Abstract
Autonomy in agricultural machinery is an important need for smart and precision farming. The productivity and accuracy of farm machinery can be drastically increased by employing autonomous path planning systems for the tedious task of manually steering vehicles which would thereby save the time of producers and reduce driver fatigue. The goal of this paper is to develop a stereo vision-based path planning system for an autonomous onion harvester. The proposed solution consists of identifying the crop rows through deep learning-based semantic segmentation of images streamed through a stereo vision camera. Segmentation information is used along with image depth map data to determine navigation waypoints for the harvester to traverse. The developed system was designed and tested on an Indian onion field dataset which gives considerably good results.
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Sinalkar, S., Nair, B.B. (2020). Stereo Vision-Based Path Planning System for an Autonomous Harvester. In: Reddy, V., Prasad, V., Wang, J., Reddy, K. (eds) Soft Computing and Signal Processing. ICSCSP 2019. Advances in Intelligent Systems and Computing, vol 1118. Springer, Singapore. https://doi.org/10.1007/978-981-15-2475-2_46
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DOI: https://doi.org/10.1007/978-981-15-2475-2_46
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