# Wheat Quantity Monitoring Methods Based on Inventory Measurement and SVR Prediction Model
## 1 Introduction
Additionally, there are specific requirements for the electronic equipment used in the measurement process, and challenges related to the depth of antenna insertion in the grain pile and the alignment between antennas must be addressed. Further research is needed to assess the practicality of this method [12,13]. The wide-beam radar detection method utilizes three antennas to acquire the Cartesian coordinates of strong scattering points on the surface of a grain pile. This method also gathers three-dimensional height information and calculates the corresponding grain volume using the relevant formula [14]. However, the measurement of grain volume is greatly influenced by the reflection of the wall facing the radar wave in the closed space of the granary. This necessitates the removal of numerous anomalies, which in turn requires high hardware capabilities for real-time processing. The grain volume measurement method based on machine vision involves acquiring and calculating images through a camera on the granary site. However, the current camera hardware equipment is unable to meet the required detection accuracy for large granaries, limiting its application to small silos. To conduct measurements in large granaries, multiple cameras need to be coordinated [15,16]. Additionally, the use of gas fumigation for insecticide treatment in grain warehouses necessitates higher cleaning requirements for the cameras. Factors like uneven lighting in the field environment also hinder the widespread implementation of grain volume measurement methods based on machine vision [17; 18]. The 3D laser scanning measurement method employs a self-developed high-precision 3D rotation measurement mechanism to automatically measure the length and width of the grain bin [19]. This method utilizes an adaptive measurement scheme by meshing and scanning the surface of the grain pile to obtain the morphology information. It replaces the outdated practice of manual laser single point measurement and enables automatic and accurate measurement of the grain pile volume in the bin. The 3D laser scanning measurement method is currently widely used in grain depot measurement. This method has led to the development of handheld, portable, and network 3D laser scanning measurement instruments based on the same measurement principle [20]. The current method of grain quantity inspection in grain depots is indirect measurement. However, this method also faces two key problems. Firstly, there is a concern about the accuracy of measuring the volume of grain piles. Secondly, accurately estimating the average density of the grain pile is another challenge. With the advancement of advanced volume measurement technology, the accuracy of grain pile volume measurement is continually improving. This improvement has resulted in the ability to meet the actual requirements of measuring grain depot volume. The average density of a grain pile is determined by multiplying the measured grain density by the correction coefficient. The accuracy of grain quantity measurement relies on correcting the average density of the grain pile. As there is currently no accurate law governing the change in average density of the grain pile, we have to rely on the modified coefficient criterion mentioned above for manual estimation. However, this method of estimating the density of two piles is heavily influenced by human factors, which significantly impacts the credibility of inspections conducted by grain management departments [21]. Hence, it is crucial to develop a method for predicting grain density using the available grain storage data. In recent years, the rapid development of machine-learning algorithms has provided new solutions for solving problems that cannot be described by specific mathematical formulas. The measurement of grain stack density in grain depots is a complex technical process, and current research has not yet identified a definitive compensation law for grain stack average density.