Smart automation in the agri-food chain: State of the art, prospects and impacts on

workforce demands

Stavros G. Vougioukas1 and Spyros Fountas2

1University of California, Davis, USA2Agricultural University of Athens, Greece

The Future of Work in Agriculture

Challenge #1

Challenge #1

Challenge #2

Challenge #3

Source: http://www.climatechange-foodsecurity.org/

Solutions Toolbox

Volume of sensors in agriculture

Data Need per Plant

Source: http://bit.ly/1KUVVoR

The Agri-food chain

Farming

Processing & Distribution

Breeding

• Power-intensive tasks.

• Control-intensive tasks.(Binswanger, 1986).

20th Century Mechanization: Power-intensive tasks

• USA average labor/acre to produce corn for grain:

1915-19: 34.2 hrs

1974-78: 3.7 hrs

2015-17: 2.7 hrs.

Present: Precision Agriculture (PA) - Variable Rate (VAR) technologies

• Utilize technology to apply:

– The right types and exact amounts of inputs;

– At exactly the right time and place.

• Power and control intensive: high throughput and selective.

• PA technologies:

– Affect mainly the efficiencies of inputs (water, chemicals, seeds, energy).

– Maintain demand for manual labor.

– Increase demand for technology-related skills.

Present-Future: Robotics-Mechatronics-Automation

• Advances in robotic/mechatronic technologies:

– Computers, electronics, sensors, actuators, perception.

• Incorporated into agricultural machines in two different ways:

I. Increased automation on existing (large) machines.

II. Smart robotic implements & self-propelled, autonomous (smaller) robots.

Present-Future: Smart Automation – Smart Farming

• Manage processes in the agri-food chain via collective use of hardware and software to:

– Collect data;

– Extract and process information from data;

– Contribute to decision-making;

– Take physical actions.

• Our focus today: on-farm, labor-impacting smart automation.

– Applicability also on post-harvest.

What are the most labor-impacting technologies?

• Farm employment in USA:

– 18% agricultural equipment operators.

– 56% farm workers (contract labor not included).

– 4% on-farm grading, sorting and packing.

• Labor-impacting technologies:

I. Full machine autonomy (fewer operators required).

II. Physical interaction with the crop and its environment in ways that were not possible before (less manual labor required).

Full autonomy for agricultural machines

I. Autonomous navigation:

– Commercial technology for field crops (GNSS-based).

– Soon available for orchards and vineyards (sensor-based).

Full autonomy for agricultural machines

II. Autonomous operation requires an operator’s advanced:

– Perception, situation awareness, judgment & task-specific knowledge.

– Not feasible soon; remote supervised autonomy is a practical scenario.

– Legal framework is not developed yet.

Targeted physical interaction with crops and environment

• Closed-loop sensing and actuation is the major mode of operation during targeted interaction.

• Major requirements:

– High throughput (i.e., operations per second).

– Very high efficiency, i.e., percentage of successful operations).

– Cost-effective operation.

• Plethora of sensors and methods.

• Requirements: Accuracy, precision, speed.

Sensing: Estimate crop and environment properties

Challenges and advances in crop sensing

• Challenges:

– Wide variations in environmental conditions.

– Plant biological variability.

– Limited crop visibility due to complex plant structures.

• Advances:

– New, low-cost, high-performance sensors.

– Machine learning (deep neural networks).

– Better visibility via breeding and horticultural practices.

Actuation: Take physical action upon the crop or environment

• Requirements:

– Very high efficiency (percentage of successful operations );

– High throughput (operations per second ).

Challenges and advances in actuation

• Challenges:– Living tissues can be easily damaged.

– Biological variation introduces variability in physical properties.

– Limited accessibility of the targeted plants or their parts.

– Contact-based manipulation has complex physics that cannot be modeled and controlled easily.

• Advances:– Innovative end-effectors – soft robotics.

– Multiple coordinated actuators; machine learning for control.

– Better accessibility via breeding and horticultural practices.

Smart automation and workforce

I. Remotely supervised teams of semi-autonomous machines

– Slightly less than one operator per machine needed.

– Increased demand for operator technical and application-related expertise.

• Configure, supervise, adjust and optimize the operating parameters of the automated equipment.

