commercial & research landscape for smart irrigation systems

Commercial & Research Landscape for Smart Irrigation Systems

Smart Irrigation Systems: Conserve water and enhance crop yields. “More Crop per Drop”

Muhammad Yaseen(muhammad.yaseen@dfki.de)

Intern, Knowledge Management GroupGerman Research Center for Artificial Intelligence (DFKI)Kaiserslautern, Germany

Research Student, Koshish Foundation Research LabNED University of Engineering and TechnologyKarachi, Pakistan

Outline

▪ The Need for Water Conservation and Yield enhancement

▪ Governmental and Global initiatives

▪ What Knowledge Management can Offer

▪ Commercial Products

▪ Research Trends

The Need for Water Conservation and Yield enhancement

▪ Just about 2.5% of the Earth’s total water is freshwater that can be used for various applications including irrigation. (US Geological Survey)

▪ On an average only 45% of the supplied water is used by crop (FAO).

▪ 15% is lost during conveyance

▪ 15% percent is lost in supply channels within the farms

▪ 25% percent is lost due to inefficient water management practices

▪ Population: 9.15 billion by 2050. Food production must increase by 70% (FAO)

Governmental and Global Initiatives

▪ European Initiative for Sustainable Development in Agriculture (EISA)

▪ Agriculture and Food Research Initiative (AFRI), USA

▪ WaterSense, USA

▪ Food and Agriculture Organization (FAO)

Common Goals: “More Crop per Drop”

▪ Increase Land and Water Productivity. “More crop per drop”

▪ Improve Crop yield

▪ Food security for growing population

▪ Reduce environmental impact of agriculture (Climate-Smart Agriculture)

▪ Provide farmers with actionable information about their crop and field

▪ Efficiently distribute and utilize irrigation water

What Knowledge Management can offer?

▪ Eliminate guesswork and hunches

▪ Combine raw data from different sources (sensor data, weather data, soil characteristics)

▪ Provides growers with reliable and actionable information

▪ Efficiently use resources. (Water, Pesticides, Farm Equipment, Energy…)

▪ Visualizations and Data driven insights

A general framework for Knowledge Management

Machine Learning

Neural Network

Knowledge Management

Field Sensor Data

Expert’s Knowledge

Crop and Soil Requirements

Optimal Irrigation Schedule

Precision Agriculture

Disease (Anomaly) DetectionWeather Data

Facts based predictions

Commercial Products

▪ 17 companies and their product offerings were examined.

▪ Different Market Categories

▪ Crop Health Management

▪ Farm Management and Agriculture Logistics

▪ Homeowners and small scale lawns / Commercial landscapes

▪ Ornamental Plant production

▪ Shortlisted: 8 companies(considering only Crop Mgmt. and Farm Mgmt.)

Hardware

• Sensors & Probes (Soil moisture, humidity, temperature…)

• Automation: Blades and Valves

• Wireless sensing networks

• Drones

• Weather stations

Commercial Products: What do they offer?

Software1. Data visualization (Graphs, Plots,

Summaries…)

2. Satellite and GIS info.

3. Predictive analytics

4. Remote control

5. Irrigation scheduling

6. Data driven insights

7. Real-time Notifications

8. …even APIs

Commercial Products: What do they offer?

Comparison Matrix Of Commercial Products

Hardware on Field

SoftwareSolution

Data Analysis and KM

Weather or Satellite Data

Rubicon Farm Connect Yes Yes Limited No

Crop Metrics Yes Yes Not Sure No

AgCo Tech. Yes Yes Limited No

Agribotix Yes Yes Yes Yes

CropX Yes Yes Yes No

AgSmarts Yes Yes Yes No

FarmLogs Not Sure Yes Yes Yes

AquaSpy Not Sure Yes Yes No

Limited: Available features very limited as compared to other products in the matrix.

These companies offer solutions for crop health management, yield improvement, farm automation and smart irrigation. Although the approaches are slightly different. These companies are directly in competition with each other.

Research Outlook

▪ WSN for monitoring temperature, humidity, soil moisture, light intensity, greenhouse gas levels.

▪ Arduino, GSM Module and MATLAB Fuzzy logic toolbox.

▪ Result: Irrigation decisions based on monitored parameters.

▪ Use of Decision Tree for crop water need prediction

▪ Parameters: Max/Min temperature, wind speed, humidity, rainfall, solar radiation, soil type, crop type.

▪ Result: Authors report “74% accuracy”. The system was able to predict the water usage and hence can help farmer make informed decisions.

Case # 1

Case # 2

Research Outlook

▪ Soil classification using 3 classification techniques. pH, Electrical Conductivity, Organic carbon, amounts of P, Fe, Zn, Mn, Cu are learning parameters

▪ Algorithms used: Naïve Bayes, C4.5 (Decision tree), JRip . Rules for soil classification were collected from soil testing lab.

▪ Result: Authors report a “91.9% accuracy” for C4.5 algorithm

▪ A two-year study of Tomato crops in Saudi Arabia.

▪ A test crop was grown with Intelligent Irrigation System (IIS). Parameters used: Solar radiation, wind speed, humidity, rain, air temperature and ET.

▪ Result: Authors report “26% water savings”.

Case # 3

Case # 4

Research Outlook

▪ Sistema Irriga – Irrigation scheduling for 185,000 hectares of land in Brazil

▪ Parameters: Air pressure, temperature, humidity, wind speed, wind direction, rainfall, solar radiation.

▪ Result: Daily irrigation recommendations. Forecasts for next 1-2 days.

Take home point!

▪ Irrigation can be scheduled to achieve efficient water usage by monitoring a set of crop or plant parameters (Humidity, Temperature, Soil Moisture etc.)

▪ Case 1,2 and 3 acquired labelled data from expert’s knowledge and prior experience. Case 4 and 5 have not mentioned how they acquired labelled data.

Case # 5

Relevant Conferences, Journals and Books

▪ InfoAg Conference

▪ European Conference on Precision Agriculture (ECPA)

▪ International Conference on Precision Agriculture (ICPA)

▪ International Conference on Computer and Computing Technologies in Agriculture (ICCTA) and associated Journal of same name.

▪ Precision Agriculture Journal

▪ Data Mining In Agriculture

References

▪ The World’s Water : http://water.usgs.gov/edu/earthwherewater.html

▪ How to feed the World in 2050: http://www.fao.org/fileadmin/templates/wsfs/docs/expert_paper/How_to_Feed_the_World_in_2050.pdf

▪ http://crpit.com/confpapers/CRPITV121Khan.pdf

▪ https://www.researchgate.net/profile/Petraq_Papajorgji2/publication/225309627_A_survey_of_data_mining_techniques_applied_to_agriculture/links/53ed09200cf2981ada11bb9c.pdf

▪ https://arxiv.org/ftp/arxiv/papers/1206/1206.1557.pdf

▪ http://www.ijetae.com/files/Volume5Issue4/IJETAE_0415_66.pdf

▪ http://www.cropj.com/marazky_7_3_2013_305_313.pdf

▪ https://books.google.de/books?id=DybQsneDddYC&lpg=PA7&dq=european%20conference%20on%20precision%20agriculture%20proceedings&pg=PA19#v=onepage&q&f=true

Fragen?

(Questions?)

Danke für Ihre Zeit(Thanks for your time)