Search Results (363)

Mortality events in commercial rabbit production can lead to significant economic losses, highlighting the need for earlier identification of elevated mortality risk at the group level using routinely collected production data. This study presents a machine learning–based framework for predicting mortality risk at future observation points using routinely collected production data. Models were developed using group-level variables and evaluated with StratifiedGroupKFold cross-validation to prevent information leakage. The selected XGBoost model achieved a balanced performance, with a recall of 0.78 ± 0.03, precision of 0.59 ± 0.04, and ROC–AUC of 0.72 ± 0.02. Predictions were translated into an alert system based on a predefined threshold, prioritising sensitivity while maintaining a moderate false alert rate. A scenario-based cost–benefit analysis indicated that economic outcomes are highly dependent on intervention effectiveness, with positive returns observed under moderate to optimistic assumptions. Overall, the framework demonstrates the feasibility of integrating predictive modelling with alert-based decision support in rabbit production, although real-world validation under commercial farm conditions is required to confirm its practical effectiveness. Full article

Digital transformation is frequently argued to improve how agricultural businesses compete, coordinate, and capture value in markets, yet evidence remains limited of how regional innovation ecosystems shape farms’ digital readiness and how this readiness translates into accounting-relevant outcomes. This study addresses that gap by linking regional innovation capacity, observed farm digital readiness, and accounting performance within a single regional analytical framework. Using cross-sectional data for 180 EU NUTS2 regions (2023), we estimate a moderated mediation model with formative constructs based on harmonized secondary indicators. This study is original in shifting the analysis from the farm or firm level to the regional scale and in operationalizing digital readiness through observable uptake of precision technologies, robotics, livestock-management machinery, internet access, and management information systems. Regional innovation capacity is positively associated with farmers’ digital readiness, and digital readiness is positively associated with accounting performance in the baseline specification. The indirect pathway from innovation capacity to accounting performance via digital readiness is significant, consistent with digital readiness acting as a transmission channel through which ecosystems relate to measurable economic outcomes. Managerial composition conditions these relationships: the share of managers under 40 weakens both the ecosystem-to-digital link and the digital-to-performance link, while female managerial share shows only marginal moderation of the first stage and no significant moderation of the second. The findings provide a basis for future multilevel research and place-based policies and advisory actions aimed at strengthening digital uptake where regional innovation capacity is weaker. Full article

Precision Livestock Farming (PLF) tools are increasingly used in dairy production, but their success depends on farmers’ perceptions, needs and investment capacity. This study explores the current use of digital technologies, satisfaction levels and future expectations among Italian dairy farmers. An online questionnaire with 19 questions collected 53 complete responses between May and November 2025. Most of the farms were free-stall Holstein dairy farms located in the Po Valley and managed by relatively young and well-educated farmers, many of whom had a background in animal production. The adoption of PLF tools was widespread: management software (73.6%), automated total mixed ration (TMR) preparation (66.0%), heat stress mitigation systems (62.3%) and collar sensors (52.8%) were the most adopted technologies. Satisfaction with current tools was high, although installation costs and poor system integration were consistently identified as major constraints. Farmers expressed clear priorities for future devices, particularly early diagnosis of health problems, calving, heat, lameness, and feeding and rumination functions. The results suggest that PLF in Italian dairy systems is moving from the adoption phase to that of consolidation. However, improvements in interoperability, affordability and farmer-centred design remain essential to support a wider and more equitable spread of the technology across the sector. Full article

This study presents a nanosensor network system for autonomous microclimate optimization in precision horticulture, leveraging a field-programmable gate array (FPGA)-based control architecture that is integrated with an edge-level machine learning inference. Unlike the conventional greenhouse automation systems, which exhibit thermal and hygroscopic hysteresis often exceeding 32 °C and 78% relative humidity, the proposed framework embeds a random forest regression (RFR) model directly within the Altera DE2-115 FPGA fabric to enable predictive environmental regulation. The model achieved an R² of 0.985 and root mean square error (RMSE) of 0.28 °C, allowing proactive compensation for the thermodynamic disturbances from the high-intensity light-emitting diode (LED) lighting with a 120 s predictive horizon. The real-time monitoring and remote supervision were supported via a NodeMCU-based IoT gateway, achieving a 140 ms mean communication latency and a 99.8% packet delivery reliability. The preliminary validation using lettuce (Lactuca sativa) optimized the environmental parameters, while the subsequent experiments with pepper (Capsicum annuum), a commercially important and environmentally sensitive crop, demonstrated system performance under real-world conditions. The control system maintained a temperature and humidity within ±0.3 °C and ±1.2% of the setpoints, respectively, and outperformed the baseline rule-based control with a 28% increase in fresh biomass, a 22% improvement in dry matter accumulation, a 25% reduction in actuator duty-cycle switching, and an 18% decrease in overall energy consumption. These results highlight the efficacy of FPGA-integrated edge intelligence combined with low-latency IoT telemetry as a scalable, energy-efficient, and high-fidelity solution for sub-degree environmental control in next-generation, controlled-environment, and vertical farming systems. Full article

