Versatile Internet of Things for Agriculture: An eXplainable AI Approach

Tsakiridis, Nikolaos L.; Diamantopoulos, Themistoklis; Symeonidis, Andreas L.; Theocharis, John B.; Iossifides, Athanassios C.; Pratos, George; Kouvas, Dimitris

doi:10.1007/978-3-030-49186-4_16

“…Tsakiridis et al [25] proposed and tested an irrigation system, Vital, that use fuzzy logic and the principles of interpretability. The sensors collect and send values to the web-based application that uses Vital to generate irrigation decisions based on a fuzzy rule base.…”

Section: Related Workmentioning

confidence: 99%

An Interpretable Artificial Intelligence Based Smart Agriculture System

Sabrina¹,

Sohail²,

Farid³

et al. 2022

Computers, Materials &Amp; Continua

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With increasing world population the demand of food production has increased exponentially. Internet of Things (IoT) based smart agriculture system can play a vital role in optimising crop yield by managing crop requirements in real-time. Interpretability can be an important factor to make such systems trusted and easily adopted by farmers. In this paper, we propose a novel artificial intelligence-based agriculture system that uses IoT data to monitor the environment and alerts farmers to take the required actions for maintaining ideal conditions for crop production. The strength of the proposed system is in its interpretability which makes it easy for farmers to understand, trust and use it. The use of fuzzy logic makes the system customisable in terms of types/number of sensors, type of crop, and adaptable for any soil types and weather conditions. The proposed system can identify anomalous data due to security breaches or hardware malfunction using machine learning algorithms. To ensure the viability of the system we have conducted thorough research related to agricultural factors such as soil type, soil moisture, soil temperature, plant life cycle, irrigation requirement and water application timing for Maize as our target crop. The experimental results show that our proposed system is interpretable, can detect anomalous data, and triggers actions accurately based on crop requirements.

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“…These devices installed in agricultural fields support the implementation of irrigation control systems using a network of sensors that are employed for monitoring humidity and temperature. For enhancing the interpretability and trustworthiness of automated monitoring in agricultural fields, an explainable AI-based decision support system is developed in [78]. The developed systems implement fuzzy rule-based automation in irrigation, which is interpretable.…”

Section: A Enabling Xai In Iot Systems: An Overviewmentioning

confidence: 99%

Explainable AI Over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions

Jagatheesaperumal¹,

Pham

²

,

Ruby

³

et al. 2022

IEEE Open J. Commun. Soc.

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Explainable Artificial Intelligence (XAI) is transforming the field of Artificial Intelligence (AI) by enhancing the trust of end-users in machines. As the number of connected devices keeps on growing, the Internet of Things (IoT) market needs to be trustworthy for the end-users. However, existing literature still lacks a systematic and comprehensive survey work on the use of XAI for IoT. To bridge this lacking, in this paper, we address the XAI frameworks with a focus on their characteristics and support for IoT. We illustrate the widely-used XAI services for IoT applications, such as security enhancement, Internet of Medical Things (IoMT), Industrial IoT (IIoT), and Internet of City Things (IoCT). We also suggest the implementation choice of XAI models over IoT systems in these applications with appropriate examples and summarize the key inferences for future works. Moreover, we present the cutting-edge development in edge XAI structures and the support of sixth-generation (6G) communication services for IoT applications, along with key inferences. In a nutshell, this paper constitutes the first holistic compilation on the development of XAI-based frameworks tailored for the demands of future IoT use cases.

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“…These devices installed in agricultural fields support the implementation of irrigation control systems using a network of sensors that are employed for monitoring humidity and temperature. For enhancing the interpretability and trustworthiness of automated monitoring in agricultural fields, an explainable AI-based decision support system is developed in [77]. The developed systems implement fuzzy rule-based automation in irrigation, which is interpretable.…”

Section: A Enabling Xai In Iot Systems: An Overviewmentioning

confidence: 99%

Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions

Jagatheesaperumal,

Pham,

Ruby

et al. 2022

Preprint

0

View full text Add to dashboard Cite

Explainable Artificial Intelligence (XAI) is transforming the field of Artificial Intelligence (AI) by enhancing the trust of end-users in machines. As the number of connected devices keeps on growing, the Internet of Things (IoT) market needs to be trustworthy for the end-users. However, existing literature still lacks a systematic and comprehensive survey work on the use of XAI for IoT. To bridge this lacking, in this paper, we address the XAI frameworks with a focus on their characteristics and support for IoT. We illustrate the widely-used XAI services for IoT applications, such as security enhancement, Internet of Medical Things (IoMT), Industrial IoT (IIoT), and Internet of City Things (IoCT). We also suggest the implementation choice of XAI models over IoT systems in these applications with appropriate examples and summarize the key inferences for future works. Moreover, we present the cutting-edge development in edge XAI structures and the support of sixth-generation (6G) communication services for IoT applications, along with key inferences. In a nutshell, this paper constitutes the first holistic compilation on the development of XAI-based frameworks tailored for the demands of future IoT use cases.

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Versatile Internet of Things for Agriculture: An eXplainable AI Approach

Cited by 31 publications

References 27 publications

An Interpretable Artificial Intelligence Based Smart Agriculture System

An Interpretable Artificial Intelligence Based Smart Agriculture System

Explainable AI Over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions

Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions

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