Search Results (517)

Smart Agriculture (SA) combines cutting edge technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and real-time sensing systems with traditional farming practices to enhance productivity, optimize resource use, and support environmental sustainability. A key aspect of SA is the continuous monitoring of field conditions, particularly Soil Moisture (SM), which plays a crucial role in crop growth and water management. Accurate forecasting of SM allows farmers to make timely irrigation decisions, improve field management, and conserve water. To support this, recent studies have increasingly adopted soil sensors, local weather data, and AI-based data-driven models for SM forecasting. In the literature, most existing review articles lack a structured framework and often overlook recent advancements, including privacy-preserving Federated Learning (FL), Transfer Learning (TL), and the integration of Large Language Models (LLMs). To address this gap, this paper proposes a novel taxonomy for SM forecasting and presents a comprehensive review of existing approaches, including traditional machine learning, deep learning, and hybrid models. Using the PRISMA methodology, we reviewed over 189 papers and selected 68 peer-reviewed studies published between 2017 and 2025. These studies are analyzed based on sensor types, input features, AI techniques, data durations, and evaluation metrics. Six guiding research questions were developed to shape the review and inform the taxonomy. Finally, this work identifies promising research directions, such as the application of TinyML for edge deployment, explainable AI for improved transparency, and privacy-aware model training. This review aims to provide researchers and practitioners with valuable insights for building accurate, scalable, and trustworthy SM forecasting systems to advance SA. Full article

This study introduces an IoT-enabled smart greenhouse system tailored for rice cultivation and designed as a controlled experimental platform to evaluate fertilizer application methods. Traditional greenhouse farming often struggles with unpredictable weather, pest infestations, and inefficient resource use. To overcome these challenges, the proposed system optimizes environmental conditions and enables precise monitoring and control through the Thingsboard IoT platform, which tracks temperature, humidity, and sunlight intensity in real time. The cultivation process involved Inceptisol soil preparation, slurrying, fertilization, seeding, transplantation, and continuous monitoring. The novelty lies in its dual-purpose design, enabling both cultivation and structured agronomic experimentation under identical environmental conditions. The system enables both rice cultivation and comparative testing of nano-silica fertilizer applied via root (soil) and foliar (leaf) methods. Automated temperature control (maintaining 20–36.5 °C) and humidity regulation (10–85% RH) with a mist blower response time under 5 s ensured consistent conditions. Sensor accuracy was validated with deviations of 0.4% (±0.11 °C) and 0.77% (±0.5% RH). Compared to conventional setups, this system achieved superior environmental stability and control precision, improving experimental reproducibility. Its integration potential with machine learning models opens new possibilities for forecasting plant responses based on historical data. Overall, the study demonstrates how advanced technology can enhance agricultural precision, sustainability, and research reliability. Full article

Biochar is increasingly recognized as a multifunctional soil amendment that improves soil fertility, nutrient cycling, and crop productivity. Studies across field, greenhouse, and incubation settings show that biochar enhances nutrient retention, reduces leaching, and regulates carbon, nitrogen, and phosphorus cycling. Its effects are shaped by intrinsic physicochemical properties and interactions with soil minerals, microbial communities, and enzymatic processes. Short-term benefits of biochar applications often include improved nutrient adsorption and water regulation, while long-term applications support stable soil organic matter formation, root development, and fertilizer use efficiency. Biochar also reshapes soil microbial diversity and activity. Beneficial bacterial groups such as Proteobacteria and Actinobacteria, along with fungi such as Mortierella, respond positively, enhancing nitrogen fixation, phosphorus solubilization, and organic matter decomposition. Meanwhile, biochar applications could suppress pathogens. Enzyme activities, including urease and phosphatase, are typically stimulated, driving nutrient mobilization. Yet outcomes remain context-dependent, with biochar feedstock, application rate, soil conditions, and crop type influencing results; excessive use may suppress enzymatic activity, reduce nutrient availability, or shift microbial communities unfavorably. Practically, biochar can improve fertilizer efficiency, restore degraded soils, and reduce greenhouse gas emissions, contributing to climate-smart agriculture. Future work should prioritize long-term, multi-site trials and advanced analytical tools to refine sustainable application strategies. Full article

