Search Results (570)

This study evaluated the performance of a smart farming technology (SFT) and a climate-smart agriculture (CSA) approach for improving irrigation management in pecan (Carya illinoinensis) orchards in México through soil moisture monitoring, evapotranspiration estimation, and real-time data integration. Continuous monitoring allowed irrigation to be maintained at field capacity, preventing plant stress while avoiding total soil saturation or permanent wilting point. Calibration of soil moisture sensors showed a very strong correlation (R² = 0.99) between sensor reverse voltage and volumetric soil water content in predominant sandy loam soils, confirming the reliability of the monitoring system for irrigation scheduling. Seasonal analysis of reference evapotranspiration (ETo) and crop evapotranspiration (ETc) revealed increasing atmospheric water demand during summer months, with crop coefficient (Kc) values ranging from approximately 0.3 during dormancy to 1.0–1.3 during peak vegetative growth. After five years of field implementation of the technology, results showed water savings exceeding 50% compared with traditional flood irrigation practices. The optimized irrigation schedule reduced total seasonal irrigation depth from 216 cm to 128 cm, representing a 59% reduction in applied water while maintaining adequate soil moisture conditions for crop development at field capacity (FC). These results highlight the potential of integrating sensor-based monitoring, evapotranspiration modeling, and IoT platforms to enhance water-use efficiency and support sustainable pecan production under increasing climate variability. Full article

The chicken farm is a specific type of agricultural site with high electricity and heat consumption, which makes it ideal for the implementation of green energy. The specificity of the farm (need for continuous ventilation, lighting, and heating) allows achieving energy independence and reducing costs. Small farms can meet their own electricity needs using clean energy through the application of photovoltaics and converting waste biomass to usable energy. These two ways of power production could also reduce carbon footprints. In this study, the feasibility of using renewable energy for energy management in a poultry farm by consecutively involving solar and biomass energy was revealed. A biotechnological process for the production of biogas from chicken litter in a continuously stirred system of tank bioreactors was performed. It was supplied by electricity from a photovoltaic system. To obtain the maximum amount of solar energy, a photovoltaic system consisting of four panels, invertor and a battery with smart control was designed to collect, store, and bring energy to the reactor system collector and connected to the laboratory bioreactor, conveying the biogas production process. Several hydraulic retention times (HRT) were tested for optimizing biogas (biomethane) production, reaching a maximum of 575.49 NmL CH₄/dm³ at an HRT of 13.3 days for the first bioreactor and 278.7 NmL CH₄/g VS_add at an HRT of 120 days for the whole system. The energy balance made, reporting meteorological data, showed the economic feasibility for small farms to meet their own electricity needs. Involving renewable energy technologies could solve the problem of fossil fuel dependency and waste management for environmental protection and profit increase. It would permit a transition toward sustainable energy practices in agriculture and food production. Full article

Precision agriculture employs advanced technologies to enhance farm productivity and sustainability; however, selecting the most appropriate tools can be challenging for small and medium-sized farms. This study conducts a comparative analysis of ten key precision agriculture technologies (PATs): remote sensing, GPS, GIS, VRT, soil & crop sensors, DSS, UAVs/Drones, AI & ML-based precision farming, autonomous agricultural machinery, and IoT-based smart farming. The analysis employs a neutrosophic set-based multi-criteria decision-making (MCDM) framework. Domain experts evaluated ten representative technologies using a structured questionnaire based on ten critical criteria, including spatial-temporal accuracy, data acquisition latency, scalability, robustness, interoperability, environmental resilience, economic feasibility, and agro-ecological impact. A hybrid MCDM methodology was employed, integrating neutrosophic entropy and DEMATEL to construct criterion weights. Furthermore, we utilized neutrosophic DEMATEL to identify inter-criterion causal relationships. Neutrosophic TOPSIS, enhanced by a newly proposed hybrid Cosine-Jaccard similarity measure, was introduced to rank the alternatives under conditions of uncertainty. The findings reveal that IoT-based smart farming solutions achieved the highest overall score, followed by remote sensing and decision-support system (DSS) platforms. At the same time, variable-rate technology and sensor networks received lower rankings. The findings underscore the appropriateness of particular PATs for small and medium-scale farming contexts and illustrate the effectiveness of neutrosophic MCDM in addressing ambiguity and indeterminacy. The comparative insights provide direction for researchers, policymakers, and practitioners in prioritizing precision agriculture technologies and strategies to enhance sustainable practices in small and medium-scale farming. Full article

