Search Results (157)

The Internet of Things (IoT) is rapidly advancing toward increased autonomy; however, the inherent dynamism, environmental uncertainty, device heterogeneity, and diverse data modalities pose serious challenges to its reliability and security. This paper proposes a novel framework for embedding security awareness into IoT systems—where security awareness refers to the system’s ability to detect uncertain changes and understand their impact on its security posture. While machine learning and deep learning (ML/DL) models integrated with explainable AI (XAI) methods offer capabilities for threat detection, they often lack contextual interpretation linked to system security. To bridge this gap, our framework maps XAI-generated explanations to a system’s structured security profile, enabling the identification of components affected by detected anomalies or threats. Additionally, we introduce a procedural method to compute an Importance Factor (IF) for each component, reflecting its operational criticality. This framework generates actionable insights by highlighting contextual changes, impacted components, and their respective IFs. We validate the framework using a smart irrigation IoT testbed, demonstrating its capability to enhance security awareness by tracking evolving conditions and providing real-time insights into potential Distributed Denial of Service (DDoS) attacks. Full article

Traditional farming practices are becoming increasingly inadequate to meet global food demand due to water scarcity, prolonged production cycles, climate variability, and declining arable land. In contrast, aeroponic, smart, soil-free farming technologies offer a more sustainable alternative by reducing land use and providing efficient water use, given that aeroponics intermittently delivers water in mist form rather than maintaining continuous root zone moisture. However, aeroponics faces critical challenges in irrigation management due to non-standardized structures and limited real-time control. A key limitation is the inability to dynamically respond to temperature (T), relative humidity (RH), light intensity (L_i), electrical conductivity (EC), pH, and photosynthesis rate (P_n), resulting in suboptimal crop yields and resource wastage. Despite growing interest, there remains a research gap in integrating internet of things (IoT) and machine learning technologies into aeroponic systems for adaptive control. IoT-enabled sensors provide real-time data on ambient conditions and plant health, while ML models can adaptively optimize misting intervals based on the fluctuations in P_n and environmental inputs. These technologies are particularly well suited to address the dynamic, data-intensive nature of aeroponic environments. This review purposes a novel, standardized IoT–ML framework to control irrigation by emphasizing IoT sensing and ML-based decision making in aeroponics. This integrated approach is essential for minimizing water loss, enhancing resource efficiency, and advancing the sustainability of controlled-environment agriculture. Full article

Precision agriculture increasingly relies on real-time data from soil sensors to optimize irrigation and nutrient application. Soil moisture and electrical conductivity (EC) are key indicators in irrigation and fertigation systems, directly affecting water-use efficiency and nutrient delivery to crops. This study evaluates the performance of an IoT-based soil-monitoring system for real-time tracking of EC and soil moisture under varied fertigation conditions in both laboratory and field scenarios. The EC sensor showed strong agreement with laboratory YSI measurements (R² = 0.999), confirming its accuracy. Column experiments were conducted in three soil types (sand, sandy loam, and loamy sand) to assess the EC and soil moisture response to fertigation. Sand showed rapid infiltration and low retention, with EC peaking at 420 µS/cm and moisture 0.33 cm³/cm³, indicating high leaching risk. Sandy loam retained the most moisture (0.35 cm³/cm³) and showed the highest EC (550 µS/cm), while loamy sand exhibited intermediate behavior. Fertilizer-specific responses showed higher EC in Calcium Ammonium Nitrate (CAN)-treated soils, while Monoammonium Phosphate (MAP) showed lower, more stable EC due to limited phosphorus mobility. Field validation confirmed that the IoT system effectively captured irrigation and fertigation events through synchronized EC and moisture peaks. These findings highlight the efficacy of IoT-based sensor networks for continuous, high-resolution soil monitoring and their potential to support precision fertigation strategies, enhancing nutrient-use efficiency while minimizing environmental losses. Full article

Although economic profitability and food security often outweigh water conservation priorities in arid and semi-arid regions, this study investigates irrigation practices by evaluating water footprint and economic feasibility through a comparative analysis of water-intensive and water-efficient crops. In this context, an optimal irrigation disparity framework integrated with Internet of Things (IoT) and Machine Learning (ML) mechanisms is proposed to evaluate the effectiveness of water conservation, thereby assessing the potential for enhancing economic profitability. IoT-enabled components are employed to monitor real-time environmental—soil moisture, temperature, and weather—conditions between March and November 2023. This data is processed using a hybrid modeling approach that integrates KNN, GBT, and LSTM algorithms to predict both the duration of cultivation and the water requirements. Finally, the predicted parameters are incorporated into a multi-objective framework aimed at minimizing the disparity in water allocation per net benefit. The final results indicate that saffron required substantially less water—ranging from (19.87 to 28.65 ∗ 10⁶ m³)—compared to watermelon, which consumed (34.61 to 47.07 ∗ 10⁶ m³), while achieving a higher average net profit (33 ∗ 10⁹ IRR) relative to watermelon (31 ∗ 10⁹ IRR). Moreover, saffron consistently approached optimal values across disparity-based objective functions, averaging (0.404). These findings emphasize the dual advantages of saffron as a value-added, water-efficient crop in achieving substantial water conservation while enhancing profitability, offering actionable insights for authorities to incentivize water-efficient crop adoption through subsidies, market mechanisms, or regulatory frameworks. These strategies operationalize technical insights into actionable pathways for balancing food security, economic growth, and environmental resilience. Full article