The plant microbiome is pivotal to sustainable agriculture and global food security, yet some challenges hinder fully harnessing it for field-scale impact. These challenges span measurement and integration, ecological predictability and translation across environments and seasons. Key obstacles include technical challenges, notably overcoming the limits of current sequencing for low-abundance taxa and whole-community coverage, integrating multi-omics data to uncover functional traits, addressing spatiotemporal variability in microbial dynamics, deciphering the interplay between plant genotypes and microbial communities, and enforcing standardized controls, metadata, depth targets and reproducible workflows. The rise of synthetic biology, omics tools, and artificial intelligence offers promising avenues for engineering plant–microbe interactions, yet their adoption requires regulatory, ethical, and scalability issues alongside clear economic viability for end-users and explicit accounting for evolutionary dynamics, including microbial adaptation and horizontal gene transfer to ensure durability. Furthermore, there is a need to translate research findings into field-ready applications that are validated across various soils, genotypes, and climates, while ensuring that advances benefit diverse regions through global, interdisciplinary collaboration, fair access, and benefit-sharing. Therefore, this review synthesizes current barriers and promising experimental and computational strategies to advance plant microbiome research. Consequently, a roadmap for fostering resilient, climate-smart, and resource-efficient agricultural systems focused on benchmarked, field-validated workflows is proposed. Full article

This study presents a novel federated learning (FL) methodology implemented directly on STM32-based microcontrollers (MCUs) for energy-efficient smart irrigation. To the best of our knowledge, this is the first work to demonstrate end-to-end FL training and aggregation on real STM32 MCU clients (STM32F722ZE), under realistic energy and memory constraints. Unlike most prior studies that rely on simulated clients or high-power edge devices, our framework deploys lightweight neural networks trained locally on MCUs and synchronized via message queuing telemetry transport (MQTT) communication. Using a smart agriculture (SA) dataset partitioned by soil type, 7 clients collaboratively trained a model over 3 federated rounds. Experimental results show that MCU clients achieved competitive accuracy (70–82%) compared to PC clients (80–85%) while consuming orders of magnitude less energy. Specifically, MCU inference required only 0.95 mJ per sample versus 60–70 mJ on PCs, and training consumed ∼70 mJ per epoch versus nearly 20 J. Latency remained modest, with MCU inference averaging 3.2 ms per sample compared to sub-millisecond execution on PCs, a negligible overhead in irrigation scenarios. The evaluation also considers the payoff between accuracy, energy consumption, and latency through the Energy Latency Accuracy Index (ELAI). This integrated perspective highlights the trade-offs inherent in deploying FL on heterogeneous devices and demonstrates the efficiency advantages of MCU-based training in energy-constrained smart irrigation settings. Full article

The integration of beneficial microorganisms with sensor technologies represents a transformative advancement toward sustainable smart agriculture. This review synthesizes recent progress in combining microbial bioinoculants with sensor-based monitoring systems to enhance crop productivity, resource-use efficiency, and environmental resilience. Beneficial bacteria and fungi improve nutrient cycling, stress tolerance, and soil fertility thereby reducing the reliance on chemical fertilizers and pesticides. In parallel, sensor networks—including soil moisture, nutrient, environmental, and remote-sensing platforms—enable real-time, data-driven management of agroecosystems. Integrated microbe–sensor approaches have demonstrated 10–25% yield increases and up to 30% reductions in agrochemical inputs under optimized field conditions. We propose an integrative Microbe–Sensor Closed Loop (MSCL) framework in which microbial activity and sensor feedback interact dynamically to optimize inputs, monitor plant–soil interactions, and sustain productivity. Key applications include precision fertilization, stress diagnostics, and early detection of nutrient or pathogen imbalances. The review also highlights barriers to large-scale adoption, such as variable field performance of inoculants, high sensor costs, and limited interoperability of data systems. Addressing these challenges through standardization, cross-disciplinary collaboration, and farmer training will accelerate the transition toward climate-smart, self-regulating agricultural systems. Collectively, the integration of biological and technological innovations provides a clear pathway toward resilient, resource-efficient, and ecologically sound food production. Full article

