Conventional seeding monitors often exhibit diminished accuracy under challenging field conditions. To address this, this 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. A sophisticated Signal Feature Identification Algorithm (SFIA) was developed 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.

Subsoiling is an effective tillage technique for alleviating soil compaction, but the high traction resistance encountered at deeper working depths constrains its widespread application. To address this issue, a self-excited and forced intelligent vibrating subsoiler was developed. The subsoiler is equipped with a compound vibration mechanism that can adaptively switch between self-excited vibration and forced vibration modes based on real-time monitoring of soil resistance. Field experiments were conducted to evaluate the performance of the self-excited and forced vibrating subsoiling (SEFV). These experiments compared its performance with conventional subsoiling (CS) and self-excited vibrating subsoiling (SEV) at different working depths (35-45 cm) and forward speeds (2 and 4 km/h). The results showed that at 2 km/h, SEFV operated in self-excited vibration mode and reduced traction resistance by 12.4%-13.1% compared to CS, with no significant difference from SEV. At 4 km/h, the resistance reduction effect of SEFV became more pronounced with increasing depth. At 45 cm depth, SEFV reduced traction resistance by 9.9% and 18.9% compared to SEV and CS, respectively, as it switched to forced vibration mode to overcome the high soil resistance. SEFV also maintained high subsoiling depth stability (>90%) at both speeds and all depths tested, demonstrating its advantage over SEV under high resistance conditions. The intelligent control system based on resistance feedback enabled the SEFV to automatically adapt to variable soil conditions and optimize its vibration behavior for improved subsoiling performance and energy efficiency. This study provides new insights into the design of adaptive vibrating subsoilers for enhanced tillage operations.