Agricultural drone challenge

From «Land O’Lakes Prize: Drone Challenge» placed in Herox, the report «Land O’Lakes Drone Challenge Crowdsourcing Competition Underway», by Matthew J. Grassi , published in Precision Agriculture The Focus At Cornell Field Day.

Farming practices in the US have soared beyond previous limits with cutting-edge precision software and machinery, and an unprecedented level of automation in the field. But don’t be fooled — there’s still plenty of room for an even more revolutionary breakthrough. And something “revolutionary” is exactly what the Drone Challenge is seeking.

Drones have tremendous potential, a tool like none other before that could revolutionize precision agriculture. Yet, a truly exceptional integration of aerial drone imagery and automation still doesn’t exist. Today’s drone solutions require a great deal of time and effort in the data collection and processing workflow, which greatly decreases the desire and ability of farmers to tap into the potential benefits. As a result, there’s a huge opportunity for innovators to bring world-class imagery, smart tech, and scalable technology together in a groundbreaking solution for farms everywhere.

Land O’Lakes, Inc. , a Fortune-200 farm-to-fork cooperative, is calling on innovators from the tech industry to enter the Land O’Lakes Prize: Drone Challenge, a crowdsourcing competition to surface valuable, user-friendly drone solutions that will enable farmers to make better decisions for their crops as they work to produce more food to feed more people.

Nowadays “Drones don’t offer a good return on investment for farmers today,” said Mike Vande Logt, EVP and COO for WinField United. “A farmer has to get to the field, launch the drone, take the pictures, pack up, download the data, stitch the images together, then figure out what the images are telling him…it’s time consuming and the applications are difficult to use.”

The new drone hardware and software solutions being sought will solve critical issues for farmers. They will limit the need for human involvement in field data collection, decrease the time needed to access crop imagery and improve the ability for a farmer to make decisions based on field health data.

The Prize is open to individuals, age 18 or older, private teams, public teams, and collegiate teams. Individual competitors and teams may originate from any country.  Employees of Land O’Lakes, Inc., HeroX, and the Dean’s Office and Computer Science and Engineering Department at the College of Science and Engineering of the University of Minnesota, and their respective immediate family members or persons living in their households (whether or not related), and advertising agencies, affiliates and/or subsidiaries of any of the above, are not eligible to enter or win.

How to particiapte?

To be eligible to be selected as a Finalist, the Solution must, at minimum:

  • Be able to record orthorectified crop images with Red (600-720 nm) and IR (760-900 nm) spectral bands in a minimum of georeferenced .geotiff format.
  • Be capable of autonomously determining an appropriate flight path to image a field after receiving a file containing the bounds of the area to be imaged
  • Be capable of autonomously operating (take off, collect data, land, transmitting data etc.) unattended for multiple days, with multiple flights per day.
  • Utilize wireless connectivity (suitable for use in rural areas) to transmit and receive data.
  • Be able to operate in winds of up to 20mph, and use data connection to determine if weather conditions are safe for flight.

The Solution may include a variety of approaches, including, but not limited to:

  • Drone and UAV hardware (multi-copter, fixed wing, fixed wing/vertical take off hybrid, inflatable, etc.)
  • Base stations, ports, or hanger hardware to enable longer term autonomy

The judging panel will rank the eligible Solutions submitted against the following criteria:

Criteria Description Percent Importance
Imaging Capabilities
  • Record orthorectified crop images
  • Red (600-720 nm) and IR (760-900 nm) spectral bands in minimum of .geotiff format
  • Automatic image stitching with sun/brightness correction
  • Ability to store unique identifier to associate imagery with a grower/farmer
15

 

Autonomous Operation
  • Receive a file defining the bounds of the area/fields to be imaged
  • Autonomously determine a flight path to image a given area
  • Identify and avoid obstacles during flight
  • Ability to operate (take off, collect data, land, recharge, transmit data output etc.) unattended for multiple days, with multiple flights per day
  • Ability to receive/alter instructions mid flight
  • A human operator must be able to override with manual inputs at any time
35
Communications and Data Handling
  • Utilize Verizon LTE network, or other wireless connectivity (suitable for use in rural areas) to transmit and receive large amounts of data
  • Solution must be able to collect and store large amounts of collected data
15
Scalability
  • A single flying vehicle should be able to image multiple different fields (at least 3 – 70 acre fields) in a 1.5 mile radius in a 4 hour window
  • Preference will be given to Solutions able to collect data unattended from the greatest number of fields in the largest total area
25
Safety
  • Operation in winds of up to 20mph
  • Utilize data connection and/or locally collected data to determine if weather conditions are safe for flight
  • Long intervals between needed service/failure
  • Drone must be able to receive a signal mid-flight to return to a safe location
10

 

Winners will retain intellectual property rights to the solutions they develop to help farmers use drone technology more effectively.

 

Be hurry, the competence could be hard («Northrop Grumman Wants to Sell Unmanned Drones to Farmers?»)

 

 

Photo: Northrop Grumman