Evaluation of a Stereo Vision System for Cotton Row Detection and Boll Location Estimation in Direct Sunlight

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.

Crop Row Segmentation and Detection in Paddy Fields Based on Treble-Classification Otsu and Double-Dimensional Clustering Method

“…Thus, some restrictions should be imposed on the judgement of feature points. To prove that it is reliable to recognize a certain area as a feature point, the information of green crops attached to the area should be relatively bigger; thus, the S k (i) of the area should be higher than a given threshold T(k), as shown in Equation (22).…”

“…Zhai et al [1] developed a multi-crop-row detection algorithm to locate the three-dimensional (3D) position of crop rows according to their spatial distribution. Fue et al [22] utilized stereo vision to determine 3D boll location and row detection, and the performance of this method showed promise as a method to assist with the real-time kinematic global navigation satellite system (RTK-GNSS) navigation. Adhikari et al [23] trained a deep convolutional encoder decoder network to detect crop lines using semantic graphics.…”

Towards Infield Navigation: leveraging simulated data for crop row detection

“…Individual work has been done to pursue various challenges in implementing a vision based navigation algorithms in crop rows. Researchers have attempted to address the effects of weed density, growth stages, shadows and discontinuities in crop row detection [4], [5]. While these attempts indicate successful solutions to each of those problems separately, the need of a generic algorithm arises in practical implementations in such systems.…”

Pure pursuit method use to control unmanned motor grader

“…One of the wellknown methods of controlling the self-driving cars is the «pure pursuit» method 1,2 . This method is used to control mobile robots 3 [2,3,4], unmanned vehicles 4,5 [5], agricultural machines [6], logging harvesters [7], underwater uninhabited vehicles [8], etc. However, there are no studies of this method when driving a grader.…”