Accurate irrigation volume prediction is crucial for sustainable agriculture. This study enhances precision irrigation by integrating diverse datasets, including historical irrigation records, soil moisture, and climatic factors, collected from a small-scale commercial estate vineyard in southwestern Idaho, the United States of America (USA), over a period of three years (2017–2019). Focusing on long-term irrigation forecasting, addressing a critical gap in sustainable water management, we use machine learning (ML) methods to predict future irrigation needs, with improved accuracy. We designed, developed, and tested a Long Short-Term Memory (LSTM) model, which achieved a Mean Squared Error (MSE) of 0.37, and evaluated its performance against a simpler baseline linear regression (LinReg) model, which yielded a higher MSE of 1.29. We validate the results of the LSTM model using a cross-validation technique, wherein a mean MSE of 0.18 was achieved. The low value of the statistical analysis (p-value = 0.0009) of a paired t-test confirmed that the improvement is significant. This research shows the potential of Artificial Intelligence (AI) to optimize irrigation planning and advance sustainable precision agriculture (PA), by providing a practical tool for long-term forecasting and that supports data-driven decisions. Full article

The integration of the Internet of Things (IoT) and artificial intelligence (AI) has reshaped modern agriculture by enabling precision farming, real-time monitoring, and data-driven decision-making. This systematic review, conducted in accordance with the PRISMA methodology, provides a comprehensive overview of recent advancements in smart sensing technologies for arable crops and grasslands. We analyzed the peer-reviewed literature published between 2020 and 2024, focusing on the adoption of IoT-based sensor networks and AI-driven analytics across various agricultural applications. The findings reveal a significant increase in research output, particularly in the use of optical, acoustic, electromagnetic, and soil sensors, alongside machine learning models such as SVMs, CNNs, and random forests for optimizing irrigation, fertilization, and pest management strategies. However, this review also identifies critical challenges, including high infrastructure costs, limited interoperability, connectivity constraints in rural areas, and ethical concerns regarding transparency and data privacy. To address these barriers, recent innovations have emphasized the potential of Edge AI for local inference, blockchain systems for decentralized data governance, and autonomous platforms for field-level automation. Moreover, policy interventions are needed to ensure fair data ownership, cybersecurity, and equitable access to smart farming tools, especially in developing regions. This review is the first to systematically examine AI-integrated sensing technologies with an exclusive focus on arable crops and grasslands, offering an in-depth synthesis of both technological progress and real-world implementation gaps. Full article

The integration of smart sensor networks and Internet of Things (IoT) technologies has emerged as a key strategy for enhancing productivity and sustainability in agriculture and aquaculture under increasing climate and resource pressures. This review consolidates empirical findings on the performance of sensor-driven systems in optimizing the management of water, nutrients, and energy. Studies have demonstrated that IoT-based irrigation systems can reduce water use by up to 50% without compromising yields, while precision nutrient monitoring enables a 20–40% reduction in fertilizer inputs. In aquaculture, real-time monitoring and automated interventions have improved feed conversion ratios, reduced mortality by up to 40%, and increased yields by 15–50%. The integration of artificial intelligence (AI) into IoT frameworks further enhances predictive capabilities and operational responsiveness. Despite these benefits, widespread adoption remains constrained by high infrastructure costs, limited sensor robustness, and fragmented policy support. This paper provides a comprehensive evaluation of current technologies, adoption barriers, and strategic directions for advancing scalable, sustainable, and data-driven food production systems. Full article

