A. Exploratory research

Project n°1: FieldView™

  1. Project carriers: The Climate Corporate
  2. Beneficiaries: Grower,Farmer,Agricultural professional
  3. Users: Farmer
  4. Need: Neasuring the impact of your agronomic decisions on your fields to optimize yield and maximize profit.
  5. Principle: By unique research and scientific models, along with the field data, to optimize the investment.
  6. Main technologies: Marchine learning, Scientific models
  7. Source: www.climate.com

Project n°2: FarmCommand

  1. Project carriers: FamersEdge
  2. Beneficiaries: Farmer,Agricultural professional
  3. Users: Farmer
  4. Need: Help farmers run efficient operations while producing more food
  5. Principle: Combining robust data sets, unique digital infrastructure and machine learning.
  6. Main technologies: Machine learning, predictive modeling
  7. Source: www.farmersedge.ca

Project n°3: AgEye

  1. Project carriers: AgEye Technologies
  2. Beneficiaries: Indoor Farmer
  3. Users: Farmer
  4. Need: The first AI-powered platform for indoor farming that monitors every moment, of every plant, to increase yields and reduce operational costs.
  5. Principle: using Cameras and sensors to create a private, secure, and fully decentralized network of synchronized communication and processing of your crop data.
  6. Main technologies: Marchine learning models
  7. Source: https://ageyetech.com/platform/

Project n°4: R7® Field Forecasting

  1. Project carriers: Land O'Lakes
  2. Beneficiaries: Farmer,Agricultural professional
  3. Users: Farmer
  4. Need: helps farmers determine optimal timing and rates for nutrient applications and get to maximize their investments.
  5. Principle: uses historical weather data, field-specific information and tissue sampling results 
  6. Main technologies: Machine learning, predictive modeling
  7. Source: https://www.landolakesinc.com/Members/Member-News/April-2018/New-ag-tech-tool-from-Winfield-United-wins-award

B.Deepening

I.FieldView™

  1. Carriers and actors of the project: A&L Great Lakes, Aker Technologies,CropMetrics
  2. Research question: How can we use Artificial Intelligence to increase yield with variable rate prescription tools for the small-scare farmers?
  3. The reason you selected this project:  Unlike other companies that rely on existing public data, FieldView™ provides growers and agricultural professionals not only with high-quality, accurate data, but also with field-level analysis, predictive modeling

II.User scenario

  1. Users: Farmer, Grower
  2. Persona:
    • Jerry
    • 41 yrs
    • Kingsley, Iowa, United States
    • Farmer
    • Like many farmers, Jerry is a solo operator, but stays in touch with his brother in Singapore using today’s digital farming tools. However, Jerry faces some unpredictable challenges during the Summer.
      • “A farmer has to make 150 decisions on their crop every year before it’s even in the bin. We just gotta go with the flow and see what happens. Mother Nature still has control.”
  3. Key features: Get your data in one place , Uncover valuable field insight, Optimize your input
  4. UX storyboard

III.Technical analysis

  1. General principle: Digital Farming’s leading software platform.
  2. Technical overview - AI version : use statistical and agronomic models, research and data (including historical, estimated and simulated weather and agronomic data), to generate the recommendations and precipitation, moisture, temperature, growth stage and other information. 
  3. Added value thanks to Artificial Intelligence: Collect, store, and visualize critical field data to monitor and measure the impact of your agronomic decisions on your fields to optimize yield and maximize profit.