# Wheat Quantity Monitoring Methods Based on Inventory Measurement and SVR Prediction Model
## 1 Introduction
However, with the emergence of machine-learning algorithms, it is expected that the prediction of grain quantity in grain depots can be achieved by utilizing storage records and measurement data from existing grain depots and training models based on this data [22]. Currently, there are various types of machine-learning algorithms, categorized into supervised learning, unsupervised learning, and reinforcement learning. Unsupervised learning involves extracting valuable information and structure from unlabeled data. Unlike supervised learning, it does not rely on labeled datasets but instead utilizes inherent data patterns and structures for learning. Unsupervised learning finds applications in data dimensionality reduction, cluster analysis, and anomaly detection. Conversely, supervised learning involves training algorithms to learn patterns from labeled datasets and make inferences for new instances. This type of learning requires specific inputs and outputs, and the choice of training data is crucial. Some commonly used supervised-learning algorithms include BP neural network, support vector machine (SVM), nearest neighbor method, Bayes method, and decision tree. Among these, SVM stands out as one of the most effective algorithms and finds wide applications in various fields [23]. However, SVM also has some disadvantages. Firstly, SVM exhibits high computational complexity and requires a long training time when processing large-scale data. Secondly, SVM is sensitive to parameter selection, necessitating the adjustment of parameters to achieve optimal results. Additionally, SVM is more susceptible to data noise and outliers, making it prone to overfitting issues. SVM is most suitable for the training and recognition of small samples. SVM can not only solve classification and pattern recognition problems but also solve regression and fitting problems. On the basis of SVM classification, the insensitive loss function is introduced, and SVR is obtained [24]. Conversely, reinforcement learning offers the advantage of automatically learning feature representations. Unlike traditional machine-learning algorithms that rely on manual feature extraction, deep learning can automatically learn higher-level feature representations through multi-level network structures. This end-to-end learning approach makes deep learning more effective in handling complex tasks. Reinforcement learning performs well with large-scale data and complex tasks and possesses strong feature-learning and representation capabilities. However, reinforcement learning requires extensive training time, computing resources, and sufficient hardware support. In this study, the limited sample size aligned with the characteristics of small sample training and learning. The application of SVR prediction models has been observed in various domains, including soil hydraulic characteristic parameters prediction [25], bridge structural damage identification [26], wireless sensor network fault detection [27], flight delay prediction [28], liquor year identification [29], ozone prediction [30], fly ash carbon content prediction [31], and system reliability prediction [32]. Therefore, the SVR prediction model was selected as the method to predict the measurement results of grain quantity in the grain depot. In order to solve the problem of checking the quantity of grain depot, based on the in-depth analysis of the storage data of previous years, our study proposed building a wheat storage prediction model by combining the recorded data of grain storage, field laser volume measurement, and SVR to predict and identify the quantity of wheat grain storage in grain depot. This paper is organized into the following sections: Section 2 provides an introduction to the measurement and calculation method of grain quantity in grain depots. It also analyzes the existing problems in the current method through formula calculations. Section 3 focuses on the principle and evaluation index of the SVR prediction algorithm model.