is about measuring, understanding and reducing variability in processes, a total quality approach that embraces legacy and emergent technologies to achieve the complementary goals of agricultural food production. PRECISION also implies more data, both from individual farming applications and collective data sources from multiple sources The developments in the latter are resulting in very large data sources (Big data) that require new techniques to accommodate their handling

5 Precision Agriculture Wherein sensors, data processing and machine control enable the conditioning of operations based on understanding the inherent variability in crop/animal production systems. Precision Agriculture is now evolving into a paradigm where new knowledge in the biophysical sciences like bio-photonics, bioelectromagnetics and bio-fluidics are enabling the specific detection of physiological traits in crops and/or animals, intending to provide farmers with better opportunities to manage their systems. Integration of data from different sources and levels, is becoming increasingly important, from commodity markets for price volatility predictions to real-time weather, soil and air quality, and equipment usage for smarter decision support. Greater data demands and opportunities are creating the need for complementary data handling facilities and handling techniques Big Data techniques

6 Big Data Big Data may be characterised by having extreme or variable values of one or more of the following features*: Volume (size of data set) Variety (structure and range of data sets) Velocity (acquisition rate of data) Veracity (uncertain quality or provenance of data) Variability (in the meaning of data and relation to quality or robustness of data) Complexity (with respect to relationships between data sets, sources of data Demanding new approaches to maximise the value extractable from large and complex data sets. *Big Data and Computing Building a Vision for ARS Information Management, USDA Agricultural Research Service Workshop, Feb 2013

7 Big Data and Big Data Analytics The Big Data approach requires less, but complementary dependence on the strictures of the causality-focused standard scientific method The approach utilises vast quantities of data to achieve by-proxy correlations that can assist in developing the foundations for Precision Agriculture Big Data Analytics, how this approach is now termed, provides the potential to catalyse a new revolution in agricultural production, presenting unprecedented opportunities for identifying associations between information and knowledge entities, often faster and with greater temporal significance than conventional small data analytics. Using the data from multitude of sensors embedded within fields, farm buildings, ground-machines, aerial vehicles and satellite platforms we can effectively inform predictive models that achieve insights and recommendations not previously possible.

8 Big Data Analytics extending the view Big Data analytics this data can be reused, time and time again to reveal associations from different perspectives, such as context, intention, objective, opportunities, constraints, know-how and so on, both within and across data sets. With dynamic additions to data set content, data set types, data set merging, old data valuation and to the parsing algorithmic windows maximum value may be extracted from the data acquired The potential that this offers for agricultural development would appear to be immense, with parallels in service provision that are being seen for big data services in other areas of business activity.

9 Platforms for Data Acquisition Global Navigation Satellite Systems Satellite Remote Sensing and Imaging systems Unmanned Aerial Systems - Sensing and Imaging systems Ground-based Sensing systems fixed and mobile

10 Internet, Internet of Things (IoT) and the Cloud Physical World Objectbased Systems Cloud computing Object-based data processing needs (Human / machinemachine-human) Farming support Access to expert data / information, eg Evidence-based medicine and diagnostic services, Super- Navigator GPS-related services information, (Human-machine-machine - Human) Farming support Internet-based facilities National / International survey data gathering eg Financial /economic/ resources / energy usage (Humanmachine-machine - Human) - Farming support Regional, National / International sensory data gathering eg For monitoring and protection of pooled resources / flood defences (machine-machine - Human / Actuation) - Farming support Regional, National / International data gathering eg For forecasting purposes weather/ natural disaster prevention (machine-machine-human Activation) Farming support Systems-defined automated software downloads and up-dates for application and service systems eg. Security support systems, surveillance systems, transport management and associated information, mobile phone-based Apps (machine-machine) Farming support Remote data analysis, eg automated analytical services for industry, commerce and services eg. Mastitis detection (Precision livestock farming), (Human-machinemachine - Human) Farming support

11 Farming Data Hubs Other Data Sources Farming Apps Data Acquisition Platforms Farm-based Data Hub Data tagging Meta data tagging Data aggregation Data transfer Big Data Providers Data aggregation Data analytics Data services Data sharing

12 Unmanned Aerial Systems for Data Acquisition Pteryx UAS UASs have gained a lot of interest in agriculture because they offer a range of attributes for remote data gathering, including: Near-real-time gathering of information from low altitude (< 120m) vantage points below cloud level (except fog conditions) on a whenever and where ever basis Low cost of investment and operation compared to common remote and proximal sensing systems; High potential for automation, which may enable inexperienced users to handle UASs with little training; Flexibility in choosing payload sensors and ground space resolution; Possibilities for use in actuator applications, such as synchronised mapping, cultivation, fertiliser application and pest control.

13 Unmanned Aerial Systems for Data Acquisition UAS platform hardware Multicopter vs helicopter vs glider plane Engines, battery Sensors for georeferencing (IMU, GNSS, ultra sonic) Hardware design (protection) UAS software Autonomous navigation Path planning Obstacle avoidance Sensor triggering Autonomous starting/landing Emergency strategies Sensor data processing Radiometry Geometry Mosaicking Storage, import, export Meta data generation Sensors for applications Multispectral cameras (Vis, NIR, IR) Spectrometers (Vis- NIR) LIDAR Applications of UAS Arable Farming Plant production Biomass mapping Nitrogen estimation Water stress, irrigation Weed identification and control Pathogen infection Livestock farming Pasture management (biomass, quality) Animal monitoring (counting, weight, activity, health) Animal drive Farm infrastructure inspection Roofs, solar panels, irrigation systems, fish basins Fences Actuators On board of UAS Controllers Sampler Other farm machinery Fertilizer spreader Sprayer Sprinkler Combine

14 Big Data Analytics to Support Farming Operations Openness Data, Tools, Networks Data Sharing loops Farm Plant/Animal Field/Barn Farm Regional Continuous Improvement Farm 1 Farm 2 Farm N National Continuous Improvement Region 1 Region 2 Region N International Country 1 Country 2 Country N Continuous Improvement Support Centres: NCPF, S&WMC context understanding Sensor data pooling for Precision Ag New Agri-Tech opportunities intention Regional data pooling for Environmental management constraint awareness objective driven Market data pooling for Economic analysis Policy making know how based DATA NGOs & Gov - sources of open-data CISCO - innovative networking tools and services SMEs - innovative and technologically challenging services

15 Big Data Extension to UAS Capability QuestUAS 200/300 Big Data and big data analytics extending the capability of unmanned aerial systems by contributing to: Foundational developments in precision agriculture National, European and international statistics for farming development National, European and international standards for precision agriculture National policy and agencies for managing farming epidemics and pest control in farming Farming management and decision support in precision farming and input into achieving sustainable competitive agricultural economies Social inclusion in farming developments, including issues of food quality, food safety and national nutritional needs assessment Environmental management and factors impacting climate change Collective studies on genomics, proteomics and phenomics Evidential support in farming practice Developments in UAS systems and issues-handling in areas such as privacy, security of data and UAS governance

16 Why is this important? - EU Priorities Business growth, research, innovation, enhancing ICT (including Internet of Things and Big Data impact upon precision developments) Shifts towards low carbon economy (impact of precision on reducing carbon footprint) Environmental, Climate Change (Precision impacts) Employment & Skills (Precision impacts) Social inclusion (impact upon precision developments, including smart city urban farming) All of these priorities are also embraced in a further priority that is national, European and GLOBAL Future food security

17 Working in Collaboration Thank you for your attention

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