Precision agriculture has become a major direction of agricultural technological development in recent decades, addressing efficiency, environmental, and economic challenges simultaneously. Input optimization based on site-specific data collection—particularly variable-rate nutrient application, precision irrigation systems, and targeted crop protection—has been shown to generate measurable cost and resource savings. The aim of the study is to explore and systematically evaluate the economic impacts influencing precision technology in crop production. Although the technical and environmental benefits of precision technologies are widely documented, their economic performance and farm-level profitability remain inconsistently interpreted. The study is based on a systematic literature review of peer-reviewed English-language journal articles retrieved from the Web of Science, Scopus, ScienceDirect, and JSTOR databases. Study selection and evaluation were conducted in accordance with the PRISMA 2020 methodological framework. The literature indicates that precision technologies achieve average input savings of 8–20% and yield increases of 2–6%, while reported return on investment (ROI) values typically range between 5% and 15%. Economic viability is strongly dependent on farm size, with most studies identifying profitability above 100–200 ha. Additional benefits include improved management of soil heterogeneity, enhanced nutrient-use efficiency, and reduced excess input application, although adoption remains constrained by high investment costs and technological complexity. Full article

Increasing agricultural productivity is vital for global food security, but it poses significant risks to aquatic ecosystems through diffuse pollution. As Türkiye aims to harmonise its agricultural policies with the European Green Deal, quantifying agricultural non-point-source pollution (ANPSP) is essential for sustainable water management. This study evaluates ANPSP loads, including Total Nitrogen (TN), Total Phosphorus (TP), Chemical Oxygen Demand (COD), and Ammonia Nitrogen (NH₃-N), originating from cereal production, fertiliser application, and livestock farming across Türkiye from 2015 to 2024. By employing activity data and pollution load coefficients, the spatiotemporal dynamics of ANPSP were analysed at both national and regional levels. The results demonstrate that cereal production is the predominant source of nutrient loading (60.5% TN, 64.9% TP), whereas livestock activities account for 52.2% of the COD load. Fertiliser use contributed 23.0% and 20.6% to TN and TP loads, respectively. The Marmara, Aegean, and Central Anatolia regions were identified as high-intensity pollution hotspots. These findings provide a robust baseline for developing region-specific mitigation strategies, such as precision fertilisation and circular waste-to-energy systems, to support Türkiye’s transition toward a Zero-Pollution and sustainable agricultural future. Full article

Forecasting crop pest outbreaks under conditions of increasing agroclimatic variability is a critical task for intelligent decision support systems in agriculture. Traditional statistical and empirical models typically have limited transferability and insufficient accuracy when describing nonlinear and multiscale relationships between climatic factors and pest population dynamics. This paper proposes a hybrid algorithm combining wavelet analysis and deep learning methods for forecasting agroclimatic pest infestation levels. The algorithm is based on multiscale decomposition of time series using a discrete wavelet transform, after which the extracted components are used as input features for a deep neural network implementing a nonlinear mapping between climatic parameters and infestation indicators. The developed computational framework includes the stages of data preprocessing, feature space formation, model training, and forecast generation in a single, reproducible pipeline. An experimental evaluation using long-term agroclimatic and phytosanitary data showed that the proposed algorithm outperforms classical regression and individual neural network models in terms of RMSE, MAE, and the coefficient of determination. The results confirm the effectiveness of integrating wavelet analysis and deep learning for developing phytosanitary risk forecasting algorithms and demonstrate the potential of the proposed approach for implementation in intelligent precision farming systems. Full article

To accurately manage precise feeding and water quality regulation in the rice–duck–crayfish integrated system (RDCI), the continuous monitoring of plankton and physicochemical parameters in the water was conducted from March 2022 to January 2023 in both the RDCI and the rice–crayfish continuous culture system (RCCC). The results showed that a total of 188 phytoplankton species and 92 zooplankton species were identified in the RDCI, whereas 152 phytoplankton species and 95 zooplankton species were detected in the RCCC. The phytoplankton community composition was similar between these two systems. For zooplankton, Rotifera was the dominant group. However, Chlorophyta and Bacillariophyta were the dominant phytoplankton groups. Compared with the RCCC, the RDCI exhibited lower plankton density during the crayfish-farming stage and overwintering stage, but higher plankton biomass during the crayfish-farming stage, overwintering stage, and rice maturity stage. The diversity indices, richness indices, and evenness indices of both phytoplankton and zooplankton in the RDCI were significantly higher than those in the RCCC. Correlation analysis indicated that water temperature, dissolved oxygen, total nitrogen, and ammonia nitrogen were the key environmental factors affecting plankton community structure. In summary, compared with the RCCC, the RDCI exhibits higher plankton diversity and better evenness, suggesting a more complex and stable community structure. The species composition of plankton and related indices indicate that the RDCI mitigates the degree of eutrophication in water during both the crayfish farming and the overwintering stages, while increasing nutrients levels during the rice planting stage. This approach is beneficial for reducing non-point-source pollution in agriculture and promoting green agricultural development. Full article

