Artificial-Intelligence-Enabled Robotic Navigation Using Crop Row Detection Based Multi-Sensory Plant Monitoring System Deployment
AdvisorsHussain, Muhammad Mustafa
Permanent link to this recordhttp://hdl.handle.net/10754/670240
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AbstractThe ability to detect crop rows and release sensors in large areas to ensure homogeneous coverage is crucial to monitor and increase the yield of crop rows. Aerial robotics in the agriculture field helps to reduce soil compaction. We report a release mechanics system based on image processing for crop row detection, which is essential for field navigation-based machine vision since most plants grow in a row. The release mechanics system is fully automated using embedded hardware and operated from a UAV. Once the crop row is detected, the release mechanics system releases lightweight, flexible multi-sensory devices on top of each plant to monitor the humidity and temperature conditions. The capability to monitor the local environmental conditions of plants can have a high impact on enhancing the plant’s health and in creasing the output of agriculture. The proposed algorithm steps: image acquisition, image processing, and line detection. First, we select the Region of Interest (ROI) from the frame, transform it to grayscale, remove noise, and then skeletonize and remove the background. Next, apply a Hough transform to detect crop rows and filter the lines. Finally, we use the Kalman filter to predict the crop row line in the next frame to improve the performance. This work’s main contribution is the release mechanism integrated with embedded hardware with a high-performance crop row detection algorithm for field navigation. The experimental results show the algorithm’s performance achieved a high accuracy of 90% of images with resolutions of (900x470) the speed reached 2 Frames Per Second (FPS).
CitationAlshanbari, R. (2021). Artificial-Intelligence-Enabled Robotic Navigation Using Crop Row Detection Based Multi-Sensory Plant Monitoring System Deployment. KAUST Research Repository. https://doi.org/10.25781/KAUST-14N5T