Development of a Digital Twin for smart farming: Irrigation management system for water saving

Alves, Rafael Gomes; Maia, Rodrigo Filev; Lima, Fábio

doi:10.1016/j.jclepro.2023.135920

Cited by 49 publications

(39 citation statements)

References 18 publications

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“…Alves et al developed a DT for smart water management taking data from temperature and humidity sensors, soil moisture, ambient light, as well as geospatial position sensors, connected to an IoT system, the cloud, and the physical twin. 45,46 The cloud contains models to simulate the behavior of the soil and crops.…”

Section: Digital Twins In Agricultural Production: Application Demons...mentioning

confidence: 99%

“…Digital technologies used in soil-related DTs include wireless system networks, IoT, edge-computing, local weather-based controllers, and soil sensors. Alves et al developed a DT for smart water management taking data from temperature and humidity sensors, soil moisture, ambient light, as well as geospatial position sensors, connected to an IoT system, the cloud, and the physical twin. , The cloud contains models to simulate the behavior of the soil and crops.…”

Section: Digital Twins In Agricultural Production: Application Demons...mentioning

confidence: 99%

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Digital Twins in Agriculture: Orchestration and Applications

Escribà-Gelonch,

Liang,

van Schalkwyk

et al. 2024

J. Agric. Food Chem.

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Digital Twins have emerged as an outstanding opportunity for precision farming, digitally replicating in real-time the functionalities of objects and plants. A virtual replica of the crop, including key agronomic development aspects such as irrigation, optimal fertilization strategies, and pest management, can support decision-making and a step change in farm management, increasing overall sustainability and direct water, fertilizer, and pesticide savings. In this review, Digital Twin technology is critically reviewed and framed in the context of recent advances in precision agriculture and Agriculture 4.0. The review is organized for each step of agricultural lifecycle, edaphic, phytotechnologic, postharvest, and farm infrastructure, with supporting case studies demonstrating direct benefits for agriculture production and supply chain considering both benefits and limitations of such an approach. Challenges and limitations are disclosed regarding the complexity of managing such an amount of data and a multitude of (often) simultaneous operations and supports.

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Section: Digital Twins In Agricultural Production: Application Demons...mentioning

confidence: 99%

Section: Digital Twins In Agricultural Production: Application Demons...mentioning

confidence: 99%

Digital Twins in Agriculture: Orchestration and Applications

Escribà-Gelonch,

Liang,

van Schalkwyk

et al. 2024

J. Agric. Food Chem.

View full text Add to dashboard Cite

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“…According to the reviewed literature summarized in Table 2 , smart farming as an implementation of agriculture 4.0 [ 19 , 26 , 27 , 28 , 29 , 45 , 63 , 73 , 75 , 76 , 77 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 ] includes smart irrigation strategies by monitoring and controlling various farming stages [ 28 ], implementing data acquisition techniques [ 27 ] for automatically controlling actuator systems in agriculture or even in high-tech data-driven greenhouses [ 79 ]. Furthermore, via simulation, monitoring, controlling, coordinating, and executing farm operations at agricultural sites [ 87 ], DTs improve predictive control in precision irrigation [ 28 , 73 , 88 ], greenhouse horticulture, and organic vegetable and livestock farming. Smart farming enhances remote detection and monitoring of vegetation and crop stress in agriculture [ 86 ], developing DT stages and forecasting plant yield in greenhouses, vertical farms, or outdoor fields [ 76 , 77 ], or even in urban and indoor farming, of vertical agriculture utilizing hydroponics, aeroponics, aquaculture, and aquaponics.…”

Section: Reviewing Dt Applications In Agriculture and Farming Domainmentioning

confidence: 99%

“…Field or soil probes that measure air and ground temperature, humidity, soil moisture, and ambient light [ 28 , 45 , 73 , 81 , 82 , 88 ], added to CO 2 sensors measuring relative CO 2 concentration [ 80 , 81 ], and infrared (IR) thermometers provide data acquisition modules for automatically controlled actuator systems in agriculture approaching digital modelling to the food process [ 64 ] monitoring activities of livestock, optimization of crops, reducing emissions to air, soil, and water. Drone image cameras [ 88 ] and high-resolution photographic cameras seem ideal for AR and VR by constructing virtual natural low-polygon 3D plant models as proposed in [ 35 , 74 ].…”