Climate change threatens global food security, highlighting the necessity for Climate-Smart Agriculture (CSA) to enhance agricultural resilience and sustainability. Yet low adoption among farmers highlights gaps in understanding adoption barriers. Existing models often overlook the dynamic, multi-layered nature of farmers’ decisions. This study introduces the Multidimensional Dynamic Decision Analysis Framework (MDDAF), which integrates Sustainable Livelihoods Framework, Diffusion of Innovations, and Behavioral Economics, and applies it to conservation agriculture in Northeast China’s black soil region. We conducted 125 semi-structured interviews (100 farmers, stage-mapped into six groups; 20 leaders of agricultural socialized service organizations; 5 technical experts) and analyzed transcripts in NVivo using a hybrid deductive–inductive approach. Findings show stage-specific barriers: superficial knowledge and fragmented perceptions in awareness; traditional norms and social stigmatization in evaluation; biosecurity risks, ecological mismatches, and land tenure disputes during decision-making; economic constraints and policy inconsistencies during implementation; and operational failures, incomplete practices, and climate-driven volatility at confirmation. Priority implications are as follows: professionalize service provision; safeguard bundle fidelity and manage climate risk; reduce context and tenure risks; and counter misbeliefs via complement-focused demonstrations, diverse opinion leaders, and targeted training. MDDAF thus links dynamic, stage-specific barriers to actionable interventions, supporting more effective CSA scale-up. Full article

Climate change poses significant challenges to viticulture worldwide, with Eastern European vineyards experiencing increased water stress due to rising temperatures, irregular precipitation patterns, and prolonged drought periods. These climatic shifts hurt vine phenology, grape quality, and overall productivity. In response, adaptive irrigation strategies such as Regulated Deficit Irrigation (RDI) have gained attention for optimizing water use while preserving grape quality. Concurrently, the adoption of smart agriculture technologies—including soil moisture sensors, automated weather stations, remote sensing, and data-driven decision support systems—enables precise monitoring and real-time management of vineyard water status. This review synthesizes recent studies from Eastern Europe, emphasizing the necessity of integrating climate adaptation measures with intelligent irrigation management to enhance vineyard resilience and sustainability under increasing climate variability. Full article

Environmental pollution is a global issue presenting risks to ecosystems and human health through release of toxic gases, existence of volatile organic compounds (VOCs) in the environment, and heavy metal contamination of waters and soils. To effectively address this issue, reliable and real-time monitoring technology is imperative. Metal–organic frameworks (MOFs) are a disruptive set of materials with high surface area, tunable porosity, and abundant chemistry to design extremely sensitive and selective pollutant detection. This review article gives an account of recent advances towards sensor technology for MOFs with application specificity towards gas and environment monitoring. We critically examine optical, electrochemical, and resistive platforms and their interfacing with embedded electronics and edge artificial intelligence (edge-AI) to realize smart, compact, and energy-efficient monitoring tools. We also detail critical challenges such as scalability, reproducibility, long-term stability, and secure data management and underscore transforming MOF-based sensors from lab prototype to functional instruments to ensure safe coverage of human health and to bring about sustainable environmental management. Full article

