Evaluation of a stereo vision system for cotton row detection and boll location estimation in direct sunlight
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Date
2020-08-05
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
Abstract
Cotton harvesting is performed by using expensive combine harvesters which makes it
difficult for small to medium-size cotton farmers to grow cotton economically. Advances in robotics
have provided an opportunity to harvest cotton using small and robust autonomous rovers that can be
deployed in the field as a “swarm” of harvesters, with each harvester responsible for a small hectarage.
However, rovers need high-performance navigation to obtain the necessary precision for harvesting.
Current precision harvesting systems depend heavily on Real-Time Kinematic Global Navigation
Satellite System (RTK-GNSS) to navigate rows of crops. However, GNSS cannot be the only method
used to navigate the farm because for robots to work as a coordinated multiagent unit on the same
farm because they also require visual systems to navigate, avoid collisions, and to accommodate
plant growth and canopy changes. Hence, the optical system remains to be a complementary method
for increasing the efficiency of the GNSS. In this study, visual detection of cotton rows and bolls
was developed, demonstrated, and evaluated. A pixel-based algorithm was used to calculate and
determine the upper and lower part of the canopy of the cotton rows by assuming the normal
distribution of the high and low depth pixels. The left and right rows were detected by using
perspective transformation and pixel-based sliding window algorithms. Then, the system determined
the Bayesian score of the detection and calculated the center of the rows for the smooth navigation of
the rover. This visual system achieved an accuracy of 92.3% and an F1 score of 0.951 for the detection of
cotton rows. Furthermore, the same stereo vision system was used to detect the location of the cotton
bolls. A comparison of the cotton bolls’ distances above the ground to the manual measurements
showed that the system achieved an average R2 value of 99% with a root mean square error (RMSE)
of 9 mm when stationary and 95% with an RMSE of 34 mm when moving at approximately 0.64 km/h.
The rover might have needed to stop several times to improve its detection accuracy or move more
slowly. Therefore, the accuracy obtained in row detection and boll location estimation is favorable for
use in a cotton harvesting robotic system. Future research should involve testing of the models in a
large farm with undefoliated plants.
Description
Journal article
Keywords
3D position estimation, GNSS, ROS, Robotics, Machine vision, Row detection, Cotton