**1. Introduction**

The feeding rate is an important factor affecting the overall performance of a silage harvester. The increase in the feeding rate can significantly improve the harvesting efficiency of a silage harvester [1]. However, if the feeding rate is too high, on the one hand, maize plants will be fed too fast, which may easily cause blockage and failure in the working part, especially the header, and throwing components in the harvester [2]. In this way, the harvesting progress is affected by the follow-up manual fault clearing. On the other hand, due to the excessive feeding volume, the whole machine will be in overloaded harvesting conditions for a long time, which may cause fatigue damage to the working parts and engine [3], thus reducing the overall machine service life. Conversely, when the feeding rate is too small, apart from directly reducing the harvesting efficiency, it may affect the shredding performance of the silage harvester, resulting in the average length of silage cutting or the uniformity of the cutting length not meeting the expected requirements. The ideal field operation state of the silage harvester is to maintain a stable

**Citation:** Wang, F.; Wang, J.; Ji, Y.; Zhao, B.; Liu, Y.; Jiang, H.; Mao, W. Research on the Measurement Method of Feeding Rate in Silage Harvester Based on Components Power Data. *Agriculture* **2023**, *13*, 391. https://doi.org/10.3390/agriculture 13020391

Academic Editor: Massimo Cecchini

Received: 15 January 2023 Revised: 1 February 2023 Accepted: 4 February 2023 Published: 7 February 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

and optimal feeding rate within the rated range on the premise of ensuring a sufficient chopping performance [4].

Currently, scholars have carried out a significant amount of research on the detection method of feeding rate in forage harvesters [5–7]. According to the detection principle, this can mainly be divided into mass flow type and volume flow type. The mass flow method mainly realizes the online detection of the feeding rate through indirect modeling by monitoring the machine's working conditions, such as speed, torque, power, pressure, capacitance, etc. [8–11]. This method has been extensively studied on grain combine harvesters and has achieved good field verification results [12–14]. However, for silage harvesters, which use whole-plant intake, the material composition and machine-crop interactions are more complex [15], and there is a lack of sufficient research on airborne applications, the existing monitoring system for a forage harvester mainly realizes the measurement of conventional parameters, such as location, rotation speed, cutter height, engine data, feeding metal, blade sharpness, etc. Most of the research applications focus on the display and early warning for the machine status, while the relationship among monitoring parameters is relatively independent, and there is a need for a better feeding rate monitoring method based on correlation analysis and multiple-parameter fusion modeling [16,17]. The volume flow detection is mainly detected by the opening of the feed inlet, without considering the influence of moisture content and material density on the model, in which repeated calibration is required [18], and further research is required. According to the model's methodology, as for the mathematical relationship between the power consumption and feeding rate for a crop combine harvester, the primary, quadratic, and exponential regression models are frequently applied, and it was shown that the measurement between feeding rate and working torque or working power is better modeled by using the primary linear regression model [19].

In consideration of the shortcomings in the existing research and the demands in feed rate monitoring, this paper proposes a measurement method based on multi-power data fusion regression for a maize silage harvester. The mechanism principle and operation process of the silage harvesting machine is analyzed, and strain resistance principles and pressure flow principles are used to design real-time power monitoring sensors for key operation components. Highly reliable pre-processing algorithms for dynamic field data are studied, a one-variable linear model and multiple-variable fusion regression model based on a correlation analysis is established to measure the feeding rate, and field harvesting experiments are carried out to validate the relevant sensors, processing algorithms, and measurement models.