Deep learning-based sheep face recognition technology significantly enhances the automation of individual sheep identification, providing critical technical support for smart livestock farming and precision agriculture. However, in real farming environments, factors such as complex backgrounds, illumination variations, and the high visual similarity of sheep faces severely constrain the comprehensive performance of recognition systems regarding accuracy and real-time capability. To address these challenges, we propose a cascaded framework comprising the WRT-DETR model for detection and LG-MobileViT for identification. WRT-DETR integrates multi-scale wavelet residual modeling and adaptive feature interaction into the RT-DETR architecture to effectively handle complex backgrounds. Subsequently, LG-MobileViT utilizes local–global collaborative modeling to distinguish fine-grained features while maintaining a lightweight footprint suitable for edge devices. Experiments conducted on a dataset of 400 individuals and 20,000 images demonstrate that WRT-DETR achieves 92.5% mAP50 in detection tasks. Furthermore, LG-MobileViT attains 98.97% recognition accuracy with a parameter size of only 4.57 MB. On edge computing platforms, the integrated system reaches an inference speed approaching 100 FPS. These results confirm that the proposed framework offers an efficient, reliable technical solution for non-contact, precise sheep identification in practical precision agriculture scenarios. Full article

Climate-driven droughts pose major threats to rainfed farming worldwide. To address these impacts, smart agricultural approaches focusing on conservation practices (CPs) have been widely recommended by institutions such as the Food and Agriculture Organization of the United Nations (FAO), the World Food Programme (WFP), and the International Fund for Agricultural Development (IFAD), among others. This systematic review synthesizes evidence on CPs for climate-driven drought adaptation and the barriers to their adoption in southern Mozambique, where drought is predominant. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, a comprehensive search across four academic databases retrieved 595 records (2000–April 2025), of which 23 were peer-reviewed studies. Data was extracted and analyzed using Microsoft Excel 365 and NVivo 15. As a result, five major CPs were identified: (i) Minimum tillage; (ii) Mulching and residue retention; (iii) Maize–legume (cowpea, groundnuts, pigeon pea, and soybeans) intercropping and crop rotation; (iv) Drought-tolerant maize varieties; and (v) indigenous practices. The systematic review has shown that minimum tillage was associated with 89–90% increase in maize and legume yields; Mulching expands maize yields by 24–59%; intercropping increases maize and legume yields by more than 30%; drought tolerant maize varieties expand yields by 26–46%; and local practices support farming continuity and contribute to resilience, although quantitative yield effects were not reported, with adoption ranging from 75–100%. These findings suggest that minimum tillage and intercropping/crop rotation are the most effective CPs in enhancing yield and resilience. Despite their potential, the adoption is generally low (average around 40%, with some as low as 7–16% for minimum tillage). Reasons for limited uptake include economic, cultural, institutional, biophysical, and technological barriers. These findings highlight the need for integrated policy approaches that combine climate-smart agriculture with indigenous knowledge in southern Mozambique. Full article

Timely and accurate detection of sow oestrus is crucial for enhancing reproductive efficiency and reducing non-productive days (NPDs) in large-scale pig farms. However, traditional manual observation is labour-intensive and subjective, while cloud-based deep learning solutions face challenges such as high latency and privacy risks when applied in intensive housing environments. This study developed an edge-intelligent monitoring system that integrates deep temporal modelling with sound source localisation technology. A three-stage hierarchical screening strategy was utilised to select and deploy a lightweight Stacked-LSTM model on the resource-constrained ESP32-S3 hardware platform. This model was trained and calibrated using a high-quality acoustic dataset validated against serum reproductive hormones, specifically follicle-stimulating hormone (FSH), luteinising hormone (LH), and progesterone ($P_4$). Experimental results demonstrate that the optimised model achieved a classification accuracy of 96.17%, with an inference latency of only 41 ms, thereby fully satisfying the stringent real-time monitoring requirements while maintaining a minimal memory footprint. Furthermore, the system integrates a localisation algorithm based on Generalised Cross-Correlation with Phase Transform (GCC-PHAT). Through spatial geometric modelling, the system successfully implements the functional mapping of vocalisation events to individual gestation stalls (Stall IDs). Laboratory pressure tests validated the robustness and low-cost deployment advantages of the “edge recognition–cloud synchronization” architecture, providing a reliable technical framework for the precision management of smart livestock farming. Full article

