Missed_maize_tassel_recognition

Leveraging AI in Hybrid Seed Production

Case Study: Innovation in corn hybrid seed production

Innovation in corn hybrid seed production: Identifying missed tassels during detasseling to increase genetic purity, decrease cost and prevent lost revenue

Hybrid corn seed production problems

  • High chances of losing a field due to missed tassels in female inbred rows
  • High cost of manual detasseling due to reruns
  • The procedure must be carried out in a very limited time with the high accuracy

CHALLENGE

Machine detasseling only results in around 85% pulling vs the needed 99.7% for hybrid corn seeds

Any tassel not removed during the detasseling phase of hybrid seed corn production can result in unwanted pollination, degrading the necessary genetic purity, quality of seeds thus impacting its commercial market value.

SOLUTION

  1. Drone images uploaded to Proofminder platform. We collected data from 5 distinct fields and multiple corn varieties
  2. Field visualization. Orthomosaic of the plot is automatically created in the system
  3. High precision stand count readily established
  4. Male and female lines are distinguished based on phenology. Sowing structure, lines, distances between lines and individual plants identified without reliance on sowing or other external data. Male / female distinction allows to ‘ignore’ male tassels during missed tassels identification phase
  5. Clear report ready. Actionable insights on the palm of a hand
  6. Yield saved. With timely actions of the agronomist cost of detasseling decreased up to 1/3

DETAILS

Data Collection Approach:

  • Drone camera angle set at 60 degrees not at nadir – much larger plant area visible, training and verification is easier
  • Our algorithm calculates tassel location with 5-10cm accuracy based on drone location
  • Off-the-shelf drones (DJI Phantom, Matrice 210 v2 + Zenmuse X5S, etc.) and flight planning software (DJI Ground Station Pro) used
  • Above 100 hectares per drone per day coverage and processing possible

Identification of missed tassels:

  • Tassels categorized and predicted per size (S-XL)
  • Male tassels excluded, leveraging row identification algorithm
  • Identification is infinitely scalable using cloud resources – processing of 1 hectare on 1 node under 30 minutes
  • Identified missed tassels visualized in the Proofminder application on desktop/tablet/mobile but can be exported into geojson or other common geospatial formats

Implement precise detasseling use case on your farm

Avoid losing a field due to missed tassels

Get higher genetic purity of the hybrid maize seeds

Decrease cost of detasseling up to 1/3 with less labor hours

TAKE CONTROL OF DETASSELING WITH DATA AND AI

Book a demo to implement precise detasseling use case on your farm!