## 3 LSTM and FSST-Based Fault Diagnosis Algorithm
### Machine Learning for Waveform Segmentation  
The machine learning classification procedure in this study involves dividing the data into training and testing datasets, training the neural network with the training data, and evaluating the neural network's performance using the testing dataset. We create distinct repositories to separate the data into training and testing datasets. In this paper, LSTM neural network models are utilized for waveform segmentation. The LSTM model generates output in the form of a sequence or mask in which each label corresponds to a segment of the input signal. This configuration is commonly applied in classification tasks where specific events within time-series data need to be detected or predicted. Here, the model diagnoses sensor statuses by detecting the occurrence of events, with the mask providing a binary value at each time step to indicate the presence or absence of these events.  
The neural network assigns labels to regions of the signal, effectively categorizing the data into segments based on event occurrence. To enable this segmentation, the region labels of the dataset are transformed into a sequence that contains one label per signal sample. This transformation is achieved through a function that identifies regions of interest and assigns labels accordingly. Regions of interest are identified based on the characteristics of the data. Each sample within these regions is labeled, while irrelevant samples are assigned an \"n/a\" (not applicable) label.  
Processing long sequences of input data requires careful management to prevent performance degradation and excessive memory usage. Thus, input signals and their corresponding label masks are split into shorter segments, improving memory storage and processing efficiency. This is accomplished by utilizing a transformed data repository and a data resizing function that cuts or splits input signals and label masks to manageable lengths. These segmented signals are then passed to the LSTM neural network for training and inference. Each segment is processed independently, allowing the LSTM to estimate based on partial sequences; this is an essential feature of analyzing large datasets in smart agriculture.  
LSTM networks are particularly well suited for waveform segmentation of time-series data in agricultural applications because of their ability to capture temporal dependencies. LSTMs excel at processing sequential data where the order of inputs is significant, making them ideal for the complex, non-linear patterns often found in agricultural time-series data. Such data may include variations in soil moisture, temperature, and crop health over time. These non-linear data necessitate a model like LSTM that can adapt and predict based on time-dependent patterns. This capability is invaluable for detecting anomalies and diagnosing sensor statuses in smart farming environments, where the details of temporal data evolution are important. By maintaining sequential continuity and providing accurate predictions, LSTM models effectively support autonomous decision making in smart agriculture, enhancing system reliability and operational efficiency.