Automatic expert system based on images for accuracy crop row detection in maize fields

  • Authors:

  • J. M. Guerrero;M. Guijarro;M. Montalvo;J. Romeo;L. Emmi;A. Ribeiro;G. Pajares

  • Affiliations:

  • Dept. Software Engineering and Artificial Intelligence, Faculty of Computer Science, Complutense University, 28040 Madrid, Spain;Dept. Software Engineering and Artificial Intelligence, Faculty of Computer Science, Complutense University, 28040 Madrid, Spain;Dept. Computer Architecture and Automatic Control, Faculty of Computer Science, Complutense University, 28040 Madrid, Spain;Dept. Software Engineering and Artificial Intelligence, Faculty of Computer Science, Complutense University, 28040 Madrid, Spain;Center for Automation and Robotics (CAR), CSIC-UPM, Arganda del Rey, Spain;Center for Automation and Robotics (CAR), CSIC-UPM, Arganda del Rey, Spain;Dept. Software Engineering and Artificial Intelligence, Faculty of Computer Science, Complutense University, 28040 Madrid, Spain

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2013

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Abstract

This paper proposes an automatic expert system for accuracy crop row detection in maize fields based on images acquired from a vision system. Different applications in maize, particularly those based on site specific treatments, require the identification of the crop rows. The vision system is designed with a defined geometry and installed onboard a mobile agricultural vehicle, i.e. submitted to vibrations, gyros or uncontrolled movements. Crop rows can be estimated by applying geometrical parameters under image perspective projection. Because of the above undesired effects, most often, the estimation results inaccurate as compared to the real crop rows. The proposed expert system exploits the human knowledge which is mapped into two modules based on image processing techniques. The first one is intended for separating green plants (crops and weeds) from the rest (soil, stones and others). The second one is based on the system geometry where the expected crop lines are mapped onto the image and then a correction is applied through the well-tested and robust Theil-Sen estimator in order to adjust them to the real ones. Its performance is favorably compared against the classical Pearson product-moment correlation coefficient.