A Prediction Model of Smart Agriculture Based on IoT Sensor Data: A Systematic Literature Review

Fondaj, Jakup; Hamiti, Mentor; Krrabaj, Samedin; Ajdari, Jaumin; Zenuni, Xhemal

doi:10.1109/meco58584.2023.10154965

Cited by 3 publications

(4 citation statements)

References 44 publications

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“…From our previous research [26] we chose four algorithms to use for the prediction of whether conditions as "NeuralProphet [12], Random Forest Regression [11], SARIMA [10], and ANN [13] facilitated by the KERAS framework" [26].…”

Section: Methodsmentioning

confidence: 99%

Comparison of Predictive Algorithms for IOT Smart Agriculture Sensor Data

Fondaj,

Hamiti,

Krrabaj

et al. 2023

Int. J. Interact. Mob. Technol.

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This paper compares predictive algorithms for smart agriculture sensor data in Internet of Things (IoT) applications. The main objective of IoT in agriculture is to improve productivity and reduce production costs using advanced technology and artificial intelligence. In this study, we compared various predictive algorithms for analyzing IoT smart agriculture sensor data. Specifically, we evaluated the performance of NeuralProphet, Random Forest Regression, SARIMA, and Artificial Neural Networks (ANN) by KERAS algorithms on a dataset containing temperature, humidity, and soil moisture data. The dataset was collected using IoT sensors in a smart agriculture system. The results showed that Random Forest Regression, Seasonal ARIMA, and Artificial Neural Networks by KERAS algorithms outperformed NeuralProphet algorithm in terms of accuracy and computational efficiency.

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Section: Methodsmentioning

confidence: 99%

Comparison of Predictive Algorithms for IOT Smart Agriculture Sensor Data

Fondaj,

Hamiti,

Krrabaj

et al. 2023

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

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“…Moreover, IoT solutions for smart farming are widely discussed by various researchers, such as Fondaj et al [31], Dewari et al [32], and Zamir et al [33]. These studies explore the role of IoT sensor data in predicting agricultural outcomes and optimizing farming practices.…”

Section: Iot In Agriculturementioning

confidence: 99%

“…Finally, Section 6 presents the conclusions drawn from this study. Through this structured approach, the study aims to contribute valuable insights to the burgeoning field of PA. [24], Ganapathi et al (2023) [6], Abu et al (2022) [25], Shi et al (2019) [26], Kumar et al (2024) [27], Polymeni et al (2023) [28], Shrestha et al (2024) [29], Widianto et al (2023) [30], Fondaj et al (2023) [31], Dewari et al (2023) [32], Zamir et al (2023) [33], Rathi et al (2023) [34], [35], Chataut et al (2023) [36], and Bulut et al (2023) [37]. Cited studies for AI only: Singh et al (2022) [38], Sharma et al (2020) [39], Cravero et [64], Gupta et al (2022) [65], and Baghel et al (2022) [66].…”

Section: Introductionmentioning

confidence: 99%

IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities

Senoo,

Anggraini,

Kumi

et al. 2024

Electronics

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The global agricultural sector confronts significant obstacles such as population growth, climate change, and natural disasters, which negatively impact food production and pose a threat to food security. In response to these challenges, the integration of IoT and AI technologies emerges as a promising solution, facilitating data-driven decision-making, optimizing resource allocation, and enhancing monitoring and control systems in agricultural operations to address these challenges and promote sustainable farming practices. This study examines the intersection of IoT and AI in precision agriculture (PA), aiming to provide a comprehensive understanding of their combined impact and mutually reinforcing relationship. Employing a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, we explore the synergies and transformative potential of integrating IoT and AI in agricultural systems. The review also aims to identify present trends, challenges, and opportunities in utilizing IoT and AI in agricultural systems. Diverse forms of agricultural practices are scrutinized to discern the applications of IoT and AI systems. Through a critical analysis of existing literature, this study contributes to a deeper understanding of how the integration of IoT and AI technologies can revolutionize PA, resulting in improved efficiency, sustainability, and productivity in the agricultural sector.

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“…Based on our previous research [17,18,19], we have chosen to use the SARIMA [22] model for further prediction in this model because it has shown better performance than other algorithms we tested [19].…”

Section: Introductionmentioning

confidence: 99%

Proposal of model for prediction of grape processing and spraying time by using IoT smart agriculture sensor data

Fondaj,

Hamit,

Krrabaj

et al. 2024

IJITS JOURNAL

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The grape industry's impact on agriculture and the economy requires precise forecasting for processing and spraying schedules to optimize production. This article introduces an innovative IoT-based model for predicting optimal timings in grape processing and spraying. By integrating real-time environmental and viticultural data, the model improves decision-making, enhancing product quality, reducing energy consumption, and increasing operational efficiency. Crucially, SARIMA predictive algorithms forecast parameters like temperature, humidity, wind speed, and air pressure. This comprehensive model transforms the grape industry, offering advanced decision support and promoting sustainable, resource-efficient production. The research signals a potential shift to precision agriculture, balancing economic viability with environmental stewardship in grapes.

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A Prediction Model of Smart Agriculture Based on IoT Sensor Data: A Systematic Literature Review

Cited by 3 publications

References 44 publications

Comparison of Predictive Algorithms for IOT Smart Agriculture Sensor Data

Comparison of Predictive Algorithms for IOT Smart Agriculture Sensor Data

IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities

Proposal of model for prediction of grape processing and spraying time by using IoT smart agriculture sensor data

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