Establishment of a Feeding Rate Prediction Model for Combine Harvesters
Zhenwei Liang,
Yongqi Qin,
Zhan Su
Abstract:Feeding rates serve as a vital indicator for adjusting the working parameters of the combine harvester. A non-invasive diagnostic approach to predicting the feed rates of combine harvesters by collecting vibration signals of the inclined conveyor was introduced in this study. To establish a feed rate prediction model, the correlation between feeding rates and vibration signal characteristics was investigated. Vibration signal characteristics in both the time domain and frequency domain were also analyzed in de… Show more
In order to solve the problems of bottom pod leakage and soil removal by header, a soybean header profiling system was designed in this paper. The cutter height off-ground detection device was installed on both sides of the header, and the cutter distance from the ground was represented by the angle sensor turning when the profiling wheel met the rolling ground. The hydraulic electromagnetic reversing valve was installed so that the profiling system could automatically control the lifting of the header, the unilateral power of the solenoid valve was 0.15 s, and the height of the cutter from the ground was changed by 10 mm. The height of the cutter off the ground was set to 80 mm, and the adjustment range of the soybean header profiling system was 45–125 mm. The test results showed that the maximum absolute error of the cutter off the ground height detection device was 5.98 mm, the minimum absolute error was 1.00 mm, and the relative error was 0.038. The cutter height adjustment device was powered for 0.15 s, and the average adjustment distance was 11.158 mm. The soybean header profiling system did not shovel soil during field harvest, and the stubble height of 85% of soybean plants was less than 10 mm from the set height after harvest. The results showed that the soybean header profiling system could effectively adjust the cutter height from the ground so that the cutter height from the ground was kept at 80 mm. This study could provide a reference for the intelligent design of soybean harvesters.
In order to solve the problems of bottom pod leakage and soil removal by header, a soybean header profiling system was designed in this paper. The cutter height off-ground detection device was installed on both sides of the header, and the cutter distance from the ground was represented by the angle sensor turning when the profiling wheel met the rolling ground. The hydraulic electromagnetic reversing valve was installed so that the profiling system could automatically control the lifting of the header, the unilateral power of the solenoid valve was 0.15 s, and the height of the cutter from the ground was changed by 10 mm. The height of the cutter off the ground was set to 80 mm, and the adjustment range of the soybean header profiling system was 45–125 mm. The test results showed that the maximum absolute error of the cutter off the ground height detection device was 5.98 mm, the minimum absolute error was 1.00 mm, and the relative error was 0.038. The cutter height adjustment device was powered for 0.15 s, and the average adjustment distance was 11.158 mm. The soybean header profiling system did not shovel soil during field harvest, and the stubble height of 85% of soybean plants was less than 10 mm from the set height after harvest. The results showed that the soybean header profiling system could effectively adjust the cutter height from the ground so that the cutter height from the ground was kept at 80 mm. This study could provide a reference for the intelligent design of soybean harvesters.
In complex field environments, wheat grows densely with overlapping organs and different plant weights. It is difficult to accurately predict feed quantity for wheat combine harvester using the existing YOLOv5s and uniform weight of a single wheat plant in a whole field. This paper proposes a feed quantity prediction method based on the improved YOLOv5s and weight of a single wheat plant without stubble. The improved YOLOv5s optimizes Backbone with compact bases to enhance wheat spike detection and reduce computational redundancy. The Neck incorporates a hierarchical residual module to enhance YOLOv5s’ representation of multi-scale features. The Head enhances the detection accuracy of small, dense wheat spikes in a large field of view. In addition, the height of a single wheat plant without stubble is estimated by the depth distribution of the wheat spike region and stubble height. The relationship model between the height and weight of a single wheat plant without stubble is fitted by experiments. Then, feed quantity can be predicted using the weight of a single wheat plant without stubble estimated by the relationship model and the number of wheat plants detected by the improved YOLOv5s. The proposed method was verified through experiments with the 4LZ-6A combine harvester. Compared with the existing YOLOv5s, YOLOv7, SSD, Faster R-CNN, and other enhancements in this paper, the mAP50 of wheat spikes detection by the improved YOLOv5s increased by over 6.8%. It achieved an average relative error of 4.19% with a prediction time of 1.34 s. The proposed method can accurately and rapidly predict feed quantity for wheat combine harvesters and further realize closed-loop control of intelligent harvesting operations.
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