– Increased stress, responsibility...

Smart automation and workforce

II. Targeted physical interaction with crops & environment

– Machines automate tasks, not jobs.

– Reduced demand for unskilled labor in tasks like manual weeding and harvesting (10%-85%).

– Increased demand for machine operators with advanced skills in automation and information technologies.

– Business model turning to service?

Development of smart automation

• Large ag. equipment manufacturers lead the development of type-I automation technologies for farm machines.

• Smaller start-up companies lead innovation in robotic implements and agricultural robots that interact with crops (type-II automation).

• Obstacles for type-II automation:

– Sensing & actuation challenges are difficult and time-consuming to overcome.

– Market fragmentation: Custom-designed machine needed for each crop.

– Capacity to conduct innovative R&D locally; not available in developing world.

– It may take a while until many local crops in Africa and Asia are mechanized.

Adoption of smart automation

• The adoption of smart automation and PA/VAR technologies is still slow.

• High investment costs remain one of the most significant barriers, especially for small farms.

• A possible future is teams of smaller highly-automated agricultural machines (no operator needed on board).

• Next: European perspective on smart farming.

H2020 IoF2020: Internet of Things

https://www.iof2020.eu

H2020 Applying Gaming Technologies for training professionals

GATES develops a serious game-based trainingplatform, making use of different gamingtechnologies, in order to train professionalsacross the agricultural value chain on the useof Smart Farming Technology, thus allowingdeploying its full economic and environmentalpotential in European agriculture

http://www.gates-game.eu

H2020 OPTIMA project on crop protection

http://optima-h2020.eu/

Optimized Pest Integrated Management to precisely detect and control plant diseases in perennial crops and open-field vegetables.

Not an easy task for the farming community…

81% of the Danish and 78% of the US famers preferred to store the data themselves.

88% of the US famers preferred not to store the data in a shared Internet-based database explaining the reluctance of software vendors to push in this direction, which further emphasize the importance of farm data ownership.

(Fountas et al., 2005. Precision Agriculture 6, 121-141.)

• TITLE: European Agricultural Knowledge and Innovation Systems (AKIS)

towards innovation-driven research in Smart Farming Technology.

• FOCUS: Smart Farming technologies: Application of ICT into Agriculture,

leading to a Third Green Revolution:

Information Management systems.

Precision Agriculture.

Automation & Robots.

Role of Thematic Network Smart-AKIS

Recommendations & policy briefs for closing the research and innovation divide in SFT in Europe

Grassroots level

European level

Trends in SFT research

Factors affecting SFT adoption &

innovation cases

Methodology

Actions to overcome the

barriers

Most popular SFT &

applications

Most popular SFT &

applications

Policy gaps for SFT adoption

Validation of barriers,

incentives and needs

Barriers, incentives and

needs

Policy gaps for SFT adoption

MULTI-ACTOR INNOVATION:

• 20 workshops held in

target countries

• +1000 actors involved

• +60 project/collaboration

ideas on Smart Farming

Technologies

32

Main achievements

SMART FARMING PLATFORM: Free, open, online and updatable:

• A total of about 1500 entries in Platform.

800 scientific articles.

220 research projects.

480 commercial products.

MAPPING SMART FARMING STAKEHOLDERS:

• Online interactive map with +110 stakeholders: operational groups, networks, platforms, research centers, accelerators, etc, updatable until end of project

MainstreamingSmart Farming

The 3 C’sproblem

Agriculturaldata

Best valuefor money

Supportstrategies

Enhancing innovation-driven agricultural research

1. Increase farmers’ participation: fund proposals’ preparation, demos, visits, etc.

2. Coordinate Thematic Networks and Multi-Actor Approach projects: joint workshops, integrated platforms, translation.

3. Reinforce the intermediary role of advisory services and other facilitators

4. Create small networks of end-users

5. Increase the RDPs budget for creation of Operational Groups

6. Facilitate synergies between different research grants for territorial cooperation and for education and training. Challenge-based approach.

7. Simplify access to R&D and innovation funding and reporting

8. Close the gap between agricultural research and rural development: Smart Villages Act

THANK YOU!

The farmer of the future ?