###### Abstract  
Sensors have been used in various agricultural production scenarios due to significant advances in the Agricultural Internet of Things (Ag-IoT), leading to smart agriculture. Intelligent control or monitoring systems rely heavily on trustworthy sensor systems. Nonetheless, sensor failures are likely due to various factors, including key equipment malfunction or human error. A faulty sensor can produce corrupted measurements, resulting in incorrect decisions. Early detection of potential faults is crucial, and fault diagnosis techniques have been proposed. The purpose of sensor fault diagnosis is to detect faulty data in the sensor and recover or isolate the faulty sensors so that the sensor can finally provide correct data to the user. Current fault diagnosis technologies are based mainly on statistical models, artificial intelligence, deep learning, etc. The further development of fault diagnosis technology is also conducive to reducing the loss caused by sensor failures. agricultural Internet of Things; sensors; fault diagnosis; deep learning 2023232528202323252823  
12]. Unmanned aircraft may lose control or even crash due to problems with the gyroscope and accelerometer [13]. Due to the influence of the external environment, sensor aging is also a source of sensor failures, leading to incorrect decisions [14] and failure to transmit information normally [15]. In different application scenarios, sensor failures can cause significant human, economic, and environmental losses and reduce the quality of IoT service. Since [PERSON] [16] proposed fault diagnosis technology in 1971, experts and scholars have conducted extensive research in sensor fault diagnosis. Figure 1 depicts the research progress of sensor fault diagnosis technology [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26]. Early sensor fault diagnosis methods relied on experience and simple ways to pinpoint the location and cause of a problem, such as sensor redundancy, function redundancy, and characteristics tracking [27]. Sensor redundancy is the use of multiple sensors to measure the same parameter, the linear combination of the measured values of several sensors is converted into an estimated value, and the final comparison of the measured value with the estimated value is used to determine whether faults occur. This method, however, raises hardware costs. Functional redundancy uses information from each sensor in the system to determine whether there is a fault via the relationship between sensors in heterogeneous and homogeneous locations. Characterization tracking determines whether the sensor values are within acceptable limits. With the widespread adoption of Ag-IoT and the rise of machine learning, a new method for sensor fault diagnosis has emerged. A fault diagnosis model that has been trained with a large amount of data has a faster and more accurate effect [28]. In particular, as deep learning exhibits powerful data representation learning and analysis capabilities, it can meet the requirements of high-order, nonlinear, adaptive feature extraction for sensor fault diagnosis [29]. A sensor fault diagnosis method based on Ag-IoT can overcome the limitations of early sensor fault diagnosis by fully using more technical support, such as machine learning, statistical analysis, and signal processing, to realize remote real-time online fault diagnosis, even without the presence of maintenance personnel. The system can diagnose itself and recover or isolate the corresponding fault, allowing the sensor network to continue operating normally after the fault occurs while improving the intelligent level of fault diagnosis [30]. This paper offers an overview of Ag-IoT sensor fault diagnosis technology. First, sensor networks and sensors are introduced, and the current state of sensor research is reviewed. The sensor fault is investigated using Ag-IoT characteristics. The common fault  
Figure 1: Timeline of development in sensor fault diagnosis technology in Ag-IoT systems.