The advent of smart farms, enabled by information and communication technologies (ICT) and the Internet of Things (IoT), has improved productivity and sustainable agriculture. However, the large-scale implementation of smart farms is currently hampered by physical constraints. These constraints have led to the concept of open-field smart farming as a viable alternative. In this paradigm, data from unmanned aerial vehicles (UAVs) play a central role in effective and sustainable agricultural management. The quantitative analysis of such data requires highly reliable technological solutions. The objective of this study is to conduct a comparative analysis of image binarization algorithms for UAV-based soybean canopy extraction across growth stages and to contribute to the development of an image labeling methodology. UAVs were used to capture images of soybean fields at different growth stages, and a comparative analysis was performed using binarization image algorithms. The performance of each algorithm was evaluated using Normalized Cross Correlation (NCC) and Mean Absolute Error (MAE). The results indicate that the Excess Green (ExG) and Excess Green minus Excess Red (ExGR) vegetation indices provide accurate and stable soybean canopy extraction across growth stages when combined with Adaptive and Otsu binarization algorithms. These indices are particularly suitable for extracting soybean canopy from UAV-based data, thereby expanding the scope of precision analysis in the agricultural sector and providing data for advancing precision agriculture technology. This study contributes to the standardization and efficient use of UAV-based agricultural data processing. However, since manual weeding was performed prior to image acquisition to ensure that only soybean plants were present, reflecting standard agricultural practices in South Korea, additional validation would be required for application in fields where weeds are naturally present. Full article

The adoption of Internet of Things (IoT) technologies has significantly enhanced precision farming by enabling continuous environmental monitoring and data-driven agricultural management. However, the increasing reliance on distributed sensor networks introduces critical challenges, including sensor faults, data anomalies, and cyber-physical security threats, which can undermine system reliability and decision accuracy. This study proposes an IoT-driven anomaly detection framework for smart agriculture that integrates a Convolutional Neural Network (CNN) optimized using a hybrid Particle Swarm Optimization and Genetic Algorithm (PSO–GA). The CNN learns complex spatio-temporal patterns from multivariate sensor data, while the PSO–GA strategy automatically tunes CNN hyperparameters to improve detection accuracy and model stability. To enhance adaptability under dynamic agricultural conditions, the proposed framework incorporates an online learning mechanism that incrementally updates the CNN model using newly arriving sensor data, enabling continuous adaptation to environmental changes and concept drift without full model retraining. Experiments conducted on a publicly available smart agriculture dataset demonstrate that the proposed CNN–PSO–GA framework achieves an accuracy of 74%, precision of 74%, recall of 100%, and an F1-score of 85%, outperforming baseline methods such as One-Class Support Vector Machine and Isolation Forest, particularly in reducing missed anomaly events. The results confirm the robustness, adaptability, and reliability of the proposed approach. Overall, the framework provides a practical and scalable solution for enhancing security, resilience, and operational effectiveness in precision farming systems. Full article

As global offshore wind power advances toward deeper, farther waters, harsh Operation and Maintenance (O&M) environments, equipment heterogeneity, and flaws in existing communication (e.g., insufficient 4G bandwidth, high-latency/cost satellite communication) drive the urgent need for intelligent O&M. This paper expounds on the development of Far-Reaching Sea Smart Wind Farms and intelligent service communication demands, studies 5G deployment schemes (hybrid networking, frequency selection, in-turbine coverage, 5G custom networks) and practical cases, discusses core edge intelligence applications (equipment monitoring, inspection, fault diagnosis, digital twin integration), and constructs a “terminal-edge-cloud-network” 5G-edge intelligence integrated architecture. It also summarizes key technology effects, points out current challenges, and looks forward to lightweight large language model deployment at the edge, providing references for 5G edge intelligence implementation in offshore wind power intelligent O&M. Full article

