Yield mapping with digital aerial color infrared (CIR) images

Sreekala Gopalapillai, Lei Tian, Donald Bullock

Research output: Contribution to journalConference articlepeer-review


Yield potential was predicted and mapped for three corn fields in Central Illinois, using digital aerial color infrared images. Three methods, namely statistical (regression) modeling, genetic algorithm optimization and artificial neural networks, were used for developing yield models. Two image resolutions of 3 and 6 m/pixel were used for modeling. All the models were trained using July 31 image and tested using images from July 2 and August 31, all from 1998. Among the three models, artificial neural networks gave best performance, with a prediction error less than 30%. The statistical model resulted in prediction errors in the range of 23 to 54%. The lower resolution images resulted in better prediction accuracy compared to resolutions higher than or equal to the yield resolution. Images after pollination resulted in better accuracy compared to images before pollination.

Original languageEnglish (US)
JournalSAE Technical Papers
StatePublished - Sep 14 1999
EventInternational Off-Highway and Powerplant Congress and Exposition - Indianapolis, IN, United States
Duration: Sep 13 1999Sep 15 1999

ASJC Scopus subject areas

  • Automotive Engineering
  • Safety, Risk, Reliability and Quality
  • Pollution
  • Industrial and Manufacturing Engineering


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