Remote Sensing Image Processing Algorithm for Assessing Plant Population at Emergence

An algorithm that processes aerial images to provide farmers with data on crop emergence needed for improving crop management decisions.

The Need

In the field of crop management, remote sensing technologies capturing soil and plant data through reflected light, provide farmers invaluable data to effectively manage crop yields. Currently, there are two type of remote sensing tools: passive and active. With passive systems, reflected sunlight is the source of energy measured, with such systems including satellite sensors, camera film or digital cameras placed on aircraft. Active sensors also measure reflected light, yet instead of relying on natural sunlight, emit their own source of light. Currently, existing active and passive systems lack the capability to both accurately and quickly to measure population emergence, as manual scouting is still the preferred method. This presents an efficiency challenge for crop management decision making.

The Technology

Researchers from the Ohio State University's College of Food, Agricultural and Environmental Sciences, have developed a software algorithm that seeks to aid agricultural industry productivity, by analyzing images captured by remote sensing devices. The algorithm is designed to process aerial images from remote sensing devices such as satellites, drones or manned aircraft, using the gathered data to accurately assess and quantify plant populations. By providing quantified, spatial visualization of crop emergence data, the algorithm eliminates the need for manual scouting to determine population emergence. Additionally, the algorithm's accuracy improves with higher resolution images.

Commercial Applications

  • Farming Assistance
  • Remote Assessment of Plant Populations


  • Assessment of crops through satellites and other remote sensing devices eliminates the need for manual scouting, saving time and labor, without decreasing quality of the assessment
  • Utilizing remote sensing devices will not only assist with managing existing plant populations, but will enable farmers to proactively determine potential new plant populations/areas
  • The TTM (Time-To-Market) is expected to be relatively short as testing and validation of the algorithm in the disclosure has been utilized with real world data, along with a user interface already developed

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