An IoT-based intelligent irrigation system with data fusion and a self-powered wide-area network

Gong, Li; Yan, Jinlong; Chen, Yiqiao; An, Jinjing; Li, He; Zheng, Lirong; Zou, Zhuo

doi:10.1016/j.jii.2022.100367

Cited by 13 publications

(8 citation statements)

References 32 publications

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“…In 2022, Gong et al [7] have proposed an intelligent irrigation system that optimized the watering schedule by integrating a LoRa network and a data fusion model. The data fusion model was presented in this work that integrated multisource heterogeneous data, such as online monitoring sensor data, weather forecasts, user irrigation logs, and historical weather data.…”

Section: A Related Workmentioning

confidence: 99%

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An Adaptive Hybrid (1D-2D) Convolution-Based ShuffleNetV2 Mechanism for Irrigation Levels Prediction in Agricultural Fields With Smart IoTs

Xu,

Lakshmi Patibandla,

Arora

et al. 2024

IEEE Access

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Rich natural resources such as fertilizers, environment, groundwater, rivers, and land are abundant in many countries. Agriculture is the primary source of income for the people living in different countries. There have not been shortages of resources like river water and groundwater, in recent decades. But, the lack of knowledge on how to use those valuable resources is the main reason for resource wastage. The amount of water applied to crop fields in a variety of soil, weather, and crop growth stages can be managed and optimized using smart farming. The crop field's soil moisture can be measured using sensors positioned at various observation points, which will show how much water has been retained. Unfortunately, the smart farming system is not capable to receive the soil moisture data provided by the irrigation management due to issues with connectivity or sensor failure. Innovative agricultural approaches can be facilitated by the Internet of Things (IoT) technologies. These IoT nodes have encountered energy limitations and challenging routing techniques as a result of their low capacity. Therefore, it is imperative to resolve the issues by implementing an effective IoTbased irrigation system in the agricultural area. The major steps of the developed model are data collection and prediction. Initially, essential image and sensor data is attained from the benchmark resources. Next, the collected images are provided to the level of the irrigation prediction phase. This phase facilitates the farmers to maximize the crop yields and minimize the production cost. Here, effective irrigation prediction is performed using an Adaptive Hybrid (1D-2D) Convolution-based ShuffleNetV2 model (AHC-ShuffleNetV2). Moreover, the parameters of the suggested AHC-ShuffleNetV2 are optimized using a Fitness-based Piranha Foraging Optimization Algorithm (FPFOA). This increases the performance rates of the proposed model. Later, several experimental analyses are executed in the developed model over classical techniques to display their effectualness rate. When considering the sigmoid activation function, the implemented smart irrigation level prediction framework's RMSE was minimized by 73.15% of POA-ShuffleNetV2, 72.36% of RSA-ShuffleNetV2, 78.94% of MRS-ShuffleNetV2, and 79.47% of PFOA-ShuffleNetV2 respectively. Hence, it is revealed that the designed smart irrigation level prediction model attained low error rates and also achieved higher efficacy than the other baseline techniques.INDEX TERMS Irrigation level prediction, agricultural fields, smart internet of things, fitness-based piranha foraging optimization algorithm, adaptive hybrid (1D-2D) convolution-based shufflenetV2 model.

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Section: A Related Workmentioning

confidence: 99%

“…Yet, it needs to reduce the human interaction and also it needs to provide higher privacy and security rate. LSTM [7] didn't require replacing the battery as it utilizes the maintenance-free battery for the whole life cycle. Yet, it faces more complications when high-power features are utilized.…”

Section: B Problem Statementmentioning

confidence: 99%

An Adaptive Hybrid (1D-2D) Convolution-Based ShuffleNetV2 Mechanism for Irrigation Levels Prediction in Agricultural Fields With Smart IoTs

Xu,

Lakshmi Patibandla,

Arora

et al. 2024

IEEE Access

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“…Modern digital industries are heavily using industrial IoT ecosystems for digital twins, smart manufacturing, and digital supply chains [16][17][18][19]. The application areas for IoT are vast and offer significant economic value potential across an array of industries such as healthcare [20,21], agriculture [22], transportation, retail, energy management, product development, and security [23]. The dependence on those digital technologies continues to surge at a rapid rate.…”

Section: Securing the Digital Worldmentioning

confidence: 99%

Securing the Digital World: Protecting Smart Infrastructures and Digital Industries with Artificial Intelligence (Ai)-Enabled Malware and Intrusion Detection

Schmitt

2023

Preprint

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“…Self-powered IoT technology [ 12 , 13 ] seems to provide a solution for power supply. This technology refers to sensing devices that are passive in the network, meaning not equipped with batteries or connected to a power cable to provide energy, which means that the sensing devices operate independently and automatically.…”

Section: Self-powered Wireless Sensor and Environment Energy Sourcesmentioning

confidence: 99%

A Wind-Solar Hybrid Energy Harvesting Approach Based on Wind-Induced Vibration Structure Applied in Smart Agriculture

Xia¹,

Tao

et al. 2022

Micromachines

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Solar energy harvesting devices are widely used in smart agriculture nowadays. However, when lighting conditions are weak, such as through the night or on cloudy days, efficiency decays a lot. Additionally, as time goes by, more and more dust and bird droppings accumulate on the panel, which decreases the performance significantly. This paper aims to overcome the disadvantages mentioned above, and a novel wind–solar hybrid energy harvesting approach is proposed with an oscillation-induced dust-cleaning function. A wind-induced vibration device is specially designed in order to generate electrical energy and/or clean the photovoltaic panel. While in good lighting conditions, the device could keep the panel in a stable state and optimize the photovoltaic power generation efficiency. Such a hybrid energy harvesting approach is called a “suppress vibration and fill vacancy” algorithm. The experimental platform of the proposed device is introduced, and both experimental and simulation results are attained, which prove that using this device, we could realize multiple purposes at the same time.

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An IoT-based intelligent irrigation system with data fusion and a self-powered wide-area network

Cited by 13 publications

References 32 publications

An Adaptive Hybrid (1D-2D) Convolution-Based ShuffleNetV2 Mechanism for Irrigation Levels Prediction in Agricultural Fields With Smart IoTs

An Adaptive Hybrid (1D-2D) Convolution-Based ShuffleNetV2 Mechanism for Irrigation Levels Prediction in Agricultural Fields With Smart IoTs

Securing the Digital World: Protecting Smart Infrastructures and Digital Industries with Artificial Intelligence (Ai)-Enabled Malware and Intrusion Detection

A Wind-Solar Hybrid Energy Harvesting Approach Based on Wind-Induced Vibration Structure Applied in Smart Agriculture

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