Section: Reviewing Dt Applications In Agriculture and Farming Domainmentioning

confidence: 99%

Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review

Peladarinos

Piromalis

Cheimaras

et al. 2023

Sensors

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Digital Twins serve as virtual counterparts, replicating the characteristics and functionalities of tangible objects, processes, or systems within the digital space, leveraging their capability to simulate and forecast real-world behavior. They have found valuable applications in smart farming, facilitating a comprehensive virtual replica of a farm that encompasses vital aspects such as crop cultivation, soil composition, and prevailing weather conditions. By amalgamating data from diverse sources, including soil, plants condition, environmental sensor networks, meteorological predictions, and high-resolution UAV and Satellite imagery, farmers gain access to dynamic and up-to-date visualization of their agricultural domains empowering them to make well-informed and timely choices concerning critical aspects like efficient irrigation plans, optimal fertilization methods, and effective pest management strategies, enhancing overall farm productivity and sustainability. This research paper aims to present a comprehensive overview of the contemporary state of research on digital twins in smart farming, including crop modelling, precision agriculture, and associated technologies, while exploring their potential applications and their impact on agricultural practices, addressing the challenges and limitations such as data privacy concerns, the need for high-quality data for accurate simulations and predictions, and the complexity of integrating multiple data sources. Lastly, the paper explores the prospects of digital twins in agriculture, highlighting potential avenues for future research and advancement in this domain.

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“…The system uses data from diverse sources, including soil, plant condition, environmental sensor networks, meteorological predictions, and high-resolution UAV and Satellite imagery, to provide farmers with a dynamic and up-to-date visualization of their agricultural domains [21]. These systems use sensory systems and data from diverse sources, including weather forecasts, soil moisture sensors, and plant water stress sensors, to optimize irrigation schedules and reduce water waste.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Cyclone Intensity Prediction for Smart Cities Using a Deep-Learning Approach for Accurate Prediction

Jayaraman,

Venkatachalam,

Eid

et al. 2023

Atmosphere

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Accurate cyclone intensity prediction is crucial for smart cities to effectively prepare and mitigate the potential devastation caused by these extreme weather events. Traditional meteorological models often face challenges in accurately forecasting cyclone intensity due to cyclonic systems’ complex and dynamic nature. Predicting the intensity of cyclones is a challenging task in meteorological research, as it requires expertise in extracting spatio-temporal features. To address this challenge, a new technique, called linear support vector regressive gradient descent Jaccardized deep multilayer perceptive classifier (LEGEMP), has been proposed to improve the accuracy of cyclone intensity prediction. This technique utilizes a dataset that contains various attributes. It employs the Herfindahl correlative linear support vector regression feature selection to identify the most important characteristics for enhancing cyclone intensity forecasting accuracy. The selected features are then used in conjunction with the Nesterov gradient descent jeopardized deep multilayer perceptive classifier to predict the intensity classes of cyclones, including depression, deep depression, cyclone, severe cyclone, very severe cyclone, and extremely severe cyclone. Experimental results have demonstrated that LEGEMP outperforms conventional methods in terms of cyclone intensity prediction accuracy, requiring minimum time, error rate, and memory consumption. By leveraging advanced techniques and feature selection, LEGEMP provides more reliable and precise predictions for cyclone intensity, enabling better preparedness and response strategies to mitigate the impact of these destructive storms. The LEGEMP technique offers an improved approach to cyclone intensity prediction, leveraging advanced classifiers and feature selection methods to enhance accuracy and reduce error rates. We demonstrate the effectiveness of our approach through rigorous evaluation and comparison with conventional prediction methods, showcasing significant improvements in prediction accuracy. Integrating our enhanced prediction model into smart city disaster management systems can substantially enhance preparedness and response strategies, ultimately contributing to the safety and resilience of communities in cyclone-prone regions.

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Development of a Digital Twin for smart farming: Irrigation management system for water saving

Cited by 49 publications

References 18 publications

Digital Twins in Agriculture: Orchestration and Applications

Digital Twins in Agriculture: Orchestration and Applications

Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review

Enhancing Cyclone Intensity Prediction for Smart Cities Using a Deep-Learning Approach for Accurate Prediction

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