Pesticides are essential for removing plant pests and sustaining good yields on agricultural land. Excessive use has detrimental repercussions, such as the depletion of soil fertility and the proliferation of immune insect species, such as Nilaparvata lunges and Nezara viridula. Unmanned aerial vehicle (UAV) variable-rate spraying offers a precise and adaptable alternative strategy for overcoming these challenges. This study explores research trends in the application of semi-automatic approaches and land-specific platforms for precision spraying. The employment of an autonomous control system, together with a selection of hardware such as microcontrollers, sensors, pumps, and nozzles, yields the performance necessary to accomplish spraying precision, UAV performance efficacy, and flexibility in meeting plant pesticide requirements. This paper discusses the implications of ongoing and developing research. The comparison of hardware, control system approaches, and data acquisition from the parameters of each study is presented to facilitate future research. Future research is incentivized to continue the precision performance of the variable rate development by combining it with cropland mapping to determine the need for pesticides, although strict limits on the amount of spraying make it difficult to achieve the same, even though the quality is very beneficial.

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Independent Control Spraying System for UAV-Based Precise Variable Sprayer: A Review

This article presents energy-efficient edge processing for an ultrahigh-frequency (UHF) band radio-frequency identification (RFID)-based vibration frequency/ physical shock sensing system. This system is especially useful for long-term measurements. In this edge processing system, an RFID reader has a standby mode operating at a low duty cycle and an active mode operating at a high duty cycle. With these modes, an RFID reader achieves both energy-efficient operations and accurate sensing performance. The standby mode is useful for reducing energy consumption, preventing overheating, and reducing greenhouse gas emissions because a low duty cycle leads to a short radio wave irradiation time. Reducing the heat generation of the RFID reader, especially for long-term measurements, the standby mode makes the RFID reader durable. If vibration/physical shocks are applied to the RFID sensor tag, then the standby mode triggers the active mode. The standby mode samples the presence of the RFID sensor tags within an experimentally determined sampling period to detect the beginning of vibration/physical shocks. A high-duty cycle in the active mode results in an increased number of RFID sensor tag readings. Because the proposed edge processing keeps measured data only for the active mode, the amount of data uploaded to the Internet-of-Things (IoT) cloud is reduced. The proposed method was experimentally evaluated by monitoring walking behaviors and a refrigerator. The vibration sensing capability was mainly validated by monitoring a refrigerator, and the physical shock sensing capability was evaluated by monitoring walking behaviors by a human examinee.

An RFID-Based Wireless Vibration and Physical-Shock Sensing System Using Edge Processing

Sensors are crucial for the control of unmanned aerial vehicles (UAVs). However, sensor faults will inevitably appear over time. Therefore, it is important to develop a sensor fault detection approach for the reliability of UAV. This article presents a novel model-based UAV fault detection approach based on quadratic Kalman filter (QKF). First, an accurate kinematic and dynamic model of UAVs is established, where the model is linearized and discretized for Kalman filter (KF). Second, the first KF is used for denoising, the secondKF is used to detrend, and residuals are calculated for detection. It is worth mentioning that the second KF is a modified Sage–Husa adaptive KF, which can automatically estimate the measurement noise variance. Compared with traditional approaches, this approach has the advantages of noise reduction, self-adaptation, divergence avoidance, and high detection rate. Simulation and experimental results show the effectiveness of the proposed approach, which can accurately detect the abrupt and incipient fault of an inertial measurement unit (IMU) sensor. At the same time, it can get the higher fault detection rates (FDRs) compared with conventional KF. Furthermore, this approach also provides the leading information and foundation for UAV fault-tolerant control. 

Quadratic-Kalman-Filter-Based Sensor Fault Detection Approach for Unmanned Aerial Vehicles

Undetected partial actuator faults on multi-rotor UAVs can lead to system failures and uncontrolled crashes, necessitating the development of accurate and efficient fault detection and isolation (FDI) strategy. This paper proposes a hybrid FDI model for a quadrotor UAV that integrates an extreme learning neuro-fuzzy algorithm with a model-based extended Kalman filter (EKF). Three FDI models using Fuzzy-ELM, R-EL-ANFIS, and EL-ANFIS are compared based on training, validation performances, and sensitivity to weaker and shorter actuator faults. They are also tested online for linear and nonlinear incipient faults by measuring their isolation time delays and accuracies. The results show that the Fuzzy-ELM FDI model exhibits greater efficiency and sensitivity, while Fuzzy-ELM and R-EL-ANFIS FDI models demonstrate better performance than a conventional neuro-fuzzy algorithm, ANFIS. 

Actuator fault detection and isolation on multi-rotor UAV using extreme learning neuro-fuzzy systems.