Surface water systems face unprecedented stress due to climate variability, urbanization, land-use change, and growing water demand—prompting a shift from traditional hydrological modeling to intelligent, adaptive systems. This review critically explores the integration of Artificial Intelligence (AI) in surface flow management, encompassing applications in streamflow forecasting, sediment transport, flood prediction, water quality monitoring, and infrastructure operations such as dam and irrigation control. Drawing from over two decades of interdisciplinary literature, this study synthesizes recent advances in machine learning (ML), deep learning (DL), the Internet of Things (IoT), remote sensing, and hybrid AI–physics models. Unlike earlier reviews focusing on single aspects, this paper presents a systems-level perspective that links AI technologies to their operational, ethical, and governance dimensions. It highlights key AI techniques—including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), Transformer models, and Reinforcement Learning—and discusses their strengths, limitations, and implementation challenges, particularly in data-scarce and climate-uncertain regions. Novel insights are provided on Explainable AI (XAI), algorithmic bias, cybersecurity risks, and institutional readiness, positioning this paper as a roadmap for equitable and resilient AI adoption. By combining methodological analysis, conceptual frameworks, and future directions, this review offers a comprehensive guide for researchers, engineers, and policy-makers navigating the next generation of intelligent surface flow management. Full article

Monitoring soil moisture content (SMC) remains challenging due to its spatial and temporal variability. Accurate SMC prediction is essential for optimizing irrigation and enhancing water use efficiency. In this research, a Gradient Boosting Regressor (GBR) model was developed and validated to predict SMC in two soil textures, loam and silt loam, using meteorological data from Internet of Things (IoT) sensors and gravimetric SMC field measurements collected from five different depths. The statistical analysis revealed significant variation in SMC across depths in loam soil (p < 0.05), while silt loam exhibited more stable moisture distribution. The GBR model demonstrated high performance in both soil textures, achieving R² values of 0.98 and 0.94 for silt loam and loam soils, respectively, with low prediction errors (RMSE 0.85 and 0.97, respectively). Feature importance analysis showed that precipitation and humidity were the most influential features in loam soil, while solar radiation had the highest impact on prediction in silt loam soil. Soil depth also showed a significant contribution to SMC prediction in both soils. These results highlight the necessity for soil-specific modeling to enhance SMC prediction accuracy, optimize irrigation systems, and support water resources management approaches aligning with SDG6 objectives. Full article

Smart agriculture is transforming traditional farming by integrating advanced sensor-based systems, intelligent control technologies, and sustainable energy solutions to meet the growing global demand for food while reducing environmental impact. This review presents a comprehensive analysis of recent innovations in smart agriculture, focusing on the deployment of IoT-based sensors, wireless communication protocols, energy-harvesting methods, and automated irrigation and fertilization systems. Furthermore, the paper explores the role of artificial intelligence (AI), machine learning (ML), computer vision, and big data analytics in monitoring and managing key agricultural parameters such as crop health, pest and disease detection, soil conditions, and water usage. Special attention is given to decision-support systems, precision agriculture techniques, and the application of remote and proximal sensing technologies like hyperspectral imaging, thermal imaging, and NDVI-based indices. By evaluating the benefits, limitations, and emerging trends of these technologies, this review aims to provide insights into how smart agriculture can enhance productivity, resource efficiency, and sustainability in modern farming systems. The findings serve as a valuable reference for researchers, practitioners, and policymakers working towards sustainable agricultural innovation. Full article

To address the challenges linked to climate change, rapidly increasing urbanization, and food security necessity, this study explores the potential of smart, low-cost innovative cultivation systems for modules on facades as nature-based solutions (NBSs) to improve building energy efficiency, urban food production, and sustainability. Innovative cultivation systems were studied and implemented in the horizontal experimental setup, with a focus on sub-irrigation techniques with terracotta pots, ozonated water, and IoT use. The best eco-smart irrigation system was selected considering both plant growth and the water savings obtained (up to 57.14%) in comparison to the traditional method. With the implementation of this system, a vertical green module (VGM) was designed, allowing for efficient distribution and water savings. The positive effects in terms of temperature reduction and energy behavior were validated by comparing two office rooms: one without VGM and the other with VGM in a Mediterranean city. The drop in internal temperatures achieved was up to 3–4 °C during the hot days of the experimental campaign. The uptake of this low-cost and smart prototype can be useful to support the enhancement of energy-efficient, eco-sustainable, and self-sufficient buildings and urban spaces, contributing to creating more climate-resilient cities and promoting sustainable urban agriculture. Full article

Unpredictable climate patterns and mounting groundwater depletion are major challenges to sustainable agriculture. The purpose of this research is to improve predictive accuracy in IoT-based smart irrigation systems using machine learning models for soil fertility estimation and water optimization. In contrast to existing research, this paper compares state-of-the-art ensemble learning models (LRBoost, LR+RF) with conventional methods to ascertain their real-time effectiveness in water usage prediction. Training and testing data were derived from open access agricultural data repositories, including soil moisture, temperature, humidity, and rainfall. Feature selection was performed through correlation analysis and model performance was evaluated using R² score, mean squared error (MSE), and root mean squared error (RMSE). Our results indicate that the hybrid ensemble model LR+RF performed better than others with an R² measure of 96.34%, an MSE of 0.0016, and an RMSE of 0.040. The findings confirm the capability of the system in minimizing water wastage and maximizing crop production. Full article