Urban gardening, particularly through food-producing green spaces, is increasingly recognized as a key strategy for addressing the complex challenges of climate change, food insecurity, biodiversity loss, and social inequity in African cities. This systematic review synthesizes evidence from 47 peer-reviewed studies across sub-Saharan Africa between 2000–2025 to analyze how urban home gardens, rooftop farms, and agroforestry systems contribute to sustainable urban development. The protocol follows PRISMA guidelines and focuses on (i) plant species selection for ecological resilience, (ii) integration of modern technologies in urban gardens, and (iii) socio-economic benefits to communities. The findings emphasize the ecological multifunctionality of urban gardens, which support services such as pollination, soil fertility, and microclimate regulation. Biodiversity services are shaped by both ecological and socio-economic factors, highlighting the importance of mechanisms such as polyculture, shared labour and management of urban gardens, pollinator activity and socio-economic status, reflected in sub-Saharan urban gardens. Socioeconomically, urban gardening plays a crucial role in enhancing household food security, income generation, and psychosocial resilience, particularly benefiting women and low-income communities. However, barriers exist, including insecure land tenure, water scarcity, weak technical support, and limited policy integration. Although technologies such as climate-smart practices and digital tools for irrigation are emerging, their adoption remains uneven. Research gaps include regional underrepresentation, a lack of longitudinal data, and limited focus on governance and gender dynamics. To unlock urban gardening’s full potential, future research and policy must adopt participatory, equity-driven approaches that bridge ecological knowledge with socio-political realities. Full article

Agriculture is a major economic industry that sustains life. Moreover, plant health is a crucial aspect of a highly functional agricultural system. Because stress agents can damage crops and plants, it is important to understand what effect these agents can have and be able to detect this negative impact early in the process. Machine learning technology can help to prevent these undesirable consequences. This research investigates machine learning applications for plant health analysis and classification. Specifically, Residual Networks (ResNet) and Long Short-Term Memory (LSTM) models are utilized to detect and classify plants response to abiotic external stressors. Two types of plants, azalea (shrub) and Chinese tallow (tree), were used in this research study and different concentrations of sodium chloride (NaCL) and acetic acid were used to treat the plants. Data from cameras and soil sensors were analyzed by the machine learning algorithms. The ResNet34 and LSTM models achieved accuracies of 96% and 97.8%, respectively, in classifying plants with good, medium, or bad health status on test data sets. These results demonstrate that machine learning algorithms can be used to accurately detect plant health status as well as healthy and unhealthy plant conditions and thus potentially prevent negative long-term effects in agriculture. Full article

This study evaluated the seasonal variability of soil–atmosphere greenhouse gas (GHG) fluxes—carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O)—across a land-use gradient in the Andean–Amazon transition zone of Colombia. The gradient included five land-use types incorporating at least one innovative climate-smart practice—improved pasture (IP), cacao agroforestry system (CaAS), copoazu agroforestry system (CoAS), secondary forest with agroforestry enrichment (SFAE), and moriche palm swamp ecosystem (MPSE)—alongside the dominant regional land uses, old-growth forest (OF) and degraded pasture (DP). Soil GHG fluxes varied markedly among land-use types and between seasons. CO₂ fluxes were consistently higher during the dry season, whereas CH₄ and N₂O fluxes peaked in the rainy season. Agroecological and restoration systems exhibited substantially lower CO₂ emissions (7.34–9.74 Mg CO₂-C ha⁻¹ yr⁻¹) compared with DP (18.85 Mg CO₂-C ha⁻¹ yr⁻¹) during the rainy season, and lower N₂O fluxes (0.21–1.04 Mg CO₂-C ha⁻¹ yr⁻¹) during the dry season. In contrast, the MPSE presented high CH₄ emissions in the rainy season (300.45 kg CH₄-C ha⁻¹ yr⁻¹). Across all land uses, CO₂ was the dominant contributor to the total GWP (>95% of emissions). The highest global warming potential (GWP) occurred in DP, whereas CaAS, CoAS and MPSE exhibited the lowest values. Soil temperature, pH, exchangeable acidity, texture, and bulk density play a decisive role in regulating GHG fluxes, whereas climatic factors, such as air temperature and relative humidity, influence fluxes indirectly by modulating soil conditions. These findings underscore the role of diversified agroforestry and restoration systems in mitigating GHG emissions and the need to integrate soil and climate drivers into regional climate models. Full article

