Design and experimental validation of a flow-guided weighing-based grain mass flow sensor for wheat combine harvester

Abstract

The grain flow sensor is a core component for achieving precise online yield measurement in combine harvesters. However, the accuracy and stability of sensor monitoring are affected by factors such as the harvester structure, operational vibrations, dust, and electromagnetic interference. Improving sensor performance under these constraints has long been a key research focus in the industry. To enhance the monitoring accuracy and stability of grain mass flow rate in combine harvesters, this study proposes a flow-guided and weighing-based grain mass flow sensor (GMFS), which incorporates flow-guiding, buffering, and flow-stabilising functions. Based on dynamic analysis, a multivariable dynamic monitoring model for the grain mass flow rate was developed, integrating key parameters such as screw conveyor speed and grain weight. Dedicated circuits for weak signal amplification, noise filtering, and signal acquisition were developed, and an improved Kalman filter (KF) was employed to enhance monitoring precision. Building on this foundation, a GMFS prototype was integrated into combine harvesters and tested, and its buffering and deceleration performance was validated through simulations. The simulation results indicated that the average particle velocity magnitude at the GMFS outlet was lower and more stable than at the inlet, confirming the sensor’s effectiveness in buffering and decelerating grain flow. Bench test results showed that, under varying mass flow conditions, the GMFS achieved a signed mean relative error (MRE) of 0.64 % and a root mean square error (RMSE) of 0.041 kg·s⁻¹ for the steady-segment (Δt₁) flow rate, with 95 % confidence intervals (CIs) reported for the MRE. Dynamic tests on a combine harvester demonstrated comparable monitoring accuracy, with an MRE of 0.97 % (95 % CI: 0.31–1.63 %) and an RMSE of 0.107 kg·s⁻¹ for the steady-segment flow rate, and an MRE in full-interval total mass of –0.28 % (mean absolute error: 1.10 %). This study provides an accurate and stable sensor design for real-time monitoring of grain mass flow in combine harvesters and provides robust support for yield monitoring technology.

Plant phenotyping relevance

コンバイン収穫時の穀粒質量流量（収量）をリアルタイム計測するセンサーを設計・実装し、シミュレーション、ベンチ試験、実機試験で精度を検証しており、植物の収量形質取得法が中心的である。

Code and data availability

公開状態または取得可能な本文経路を確認できませんでした。

No evidence-backed public reproduction asset is currently recorded.