Smart-Contract-Based Agricultural Service Platform for Drone Plant Protection Operation Optimization

Qin, Zheng; Lin, Na; Fu, Di; Tian-jun, Liu; Zhu, Yanhui; Feng, Xiaochun; Ruan, Junhu

doi:10.1109/jiot.2023.3288870

Cited by 7 publications

(1 citation statement)

References 41 publications

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“…In recent years, unmanned aerial vehicle (UAV) technology has undergone rapid development, expanding its application scenarios and use cases. Owing to its user friendly operation, robust scalability, and cost effectiveness, UAVs have found widespread utilization in diverse fields such as agriculture, plant protection [1,2], and disaster rescue [3]. However, extended operations, challenging working environments, and deliberate damage can impact the overall health status of UAVs, leading to system failures, loss of control, substantial economic losses, and even casualties.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of drone motors driven by current signal data with few samples

Chen,

Li,

et al. 2024

Meas. Sci. Technol.

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Multirotor UAVs find extensive application across diverse domains, with their motors serving as pivotal components in the UAV power system. The majority of UAV failures and crashes stem from motor malfunctions, underscoring the critical need for research in UAV motor fault diag-nosis. This paper focuses on quadrotor UAVs employing DC brushless motors as the research subject. The study designs fault experiments for three key components—stator, rotor, and bear-ing—based on the structural and operational characteristics of these motors. Addressing chal-lenges such as limited installation space for UAV payloads and redundant components, difficul-ties in sensor installation, high costs, and data acquisition complexities, this research adopts the current signal as the diagnostic signal. It explores a UAV motor fault diagnostic method based on the current signal. Furthermore, the study investigates a motor fault diagnosis method based on the current signal. To overcome challenges arising from the UAV's heightened sensitivity to the health of its components and the limited availability of fault data training samples, traditional machine learning and deep learning methods face difficulties in identifying representative fea-tures under small samples, often succumbing to overfitting risks and resulting in low diagnostic accuracy. To address these issues, a hybrid neural network fault diagnosis model is proposed, integrating the width learning system and convolutional neural network. The width learning system maps the original current signal into a feature space, yielding more robust and repre-sentative sample features. Subsequently, the convolutional neural network undertakes feature extraction and classification tasks. In experiments focused on current signal data-driven small-sample fault diagnosis in UAV motors, the proposed model outperforms other models used for comparison.

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Section: Introductionmentioning

confidence: 99%

Fault diagnosis of drone motors driven by current signal data with few samples

Chen,

Li,

et al. 2024

Meas. Sci. Technol.

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Integration of Digital Twin and Blockchain for Smart Cities

Tyagi,

Kumari,

Surve

2024

Digital Twin and Blockchain for Smart Cities

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Internet of Things and Wireless Sensor Networks for Smart Agriculture Applications: A Survey

Mowla,

Shah

et al. 2023

IEEE Access

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The increasing food scarcity necessitates sustainable agriculture achieved through automation to meet the growing demand. Integrating the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) is crucial in enhancing food production across various agricultural domains, encompassing irrigation, soil moisture monitoring, fertilizer optimization and control, early-stage pest and crop disease management, and energy conservation. Wireless application protocols such as ZigBee, WiFi, SigFox, and LoRaWAN are commonly employed to collect real-time data for monitoring purposes. Embracing advanced technology is imperative to ensure efficient annual production. Therefore, this study emphasizes a comprehensive, futureoriented approach, delving into IoT-WSNs, wireless network protocols, and their applications in agriculture since 2019. It thoroughly discusses the overview of IoT and WSNs, encompassing their architectures and summarization of network protocols. Furthermore, the study addresses recent issues and challenges related to IoT-WSNs and proposes mitigation strategies. It provides clear recommendations for the future, emphasizing the integration of advanced technology aiming to contribute to the future development of smart agriculture systems. INDEX TERMSInternet of things, wireless sensor networks, wireless network protocols, smart agriculture applications.

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Smart-Contract-Based Agricultural Service Platform for Drone Plant Protection Operation Optimization

Cited by 7 publications

References 41 publications

Fault diagnosis of drone motors driven by current signal data with few samples

Fault diagnosis of drone motors driven by current signal data with few samples

Integration of Digital Twin and Blockchain for Smart Cities

Internet of Things and Wireless Sensor Networks for Smart Agriculture Applications: A Survey

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