Compaction and sintering of low and high carbon content ferrous powders with surface roughness studies

Early drone anomaly inspection is vital to ensure the drone’s safety and effectiveness. This process is often overlooked, especially by amateur drone pilots; however, some faulty conditions are difficult to notice by the naked eye or discover, even though the drone inspection process has been conducted; therefore, a real-time early drone inspection approach based on vibration data is proposed in this study. Firstly, the reliability of several microelectromechanical systems (MEMS) sensors, namely the ADXL335 accelerometer, ADXL 345 accelerometer, ADXL377 accelerometer, and SW420 vibration sensor in detecting faulty conditions, were tested and compared. The experimental results demonstrated that the vibration parameter measured using ADXL335 and ADXL345 accelerometers are the best choice as most of the faulty conditions can be detected, in contrast to other MEMS sensors. The output produced from the anomaly inspection algorithm is then converted to the “Healthy” or “Faulty” state, which is displayed in a mobile application for easy monitoring.

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An Investigation of the Reliability of Different Types of Sensors in the Real-Time Vibration-Based Anomaly Inspection in Drone

Recent years have seen a huge increase in the study of drones. There is a lot of published articles regarding drone, focusing on control optimization, fault detection, safety mechanisms, etc. In fault detection, most studies focused on the effects of faulty propellers and rotors, and there is very limited academic research on drone arms. In this paper, a fault detection based on the vibration of the multirotor arms using artificial intelligence (AI) is proposed. There are some cases in which, due to accident, the arm of the multirotor crack or loosen. This is normally unnoticeable without disassembly, and if not taken care of, it would have likely resulted in a sudden loss of flight stability, which will lead to a crash. Different types of AI methods are incorporated in this study, namely, fuzzy logic, neuro-fuzzy, and neural network (NN). Their results are compared to determine the best method in predicting the safety of the multirotor. Fuzzy logic and neuro-fuzzy methods provided acceptable decision-making, but the performance of the neuro-fuzzy approach depend on the dataset used because overfit model might give incorrect decision-making. This also applies to the NN technique. Because the vibration data are collected in the laboratory environment without consideration of wind effect, this framework is more suitable for early prediction before flying the multirotor in the outdoor environment.

/pdf/vibration-based-fault-detection-in-drone-using-artificial-27o4271g.pdf

Vibration-Based Fault Detection in Drone Using Artificial Intelligence

The dynamic characteristics such as natural frequencies, mode shapes and frequency response function (FRF) are the important characteristics to be investigated to access the level of durability of any dam structures. These characteristics are important since it will be the reference information for any operational methods to be used for the dam structures. In this study, one of the real dam (i.e., Chenderoh Dam) that available in Malaysia is taken into consideration, where the dynamic analysis of the sector gate section of the dam structure is investigated. The real scale of the sector gate section is measured on site and modelled into the CAD software with the consideration of real build-in materials. Then, the finite element (FE) model is constructed in ANSYS software with the required boundary condition and meshing sensitivity analysis. From the result of modal analysis, 30 natural frequencies are determined in the range of 0.5904 Hz to 8.471 Hz together with the mode shapes but only the most significant natural frequencies will be shown in this paper. In addition, all three axes of the FRF graphs show an agreement for the highest natural frequency value at 7.95 Hz, where the maximum deflection occurred in x axis direction with 2.03 x 10-7 m.

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Structural Dynamic Analysis of the Chenderoh Dam Sector Gate Section

Surface roughness (Ra) is an important parameter in determining the quality of surface finishing of any products obtained from the machining process. Surface roughness can be affected by several factors such as machining parameters, workpiece material properties, cutting tool parameters and machine tool conditions. In this study, the effects of machining parameters such as feed rate and cutting speed on the surface roughness was investigated for aluminium, mild steel and brass materials during the turning process using CNC Lathe machine. The experimental Ra values obtained were compared with the theoretical values with discrepancy ranging from 1.14 % to 113.71 %. From the study, it can be concluded that the surface roughness of materials will increases as the feed rate increases and decrease when higher cutting speed is applied.

https://iopscience.iop.org/article/10.1088/1757-899X/530/1/012008/pdf

Effect of Machining Parameters on the Surface Roughness for Different Type of Materials

Abstract— Drones have been widely applied in the precision agriculture sector in the past few years. Incorporating artificial intelligence (AI), sensors, microcontrollers, and the Internet of Things (IoT) into the drones can help overcome the challenges faced by the farmers, such as livestock monitoring, wide land area, crop spraying, and in-depth crop health analysis. In this paper, several drone applications in precision agriculture are discussed, including the hardware and techniques involved. In addition, commercial agricultural drones available in the market to date are presented. The publications trend regarding drone application in precision agriculture is also included and based on reviewing more than 50 articles, a quadcopter-type drone is the most used drone in this sector, and seed planting is the least explored drone application area. Keywords—camera, crop monitoring, drone, mapping, spraying system

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Mohamad Hazwan Mohd Ghazali

Papers

An Investigation of the Reliability of Different Types of Sensors in the Real-Time Vibration-Based Anomaly Inspection in Drone

Vibration-Based Fault Detection in Drone Using Artificial Intelligence

Structural Dynamic Analysis of the Chenderoh Dam Sector Gate Section

Effect of Machining Parameters on the Surface Roughness for Different Type of Materials

Drone Implementation in Precision Agriculture – A Survey