Overcurrent-driven LEDs for consistent image colour and brightness in agricultural machine vision applications

Omeed Mirbod, Daeun Choi, Roderick Thomas, Long He

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Machine vision systems are being utilized extensively in agriculture applications. Daytime imaging in outdoor field conditions presents challenges such as variable lighting and colour inconsistencies due to sunlight. Motion blur can occur due to vehicle movement and vibrations from ground terrain. A camera system with active lighting can be a solution to overcome these challenges. In this study, the usage of over-current driven LEDs to produce a powerful flash was investigated as a viable light source for daytime imaging. The current drawn by an LED was increased by a factor of six times its normal rating resulting in increased illuminance. A circuit was designed for storing and releasing energy to the LEDs for a strobe-like effect and a controller was used for synchronizing the strobe with a camera to acquire images. The system was deployed in an apple orchard on three days in summer of 2020. Images were taken throughout the day in both sunny and cloudy conditions of different canopy structures. There was substantial improvement in image brightness and colour consistency by using the LED flashes. Images captured by the prototype system during an 11-hour period showed an average decrease of 85% in standard deviation for the Hue-Saturation-Value (HSV) channels compared to that of the auto-exposure setting. Additionally, the prototype system was able to fix motion blur in images averaging 7 mm in error for a stereo vision application with the camera moving at 7 km/hr. These results show that the designed LED flash system can reduce the undesirable effects of lighting variability and motion blur in images stemming from outdoor field conditions.

Original languageEnglish (US)
Article number106266
JournalComputers and Electronics in Agriculture
StatePublished - Aug 2021

All Science Journal Classification (ASJC) codes

  • Forestry
  • Agronomy and Crop Science
  • Computer Science Applications
  • Horticulture


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