The integration of Internet of Things (IoT) technologies in agriculture has facilitated real-time environmental monitoring, with low-cost IoT-enabled agrometeorological stations emerging as a valuable tool for climate-smart farming. This systematic review examines low-cost IoT-based weather stations by analyzing their hardware and software components and assessing their potential in comparison to conventional weather stations. It emphasizes their contribution to improving climate resilience, facilitating data-driven decision-making, and expanding access to weather data in resource-constrained environments. The analysis revealed widespread adoption of ESP32 microcontrollers, favored for its affordability and modularity, as well as increasing use of communication protocols like LoRa and Wi-Fi due to their balance of range, power efficiency, and scalability. Sensor integration largely focused on core parameters such as air temperature, relative humidity, soil moisture, and rainfall supporting climate-smart irrigation, disease risk modeling, and microclimate management. Studies highlighted the importance of usability and adaptability through modular hardware and open-source platforms. Additionally, scalability was demonstrated through community-level and multi-station deployments. Despite their promise, challenges persist regarding sensor calibration, data interoperability, and long-term field validation. Future research should explore the integration of edge computing, adaptive analytics, and standardization protocols to further enhance the reliability and functionality of IoT-enabled agrometeorological systems. Full article

In response to water scarcity and low agricultural water-use efficiency in arid regions in Northwest China, this study conducted field experiments in Wuwei, Gansu Province, from 2023 to 2024. It aimed to develop a water stress diagnosis model for spring maize to provide a scientific basis for precision irrigation and water management. In this work, two irrigation methods—plastic film-mulched drip irrigation (FD, where drip lines are laid on the soil surface and covered with film) and plastic film-mulched shallow-buried drip irrigation (MD, where drip lines are buried 3–7 cm below the surface under film)—were tested under five irrigation gradients. Multispectral UAV remote sensing data were collected from key growth stages (i.e., the jointing stage, the tasseling stage, and the grain filling stage). Then, vegetation indices were extracted, and the leaf water content (LWC) was retrieved. LWC inversion models were established using Partial Least Squares Regression (PLSR), Random Forest (RF), and Support Vector Regression (SVR). Different irrigation treatments significantly affected LWC in spring maize, with higher LWC under sufficient water supply. In the correlation analysis, plant height (hc) showed the strongest correlation with LWC under both MD and FD treatments, with R² values of −0.87 and −0.82, respectively. Among the models tested, the RF model under the MD treatment achieved the highest prediction accuracy (training set: R² = 0.98, RMSE = 0.01; test set: R² = 0.88, RMSE = 0.02), which can be attributed to its ability to capture complex nonlinear relationships and reduce multicollinearity. This study can provide theoretical support and practical pathways for precision irrigation and integrated water–fertilizer regulation in smart agriculture, boasting significant potential for broader application of such models. Full article

The integration of Artificial Intelligence (AI) into remote sensing data classification has revolutionized agriculture and environmental monitoring. AI is one of the main technologies used in smart farming that enhances and optimizes the sustainability of agricultural production. The use of AI in agriculture can involve land use mapping and crop detection, crop yield monitoring, flood-prone area detection, pest disease monitoring, droughts prediction, soil content analysis and soil production capacity detection, and for monitoring the evolution of forests and vegetation. This review examines recent advancements in AI-driven classification techniques for various applications regarding agriculture and environmental monitoring to answer the following research questions: (1) What are the main problems that can be solved through incorporating AI-driven classification techniques into the field of smart agriculture and environmental monitoring? (2) What are the main methods and strategies used in this technology? (3) What type of data can be used in this regard? For this study, a systematic literature review approach was adopted, analyzing publications from Scopus and WoS (Web of Science) between 1 January 2020 and 31 December 2024. By synthesizing recent developments, this review provides valuable insights for researchers, highlighting the current trends, challenges and future research directions, in the context of achieving the Sustainable Development Goals. Full article