The transition toward sustainable and circular bioeconomies in Agriculture 4.0 demands fertilization strategies that reduce environmental impacts while maintaining agronomic productivity. This article presents a structured narrative review of peer-reviewed literature integrating evidence across waste management, soil science, food safety, and regulatory frameworks to evaluate the potential of seafood processing byproducts including fish offal, shellfish residues, and aquaculture effluents as nutrient-rich fertilizers. These materials provide nitrogen, phosphorus, calcium, and essential micronutrients and may contribute to nutrient recycling within precision and resource-efficient agricultural systems. Evidence from diverse cropping contexts indicates that seafood waste-derived fertilizers can improve crop yield, nutrient use efficiency, and soil biological activity under site-specific conditions. Biological processing methods, including composting, enzymatic hydrolysis, and fermentation, are examined for their roles in enhancing nutrient bioavailability and reducing undesirable constituents. Particular emphasis is placed on food safety considerations, including heavy metals, persistent organic pollutants, antimicrobial resistance, pathogens, and microplastics, with discussion of speciation-based risk assessment and mitigation strategies such as thermal treatment, microbial screening, and compliance with international standards. Regulatory fragmentation, economic feasibility, and lifecycle environmental implications are also critically assessed. Emerging digital tools, including Internet of Things (IoT)-enabled nutrient monitoring and artificial intelligence (AI)-assisted compost optimization, are discussed as enabling technologies for integrating seafood-derived biofertilizers into smart farming systems. Overall, this interdisciplinary synthesis highlights the potential contribution of seafood waste valorization to circular nutrient management, environmental stewardship, and sustainable food production. Full article

Artificial intelligence (AI) is rapidly affecting soil science by allowing the analysis of large, complex, and heterogeneous datasets that were previously difficult to exploit. The current review synthesizes the recent advances of AI and highlights how these tools are applied in key soil science domains, such as digital soil mapping, soil fertility management, soil moisture prediction, contamination monitoring, soil carbon assessment, and precision agriculture. This study evaluates the performance of different AI methods, showing that techniques such as random forests, neural networks, and convolutional neural networks often outperform traditional methods in capturing non-linear soil-environment. At the same time, it identifies major limitations such as data scarcity, reproducibility, lack of large datasets, uncertainty, and the “black-box” nature of many models. This review concludes that AI has strong potential to support sustainable soil management, but its real-world impact will depend on better data integration, explainability, standardization, and closer collaboration with scientists, technologists, and end-users. Full article

Globally, the swine industry is a major component of agricultural production, and the increasing scale and intensification of pig farming have heightened concerns about NH₃ emissions. As farms expand and adopt smart farming technologies, there is a need for reliable prediction of NH₃ concentrations without relying solely on costly physical sensors. In this study, we developed an artificial intelligence-based prediction model for NH₃ concentration in commercial pig houses and examined the effects of data collection intervals and learning strategies. We compared a standalone model trained only on local data with a transfer learning model that adapts a pre-trained model to a target farm with limited data. Transfer learning consistently outperformed the standalone approach across all data collection intervals (10, 20, 30 and 60 min). The best-performing Random Forest and XGBoost models achieved a coefficient of determination (R²) of 0.969, root mean square error (RMSE) of about 1.0 ppm and mean absolute percentage error (MAPE) below 5%. These results show that transfer learning can provide accurate NH₃ predictions in swine housing even with sparse data, supporting more sustainable and data-efficient environmental management. Full article

