Article ; Online: A high-precision strain seeding spacing monitoring system based on a combined bionic strain sensor and strain peak recognition algorithm
Computers and Electronics in Agriculture. 2023 Sept., v. 212 p.108061-
2023
Abstract: This study has discovered a novel monitoring quantity suitable for seeding spacing monitoring, which is the strain signal generated by the seed flow through the seed discharge port during the operation of the finger-clip seed metering device. The strain ... ...
Abstract | This study has discovered a novel monitoring quantity suitable for seeding spacing monitoring, which is the strain signal generated by the seed flow through the seed discharge port during the operation of the finger-clip seed metering device. The strain sensor output a dataset of approximately 30 electrical signals for each flow of the seed pick-up clip of the finger-clip seed metering device through the seed discharge port. The number of peak points in this data set is equivalent to the number of seeds sown, enabling accurate judgment of missed seeding or reseeding. This study addressed the problem that field vibrations tend to cause displacement of seed, which in turn triggers a change in strain direction. Through a combined bionic design, a scorpion body surface V-shaped crack structure (ultrasensitive sensing function) and a spider-web annular topology (omnidirectional equalization sensing function) were selected as bionic prototypes to develop a combined bionic strain sensor (CBSS). The test results showed that the CBSS combines the functional characteristics of both bionic structures and was more suitable for seed spacing monitoring. Compared to the conventional straight-line V-shaped crack strain sensor (CSS), CBSS improved the omnidirectional sensitivity by 21.54–35.57%. To mitigate the impact of ground frequency noise in the system causing fluctuations in the CBSS output signal, a strain peak recognition algorithm (SPRA) was developed using image processing techniques and Python. The SPRA effectively removed ground frequency noise using a gray-scale erosion algorithm and accurately recognized peak points in the electrical signal dataset through the Suzuki and CEV algorithms. Results showed that the SPRA reduced monitoring errors by 20.52% compared to the conventional direct data processing method (DDPM). Based on the CBSS and SPRA, a strain seeding spacing monitoring system (S-SMS) was developed. Bench tests revealed that the parameters of the combined bionic annular crack structure (crack depth h) of the CBSS significantly affected the monitoring error of the S-SMS, with optimal performance achieved at the h value of 82 μm. Field tests demonstrated that the CBSS accurately monitored the strain signal triggered by each seed with 98.51% accuracy, while the SPRA recognized missed seeding and reseeding with 98.64% and 98.42% accuracy, respectively. Compared to the conventional photoelectric diffuse reflection seeding monitoring system (P-SMS), the S-SMS reduced seeding monitoring errors in the field by 3.51%. This research can provide a new research path for the development of seeding spacing monitoring systems. |
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Keywords | Scorpiones ; agriculture ; algorithms ; data collection ; electronics ; sowing ; topology ; Seeding spacing monitoring ; Strain sensor ; Combined bionic design |
Language | English |
Dates of publication | 2023-09 |
Publishing place | Elsevier B.V. |
Document type | Article ; Online |
ZDB-ID | 395514-x |
ISSN | 0168-1699 |
ISSN | 0168-1699 |
DOI | 10.1016/j.compag.2023.108061 |
Database | NAL-Catalogue (AGRICOLA) |
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