Enhancing the accuracy of seeding operation monitoring by seeding monitoring system based on flexible pressure sensors and SFIA
Abstract
Conventional seeding monitors often exhibit diminished accuracy under challenging field conditions. To address this, our study introduces a novel monitoring system leveraging flexible pressure sensors integrated with a finger-clamp seed metering device. The core principle is that the passage of each seed-clamping finger over the seed outlet generates a distinct, continuous pressure signal profile. We developed a sophisticated Signal Feature Identification Algorithm (SFIA) that transforms this raw signal data into a one-dimensional image for analysis. By employing binarization and bilateral filtering, the SFIA effectively suppresses noise from field vibrations and extracts key topographical features, enabling precise quantification of seeding events through peak detection. The complete system, implemented using LabVIEW and Python, was rigorously evaluated in field trials. Under conventional tillage, the system achieved an overall monitoring accuracy of 96.55%, with reseeding and missed seeding detection accuracies of 98.96% and 98.55%, respectively. Critically, it maintained high performance in challenging no-till conditions, demonstrating 95.46% overall accuracy, with 98.35% for reseeding and 98.42% for missed seeding detection. This research validates a pressure-based sensing approach as a robust alternative to traditional methods, presenting a new technological pathway for developing high-precision seeding monitoring systems resilient to common agricultural interferences.
Keywords: seeding monitoring; pressure sensor; signal processing; feature extraction; no-till seeding
DOI: 10.25165/j.ijabe.20261903.10124
Citation: Song Y X, Gong J G, Zhao R, Wang Y J, Wang X G, Guo M Z, et al. Enhancing the accuracy of seeding operation monitoring by seeding monitoring system based on flexible pressure sensors and SFIA. Int J Agric & Biol Eng, 2026;19(3): 225–234.
References
[1] Qi J T, Jia H L, Li Y, Yu H B, Liu X H, Lan Y B, et al. Design and test of fault monitoring system for corn precision planter. Int J Agric & Biol Eng, 2015; 8（6）: 13–19.
[2] Kim S J, Lee H S, Hwang S J, Kim J H, Jang M K, Nam J S. Development of seeding rate monitoring system applicable to a mechanical pot-seeding machine. Agriculture, 2023; 13（10）: 2000.
[3] Zhang X Q, Li H S, Zhang S S. Design and analysis of laser photoelectric detection sensor. Microwave and Optical Technology Letters, 2021; 63（12）: 3092–3099.
[4] Tao X, Lu H B, Luo W S. A robust photoelectric angular position sensor especially for a steerable underground boring tool. Sensors and Actuators a-Physical, 2005; 120（2）: 311–316.
[5] Cleary T. Performance of dual photoelectric/ionization smoke alarms in full-scale fire tests. Fire Technology, 2010; 50（3）: 753–773.
[6] Ding Z Q, Zhao Y, Zhang G L, Zhong M L, Guan X H, Zhang Y J. Application of visual mechanical signal detection and loading platform with super-resolution based on deep learning. International Journal of Intelligent Systems, 2022; 37（10）: 7812–7836.
[7] Antoni J. Apports de la cyclostationnarité à l’analyse des signaux mécaniques. Mécanique & Industries, 2010; 11（1）: 57–68.
[8] Arregi A, Lertxundi A, Vegas O, García-Baquero G, Ibarluzea J, Anabitarte A, et al. Environmental noise exposure and sleep habits among children in a cohort from Northern Spain. International Journal of Environmental Research and Public Health, 2022; 19（23）: 16321.
[9] Gierz L, Paszkiewicz B K. PVDF piezoelectric sensors for seeds counting and coulter clogging detection in sowing process monitoring. Journal of Engineering, 2020; 2020: 1–7.
[10] Li X, Li Y B, Tang C R, Li Y S. Modulation recognition network of multi-scale analysis with deep threshold noise elimination. Frontiers of Information Technology & Electronic Engineering, 2023; 24（5）: 742–758.
[11] Nam H, Alouini M S. Effect of threshold quantization in opportunistic splitting algorithm. IEEE Communications Letters, 2011; 15（12）: 1394–1397.
[12] Zhao Y, Chen Q, Cao W G, Yang J, Xiong J, Gui G. Deep learning for risk detection and trajectory tracking at construction sites. IEEE Access, 2019; 7: 30905–30912.
[13] Wang J, Chen X, Fang D, Wu C Q, Yang Z, Xing T. Transferring compressive-sensing-based device-free localization across target diversity. IEEE Transactions on Industrial Electronics, 2015; 62（4）: 2397–2409.
[14] Dereich S, Vormoor C. The high resolution vector quantization problem with Orlicz norm distortion. Journal of Theoretical Probability, 2011; 24（2）: 517–544.
[15] Zhao X, Gao Z M, Feng T, Shah S, Shi W D. Continuous fine-grained arm action recognition using motion spectrum mixture models. Electronics Letters, 2014; 50（22）: 1633–1635.
[16] Fan H H, De P. High speed adaptive signal progressing using the delta operator. Digital Signal Processing, 2001; 11（1）: 3–34.
[17] Li H G, Shi Y, Zhang B C, Wang Y F. Superpixel-based feature for aerial image scene recognition. Sensors, 2018; 18（1）: 156.
[18] Wang H Z, Ma L. Image generation and recognition technology based on attention residual GAN. IEEE Access, 2023; 11: 61855–61865.
[19] Yu L. Application of infrared image detection based on high-resolution image processing in motion recognition. Optical and Quantum Electronics, 2024; 56（4）: 615.
[20] Klemm O, Lange H. Trends of air pollution in the Fichtelgebirge mountains, Bavaria. Environmental Science and Pollution Research, 1999; 6（4）: 193–199.
[21] Zhang W P, Zhao B, Gao S B, Ji Y X, Zhou L M, Niu K, Qiu Z M, Jin X. Online recognition of small vegetable seed sowing based on machine vision. IEEE Access, 2023; 11: 134331–134339.
[22] Wang Y, Jagota V, Makhatha M E, Kumar P. Vibration signal acquisition and computer simulation detection of mechanical equipment failure. Nonlinear Engineering, 2022; 11（1）: 207–214.
[23] Xu F J, Jia T W, Jing R R. Recognition of key information in non-stationary signals based on wavelet threshold denoising and back propagation neural network optimized by manta ray foraging optimization algorithm. IEEE Access, 2022; 10: 118156–118166.
[24] Zhao J L, Wang X G, Wang J X, Han Z W. 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; 212: 108061.
[25] Tang H. Design and mechanism analysis of ripple surface pickup finger maize precision seed metering device. Doctoral Dissertation. Northeast Agricultural University, 2019.
[26] Ren J. Design and test of swinging finger clip corn seed-metering device. Master’s Thesis. Northeast Agricultural University, 2021.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 International Journal of Agricultural and Biological Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
IJABE is an international peer reviewed, open access journal, adopting Creative Commons Copyright Notices as follows.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