This study examines Chinese farmers’ stated preferences and the compensation they would be willing to accept (willingness to accept; WTA) in return after adopting the Visualization System (VS), a promising method of smart agricultural technology. Using discrete choice experiments and a mixed logit model, we investigate farmers’ preferences under uncertain price premiums. Specifically, premium is defined as the additional price increment associated with VS adoption, reflecting the potential market reward for improved transparency, traceability, and other benefits. Uncertainty is measured by different fluctuation levels of this premium. We also assess the impacts of farmers’ individual characteristics on their WTA. Results (n = 348) show that farmers prefer higher premiums and lower fluctuations. Better VS knowledge reduces farmers’ WTA by 0.439 CNY/kg, and younger farmers tend to be more tolerant of fluctuations. Among younger farmers, those without off-farm income are more sensitive to fluctuations than those with off-farm income. Importantly, enhancing farmers’ VS knowledge leads to a 50.3% decrease in the implied price relative to the reference price, suggesting it may be more effective than mitigating fluctuations or targeting younger farmers. Overall, our findings highlight the potential of smart agriculture in China and suggest that enhancing farmers’ awareness and understanding of the VS is key to accelerating adoption. Full article

Wireless communication systems, which rely on radio frequencies (RFs), are widely utilized in various applications, such as mobile communications, radio frequency identification, marine networks, smart farms, and smart homes. Due to their ease of installation, wireless systems offer advantages over wired alternatives. But the deployment of high-frequency radio waves for a communication system can pose potential health risks. To address these concerns, many researchers have explored the use of visible light as a safer alternative to radio frequency communication. In this context, optical camera communication has emerged as a good candidate compared to the RF system. Meanwhile, artificial intelligence (AI) is reshaping industries and human life by solving complex problems, enabling intelligent automation, and driving advancements in technologies such as smart farms, smart homes, and future internet of things systems. In this study, we recommend a Multiple-Input Multiple-Output Camera On–Off Keying (MIMO C-OOK) modulation that integrates a YOLOv11 for light source detection and tracking and a deep learning network-based decoder algorithm, optimized for long-range and mobility communication scenarios. The proposed approach enhances the conventional C-OOK system by increasing the data rate and transmission range while reducing errors at the receiver. Implementation results show that the proposed approach can achieve reliable communication up to 10 m with minimal errors, even under mobility conditions (3 m/s, equivalent to walking speed), by optimizing camera parameters and employing forward error correction (FEC). Full article

Considering the present environmental concerns, nanomaterial-based methods should be applied to achieve the bioeconomic sustainability initiatives and climate change mitigation. Plants and plant extracts are one of the most underused biomass and bioactive ingredients resources. Moreover, nowadays crop loss is one of the main problems that the world faces, together with the depletion of natural resources, increasing population and limited arable land, leading to increased food scarcity and demand. To correctly attribute/use plant-based bioresources or to rapidly decide which farming operations should be performed before crop loss, we should be able to properly characterize plants or plant-based resources by the desired useful characteristics, such as (bio)chemical characteristics, rather than simply observing physical traits of plants (because, when these traits become visible, it may be too late for crop loss mitigation). Plant crops could be optimized, for example, using electrochemical methods that assess the nutrient uptake and nutrient use efficiency (NUE) or the oxidative stress burst encountered before crop loss, in order to improve crop yields and crop quality. Other different important analytes (such as hormones, pathogens, metabolites, etc.) or plant characteristics (such as genus, species, phylogenetic analysis, etc.) can be evaluated with these electrochemical sensors and methods. In the present review, we focus on the application of nanomaterials/nanotechnologies for the development of fast, accurate, accessible, cost-effective, sensitive and selective analytical electrochemical methods for the detection of different relevant biomolecules in plants or plant-related samples (plant extracts, plant cells, plant tissues, and/or plant-derived natural drinks/foods, as well as entire plants/plant parts), both in vivo vs. ex vivo and in situ vs. ex situ. This review systematically presents and critically discusses the outcomes of current electrochemical methods (both applied in the lab or as wearable/implantable sensors) and the future perspectives of these nanotechnology-based sensors, with an accent on wearable sensors for smart and precision agriculture, as real-world sensing technologies with significant practical impact. The novelty of this article is the abundance of electrochemical analytical parameters gathered and discussed, for such a large number of analyte categories. Full article