An Active Seeding-Depth Control System Based on Multisource Sensing and Collaborative Control

To improve the consistency of the downforce exerted by the seeding row unit and the stability of seeding depth, an active seeding-depth control method based on the coordinated operation of the row-cleaning and seeding units was proposed for high-speed no-tillage seeding. An STM32 microcontroller-based active seeding-depth control system was developed by integrating modules for soil-surface profile sensing, gauge wheel–soil contact pressure measurement, pneumatic actuation, wireless data transmission, and human–machine interaction. The coordinated control system uses the operating state of the front-mounted row-cleaning unit as preview information and employs a machine-learning model to simultaneously predict the feedforward downforce required by the seeding unit and the feedback downforce required by the row-cleaning unit. A fuzzy controller for the row-cleaning unit and a PID controller for the seeding unit are then coordinated to achieve integrated control combining anticipatory row-cleaning regulation with precise compensation during furrow opening. To support model prediction and coordinated control, a multi-sensor synchronous data-acquisition platform was developed, and downforce prediction models were established. Based on their performance on the test sets, the Backpropagation neural network and Random Forest models achieved the best predictive performance for the two sample categories, respectively, and were therefore selected for implementation in the coordinated control strategy. On the test sets, the two optimal prediction models achieved coefficients of determination (R2) of 0.95 and 0.98 and RMSE values of 2.07 and 21.17, respectively, demonstrating high predictive accuracy. Field experiments showed that, at operating speeds of 8–12 km·h−1, the system achieved a seeding-depth qualification rate of 84.5–87.3% and a coefficient of variation in seeding depth of 12.7–15.9%. These results provide a technical basis for the intelligent control of high-speed precision no-tillage seeders.

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Publication Details

Journal
Agronomy
Published
2026-10-05
DOI
https://doi.org/10.3390/agronomy16191948
Primary Topic
Agricultural Engineering and Mechanization
Type
article
Field-Weighted Citation Impact
0.00
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An Active Seeding-Depth Control System Based on Multisource Sensing and Collaborative Control

Feng Shi, Ze Liu, Jun Yuan, Hongzheng Zhang et al.
Agronomy
Agricultural Engineering and Mechanization
article

An Active Seeding-Depth Control System Based on Multisource Sensing and Collaborative Control

Feng Shi, Ze Liu, Jun Yuan, Hongzheng Zhang, Dongyan Huang, Shengxian Wu, Libo Sun, Xinbo Zhang
article en

Abstract

To improve the consistency of the downforce exerted by the seeding row unit and the stability of seeding depth, an active seeding-depth control method based on the coordinated operation of the row-cleaning and seeding units was proposed for high-speed no-tillage seeding. An STM32 microcontroller-based active seeding-depth control system was developed by integrating modules for soil-surface profile sensing, gauge wheel–soil contact pressure measurement, pneumatic actuation, wireless data transmission, and human–machine interaction. The coordinated control system uses the operating state of the front-mounted row-cleaning unit as preview information and employs a machine-learning model to simultaneously predict the feedforward downforce required by the seeding unit and the feedback downforce required by the row-cleaning unit. A fuzzy controller for the row-cleaning unit and a PID controller for the seeding unit are then coordinated to achieve integrated control combining anticipatory row-cleaning regulation with precise compensation during furrow opening. To support model prediction and coordinated control, a multi-sensor synchronous data-acquisition platform was developed, and downforce prediction models were established. Based on their performance on the test sets, the Backpropagation neural network and Random Forest models achieved the best predictive performance for the two sample categories, respectively, and were therefore selected for implementation in the coordinated control strategy. On the test sets, the two optimal prediction models achieved coefficients of determination (R2) of 0.95 and 0.98 and RMSE values of 2.07 and 21.17, respectively, demonstrating high predictive accuracy. Field experiments showed that, at operating speeds of 8–12 km·h−1, the system achieved a seeding-depth qualification rate of 84.5–87.3% and a coefficient of variation in seeding depth of 12.7–15.9%. These results provide a technical basis for the intelligent control of high-speed precision no-tillage seeders.

AgronomyVol. 16(19)
Jilin Agricultural University (CN)
Openalex Percentile: Top 21%
Agricultural Engineering and Mechanization
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