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ORIGINAL RESEARCH article

Front. Artif. Intell.
Sec. AI in Food, Agriculture and Water
Volume 7 - 2024 | doi: 10.3389/frai.2024.1435686

Real-Time Crop Row Detection Using Computer Vision and Application in Agricultural Robot

Provisionally accepted
Md N. Khan Md N. Khan 1Adibuzzaman Rahi Adibuzzaman Rahi 2*Veera P. Rajendran Veera P. Rajendran 3Mohammad Al Hasan Mohammad Al Hasan 2Sohel Anwar Sohel Anwar 2
  • 1 Other, Cincinnati, OH, United States
  • 2 Indiana University, Purdue University Indianapolis, Indianapolis, United States
  • 3 Other, Mooresville, IN, United States

The final, formatted version of the article will be published soon.

    The goal of achieving autonomous navigation for agricultural robots poses significant challenges, mostly arising from the substantial natural variations in crop row images as a result of weather conditions and the growth stages of crops. The processing of the detection algorithm also must be significantly low for real-time applications. In order to address the aforementioned requirements, we propose a crop row detection algorithm that has the following features: Firstly, a projective transformation is applied to transform the camera view and a color-based segmentation is employed to distinguish crop and weed from the background.Secondly, a clustering algorithm is used to differentiate between the crop and weed pixels. Lastly, a robust line-fitting approach is implemented to detect crop rows. The proposed algorithm is evaluated throughout a diverse range of scenarios, and its efficacy is assessed in comparison to four distinct existing solutions. The algorithm achieves an overall intersection over union (IOU) of 0.73 and exhibits robustness in challenging scenarios with high weed growth. The experiments conducted on real-time video featuring challenging scenarios show that our proposed algorithm 1 Khan et al.exhibits a detection accuracy of over 90% and is a viable option for real-time implementation.

    Keywords: Crop Row Detection, Precision farming, Agricultural robot, unsupervised learning, real-time application

    Received: 27 May 2024; Accepted: 18 Sep 2024.

    Copyright: © 2024 Khan, Rahi, Rajendran, Al Hasan and Anwar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Adibuzzaman Rahi, Indiana University, Purdue University Indianapolis, Indianapolis, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.