# Wheat Quantity Monitoring Methods Based on Inventory Measurement and SVR Prediction Model
## 5 Conclusions  
This study innovatively proposes a grain quantity monitoring method that combines wheat inventory measurement with the SVR prediction model. By learning wheat inventory information and measuring grain pile volume using a laser, combined with the trained SVR prediction model, it can greatly improve the human factor interference caused by the use of empirical formulas in the process of grain depot inspection. It improves the objectivity and accuracy of the inventory of grain depot. The model takes storage time, storage weight, storage moisture content, measured moisture content, measured bulk density, and measured net volume as training samples. Through experimental comparison, it is found that the SVR prediction model based on linear kernel function has the best prediction effect, and the average prediction accuracy of grain depot error rate reaches 93.2%.  
In this study, sample selection was a complex and time-consuming task, as the accuracy of sample acquisition directly impacted the reliability of the SVR prediction model. To ensure sample accuracy, we dedicated nearly two years to collecting samples, although the number obtained remains relatively limited. Nonetheless, based on the available samples, this study proposes an SVR prediction method that combines grain storage volume measurements with the recording parameters of grain storage. This approach eliminates the issue of individuals setting the correction coefficient for average grain storage density based on experience, presenting a novel method for scientifically evaluating grain storage quantity inspection and enhancing the existing means of inspection. Therefore, only the SVR prediction method, which is suitable for small sample training, was used for this research. In future studies, we plan to collect more sample data for grain storage forecasting and expand the sample characteristics. Once we have accumulated a sufficient amount of sample data, we will explore the use of deep-learning methods to address the problem. Deep learning has unique advantages in handling large samples and complex tasks, and it can potentially provide accurate predictions for grain quantity in grain storage.  
Software, Z.Z.; writing--original draft, Z.Z. and C.W.; writing--review and editing, Z.Z. and C.W. All authors have read and agreed to the published version of the manuscript. This paper was supported by the Open Project of Key Laboratory of Grain Information Processing and Control (KFJ)-2021-111); Zhongyuan Scholars Workstation Funded Project (224400510030); Zhongyuan Scholars Workstation (ZYGZZ2021060); Natural Science Program of the Henan Provincial Department of Education (22A440009); High-level Talents Research Start-up Fund Project of Henan University of Technology (2020B011); Natural Science Project of Zhengzhou Science and Technology Bureau (22 ZZRDZX07); Open Project of Henan Engineering Laboratory for Optoelectronic Sensing and Intelligent Measurement and Control (HELPSIMC-2020-005); Henan Provincial Science and Technology Research and Development Plan Joint Fund (222103810084); Science and Technology Research Plan Project of Jiyuan City in 2019 (19022011); and Henan Province Science and Technology Research Project (222102220100).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare they have no conflict of interest.