In citriculture, inputs like water and fertilizer are applied through traditional basin methods, thereby incurring reduced use-efficiency. The response of conventional crop coefficient-based fertigation scheduling continues to be inconsistent and complex in its field implementation, thereby necessitating the intervention of sensor-based (Internet of Things; IoT) technology for fertigation scheduling on a real-time basis. The study aimed to investigate fertigation scheduling involving four levels of irrigation, viz., I₁ (100% evapotranspiration (ET) as the conventional practice), I₂ (15% volumetric moisture content (VMC)), I₃ (20% VMC), and I₄ (25% VMC), as the main treatments and three levels of recommended doses of fertigation, achieved by reappropriating different nutrients across phenologically defined critical growth stages, viz., F₁, F₂, and F₃ (conventional fertilization practice), as sub-treatments, which were evaluated through a split-plot design over two harvesting seasons in 2021–2023. Nagpur mandarin (Citrus reticulata Blanco) was used as the test crop, which was raised on Indian Vertisol facing multiple nutrient constraints. Maximum values for physiological growth parameters (plant height, canopy area, canopy volume, and relative leaf water content (RLWC)) and fruit yield (characterized by 9% and 5%, respectively, higher A-grade-sized fruits with the I₄ and F₁ treatments over corresponding conventional practices, viz., I₁ and F₃) were observed with the I₄ irrigation treatment in combination with the F₁ fertilizer treatment (I₄F₁). Likewise, fruit quality parameters, viz., juice content, TSS, TSS: acid ratio, and fruit diameter, registered significantly higher with the I₄F₁ treatment, featuring the application of B at the new-leaf initiation stage (NLI) and Zn across the crop development (CD), color break (CB), and crop harvesting (CH) growth stages, which resulted in a higher leaf nutrient composition. Treatment I₄F₁ conserved 20–30% more water and 65–87% more nutrients than the I₁F₃ treatment (conventional practice) by reducing the rate of evaporation loss of water, thereby elevating the plant’s available nutrient supply within the root zone. Our study suggests that I₄F₁ is the best combination of sensor-based (IoT) irrigation and fertilization for optimizing the quality production of Nagpur mandarin, ensuring higher water productivity (WP) and nutrient-use-efficiency (NUE) coupled with the improved nutritional quality of the fruit. Full article

Modern agriculture is increasingly challenged by the need for scalable, sustainable, and connectivity-resilient digital solutions. While existing smart farming platforms offer valuable insights, they often rely heavily on centralized cloud infrastructure, which can be impractical in rural or remote settings. To address this gap, this paper presents AGRARIAN, a hybrid AI-driven architecture that combines IoT sensor networks, UAV-based monitoring, satellite connectivity, and edge-cloud computing to deliver real-time, adaptive agricultural intelligence. AGRARIAN supports a modular and interoperable architecture structured across four layers—Sensor, Network, Data Processing, and Application—enabling flexible deployment in diverse use cases such as precision irrigation, livestock monitoring, and pest forecasting. A key innovation lies in its localized edge processing and federated AI models, which reduce reliance on continuous cloud access while maintaining analytical performance. Pilot scenarios demonstrate the system’s ability to provide timely, context-aware decision support, enhancing both operational efficiency and digital inclusion for farmers. AGRARIAN offers a robust and scalable pathway for advancing autonomous, sustainable, and connected farming systems. Full article

Rapid population growth, rising food demand, and climate change have created significant challenges to meet the water demands for agriculture. Effective irrigation water management is essential to address the world’s water crisis. The transition from conventional, frequently ineffective gravity-driven irrigations to contemporary, pressure-driven precision irrigation methods are explored in this article, addressing the difficulties associated with water-intensive irrigation, the possibility of updating conventional techniques, and the developments in smart and precision irrigation technologies. This study comprehensively analyses published literature of 150 articles from the year 2005 to 2024, based on titles, abstract, and conclusions that contain keywords such as precision irrigation scheduling, water-saving technologies, and smart irrigation systems, in addition to providing potential solutions to achieve sustainable development goals and smart agricultural production systems. Moreover, it explores the fundamentals and processes of smart irrigation, such as open- and closed-loop control, precision monitoring and control systems, and smart monitoring methods based on soil data, plant water status, weather data, remote sensing, and participatory irrigation management. Likewise, to emphasize the potential of these technologies for a more sustainable agricultural future, several smart techniques, including IoT, wireless sensor networks, deep learning, and fuzzy logic, and their effects on crop performance and water conservation across various crops are discussed. The review concludes by summarizing the limitations and challenges of implementing precision irrigation systems and AI in agriculture along with highlighting the relationship of adopting precision irrigation and ultimately achieving various sustainable development goals (SDGs). Full article