## 5 Intelligent Fault Diagnosis of Sensor Faults
### Artificial Intelligence-Based Fault Diagnosis Methods
#### 5.2.3 Machine Learning Methods  
There is no simple correspondence between fault types and fault characteristics due to the complexity and unpredictability of sensor systems. The fault diagnosis method based on machine learning can train support vector machine (SVM), artificial neural network (NN), and other machine learning algorithms using normal data and fault data, and then diagnose the fault of sensors. SVM is a machine learning algorithm that solves binary classification problems [100]; sensor fault diagnosis is based on small samples. Consequently, the SVM model is ideally suited for sensor fault diagnosis and has been widely implemented in the field [101; 102]. [PERSON] et al. [103] divided multiple fault combinations according to the correlation of faults. Theydeveloped multiple SVM models based on these fault combinations to combat the misdiagnosis caused by insufficient and unreliable training data in traditional SVM models. Each SVM model assigned positive and negative labels to fault samples, corrected various label combinations using DS theory, and then determined the fault type. Although the fault identification accuracy of this scheme has been enhanced, the DS-theory-based correction rate must be improved. [PERSON] et al. [104] proposed OS-LSSVM for sensor fault detection, which solved the problem of LS-SVM lacking sparsity, enabled online fault diagnosis, and increased calculation speed. The SVM method primarily solves the quadratic programming problem; however, the calculation method based on quadratic programming restricts real-time performance and is unsuitable for online fault diagnosis. [PERSON] et al. [105] proposed an LS-SVM model that reduced the computational complexity by transforming the quadratic programming problem into a linear equation. The model's execution time was reduced by 36.7% compared to the SVM model, and the diagnostic accuracy was also enhanced. In addition, [PERSON] et al. [106] proposed a KNN-FSVM fault diagnosis scheme, which trained SVM by only selecting boundary data, thereby reducing the need for computing and storage resources. This was a solution to the problem that the computing time of SVM increases exponentially with the amount of data, as do the requirements for computing and storage resources. While traditional SVM models can diagnose systems with a small number of data samples, as sensor performance becomes more complex, the types of sensor faults increase, and the accuracy of the classification model decreases. Therefore, the SVM-based fault diagnosis method is not suitable for use alone in the current diversified agricultural production mode. ANN is a mathematical model that simulates the mechanism by which the nervous system of the human brain processes complex information based on the fundamental principles of neural networks in biology [107]. ANN is a nonlinear model primarily composed of neurons that are incorporated into three layers, referred to as the input layer, hidden layer, and output layer, with the ability to simulate any continuous nonlinear function and learn from samples, expressing the learned fault diagnosis knowledge with neural network connection weights, which has been utilized in the fault diagnosis of complex systems [108; 109]. Back-propagation Neural Network (BPNN) [110] is currently the most widely used neural network model. Researchers created it to address the challenges of multi-layer neural networks. [PERSON] et al. [111] employed BPNN for temperature sensor fault diagnosis. The model selected the most recent local data, reducing the computation required and enabling online detection. In order to eliminate data noise and improve the accuracy of neural network fault diagnosis, [PERSON] et al. [112] incorporated wavelet denoising technology into the BPNN, which enhanced the BPNN's fault detection rate. [PERSON] et al. [113] proposed an EFMSAE-LSTM method for predicting the time series of mechanical failures, which can accurately predict the failure time series of most key mechanical components. [PERSON] et al.