The proposed study investigates the key factors influencing UAV adoption and proposes an integrated educational–operational framework to enhance implementation in agricultural practice. A case study in Sibiu County, Romania, combined survey-based empirical analysis (n = 80), strategic environmental assessment and the deployment of a demonstration aerial crop monitoring system for educational purposes (ACMS-E). We integrated the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB) to examine adoption intentions, revealing perceived usefulness (β = 0.355, p = 0.021) and positive attitudes (β = 0.382, p = 0.005) as the strongest predictors, explaining 44.1% of variance. Based on these findings, a modular training curriculum was designed, combining theoretical instruction, flight operation exercises, remote sensing techniques, data analytics and farm-management integration. ACMS-E provides hands-on training and promotes capacity-building, bridging the gap between technological availability and real-world adoption. By linking technological capabilities with structured training, ACMS-E bridges the gap between UAV availability and effective implementation, offering a scalable model for precision agriculture. This framework provides a pathway to accelerate UAV adoption, optimize field-level monitoring and support evidence-based, resource-efficient farm management in emerging and developed agricultural contexts. Full article

Crop monitoring over large land extensions represents a central challenge in precision agriculture, especially in polyculture contexts where species with different nutritional needs are combined. This study presents a methodology to manage and analyze large volumes of multispectral images captured by unmanned aerial vehicles (UAVs) in order to identify and monitor crops at the plant level. The images are efficiently stored and retrieved using a Hilbert Curve, which reduces the complexity of the search process from $O\left(n^2\right)$ to $O(log(n\left)\right)$ where n represents the number of indexed data points). The system connects to a distributed Structured Query Language (SQL) database, allowing for fast image retrieval based on GPS coordinates and other metadata. Additionally, the Normalized Difference Vegetation Index (NDVI) is calculated using reflectance data from the red and near-infrared channels, adjusted by semantic segmentation masks generated with a U-Net model, which allows for species-specific evaluations. The methodology was evaluated on a 20,000 m² polyculture farm with coffee, avocado, and plantain crops, using a dataset of 270 aerial images partitioned into 70% for training and 30% for validation. The results show improvements in retrieval speed and precision with the Hilbert Space-Filling Curve (HSFC) approach, and an accuracy of 82.3% and an the Mean Intersection over Union (MIoU) of 68.4% in species detection with the U-Net model. Overall, this integrated framework demonstrates a scalable potential for precision agriculture in complex polyculture systems, facilitating efficient data management and targeted crop interventions. Full article

Goats are important livestock species in most rural households and were amongst the first species to be domesticated. Despite this, their production is based on extensive systems, exposing them to numerous challenges affecting their productivity. This review examines the applications of precision livestock farming (PLF) and AI-driven technologies in goat management, focusing on their impacts on productivity, welfare, genetic potential, health monitoring, feeding efficiency and sustainability outcomes and identifying challenges for their adoption in smallholder and extensive systems. Unlike previous reviews that focus mainly on cattle raised under intensive systems, this review synthesizes their use in goat production and highlights technological, socio-economic and infrastructural constraints. A conventional literature review approach is used, with studies retrieved from major databases using relevant keywords. The selected studies are evaluated to assess technological applications, benefits and adoption challenges, followed by a SWOT analysis. Engineering aspects of precision livestock farming—including sensors, data connectivity, system integration, automation and scalability—are also discussed. Ideally, these technologies operate as integrated decision-support systems that jointly improve productivity, animal welfare and sustainability, rather than performing isolated tasks. However, many PLF solutions remain at low technology-readiness levels and are constrained by infrastructure gaps, sensor reliability and compatibility issues, which collectively limit adoption in smallholder systems. Future research should focus on the development of cost-effective, reliable PLF systems for smallholder producers, while policy and capacity-building initiatives are needed to enhance infrastructure, training and technology adoption for scalable implementation. Full article

Apple snail infestation poses a persistent threat to rice production in open-field environments, where long-term coexistence with this species is unavoidable. This study presents a drone-based precision control approach that integrates high-resolution micro-topographic mapping with site-specific pesticide application. A lightweight mapping unmanned aerial vehicle was deployed to produce centimeter-level microtopographic data across paddy fields, facilitating the identification of deep-water areas preferred by apple snails. From these elevation-derived water risk patterns, prescription maps were generated to guide downstream management decisions, and agricultural drones equipped for granular application subsequently performed targeted pesticide delivery only in these high-risk areas. Over 2 years of field experiments, the proposed method achieved rice yields comparable to those under conventional management while reducing pesticide use by 44.1–63.0%, with lower estimated crop damage in regions with high apple snail occurrence. Designed with robustness and scalability in mind, the system demonstrated considerable potential for practical implementation in general farming households and broader applications in precision pest management. Full article

Tropical estuarine hydrodynamic processes are governed by complex interactions between tides, monsoons, and fluvial runoff. To obtain long-term (≥30 years) hydrodynamic conditions of the Mekong River Estuary, this study established a Finite Volume Coastal Ocean Model (FVCOM) coupled with validated Weather Research and Forecast (WRF) wind forcing for a 32-year (1988–2019) high-resolution simulation. Validation against in situ observations confirms the model’s robustness. Temporal–spatial patterns of water level and current were analyzed, and extreme parameters for 1–100 year return periods were derived via the Pearson-III probability distribution. Results indicate the study area is a mesotidal environment (tidal range = 3.58 m) dominated by SSE-NNW reciprocating tidal currents. Relative to Vietnam’s national elevation datum, 100-year return period extreme high/low water levels are 2.15 m and −2.03 m, with a maximum storm surge setup of 2.09 m. The 100-year return period maximum current velocity reaches 4.58 m/s (A21 station), and Mekong River runoff exerts a negligible influence (<5% velocity change). This study provides high-precision baseline data for offshore wind farm engineering and disaster risk assessment, offering a methodological reference for tropical estuarine hydrodynamic simulations. Full article

In modern smart dairy farms, precise feed management and accurate monitoring of dairy cows’ feeding behavior are crucial for improving production efficiency and reducing feeding costs. However, in practical applications, complex environmental factors such as varying illumination, frequent occlusion, and dense multi-targets pose significant challenges to real-time visual perception. To address these issues, this paper proposes a lightweight multi-target detection model, BFDet-YOLO, for the joint detection of dairy cows’ feeding behavior and feed density levels in pasture environments. Based on the YOLOv10 framework, the model incorporates four targeted improvements: (1) a bidirectional feature fusion network (BiFPN) to address the insufficient multi-scale feature interaction between dairy cows (large targets) and feed particles (small targets); (2) a lightweight downsampling module (Adown) to preserve fine-grained features of feed particles and reduce the risk of small target miss detection; (3) an attention-enhanced detection head (SEAM) to mitigate occlusion interference caused by cow stacking and feed accumulation; (4) an improved bounding box regression loss function (DIoU) to optimize the localization accuracy of non-overlapping small targets. Additionally, this paper constructs a pasture-specific dataset integrating dairy cows’ feeding behavior and feed distribution information, which is annotated and expanded by combining public datasets with on-site monitoring data. Experimental results demonstrate that BFDet-YOLO outperforms the original YOLOv10 and other mainstream target recognition models in terms of detection accuracy and robustness while maintaining a significantly streamlined model scale. On the constructed dataset, the model achieves 95.7% mAP@0.5 and 70.7% mAP@0.5:0.95 with only 1.85 M parameters. These results validate the effectiveness and deployability of the proposed method, providing a reliable visual perception solution for intelligent feeding systems and smart pasture management. Full article

Achieving sustainable rice production is essential for food security, rural livelihoods, and the resilience of rice-based food systems that supply raw materials to the food processing industry. Improving technical efficiency (TE) at the farm level is a key pathway to reduce resource use and environmental pressures per unit of output while ensuring a stable supply of high-quality rice for downstream processing and value-added products. Drawing on micro-survey data collected in 2021–2022 from 455 rice farmers selected through a multi-stage sampling strategy in Jiangsu Province, China, this study investigates how agricultural productive services (APSs) affect stage-specific technical efficiency along the production process and discusses the implications for sustainable rice production and the rice-based food industry. We apply a stochastic frontier production function to estimate overall and stage-specific TE and examine the effects of different APS combinations for land preparation, sowing, fertilization, pest control, and harvesting. The results show that overall participation in APSs significantly improves rice farmers’ TE. Stage-specific analysis reveals that APSs in land preparation, sowing, and harvesting are associated with higher TE, supporting more sustainable use of machinery and labor, while APSs for fertilization and pesticide application do not consistently improve TE and may reflect potential overuse of chemical inputs. Multi-stage service combinations that include both production and pest-control operations can further enhance TE. These findings suggest that well-designed APSs can contribute to sustainable intensification and low-carbon transformation of rice production, thereby strengthening the sustainability of rice-based food systems. Policy interventions should guide APS providers and farmers toward integrated, precision-oriented, and environmentally friendly service packages that support both farm-level efficiency and the sustainability goals of the broader food industry. Full article