Search Results (1,332)

Pear cultivation in Greece, with an annual production of approximately 81,000 tonnes, constitutes a significant segment of the national fruit industry, particularly in Northern regions such as Macedonia and Thessaly. Despite ranking 8th in the EU in terms of pear production, Greece’s cultivated area is slightly declining, and adoption of smart agriculture technologies (SAT) remains limited. In this context, the present study investigates the preferences, patterns, and barriers of SAT adoption within the Greek pear sector, aiming to lay the groundwork for more effective digital transformation in the agri-food domain. Using a structured interview-based survey, data were collected from 30 pear growers, revealing critical insights into the technological landscape of the sector. A central challenge that emerged was the insufficient internet connectivity in rural farming areas, highlighting the urgent need for improved digital infrastructure to support SAT deployment. Furthermore, the study emphasizes the importance of targeted education and awareness programs to bridge the digital knowledge gap among pear farmers. An especially notable finding concerns the role of the chosen tree training system in influencing SAT uptake: more than 50% of adopters utilize the palmette training system, suggesting a strong correlation between orchard design and technological readiness. Among the SAT categories, Data Analytics and Farm Management Software were the most widely adopted, a trend partly driven by attractive governmental subsidies of €30 per hectare. Importantly, all respondents who had implemented SAT (100%) reported a measurable increase in farm income, reinforcing the technologies’ impact on productivity and profitability. Foremost among the challenges encountered is the deficit in technical knowledge and training. In conclusion, this study offers a comprehensive overview of Greek pear producers’ perceptions, challenges, and emerging opportunities related to smart agriculture. Full article

Climate change poses a threat to global rice production by increasing the frequency and intensity of extreme weather events. The widespread cultivation of genetically uniform modern varieties has narrowed the genetic base of rice, increasing its vulnerability to these increased pressures. Rice landraces are traditional rice varieties that have been cultivated by farming communities for centuries and are considered crucial resources of genetic diversity. These landraces are adapted to a wide range of agro-ecological environments and exhibit valuable traits that provide tolerance to various biotic stresses, including drought, salinity, nutrient-deficient soils, and the increasing severity of climate-related temperature extremes. In addition, many landraces possess diverse alleles associated with resistance to biotic stresses, including pests and diseases. In addition, rice landraces exhibit great grain quality characters including high levels of essential amino acids, antioxidants, flavonoids, vitamins, and micronutrients. Hence, their preservation is vital for maintaining agricultural biodiversity and enhancing nutritional security, especially in vulnerable and resource-limited regions. However, rice landraces are increasingly threatened by genetic erosion due to widespread adoption of modern high-yielding varieties, habitat loss, and changing farming practices. This review discusses the roles of rice landraces in developing resilient and climate-smart rice cultivars. Moreover, the Pantiange Heigu landrace, cultivated at one of the highest altitudes globally in Yunnan Province, China, has been used as a case study for integrated conservation by demonstrating the successful combination of in situ and ex situ strategies, community engagement, policy support, and value-added development to sustainably preserve genetic diversity under challenging environmental and socio-economic challenges. Finally, this study explores the importance of employing advanced genomic technologies with supportive policies and economic encouragements to enhance conservation and sustainable development of rice landraces as a strategic imperative for global food security. By preserving and enhancing the utilization of rice landraces, the agricultural community can strengthen the genetic base of rice, improve crop resilience, and contribute substantially to global food security and sustainable agricultural development in the face of environmental and socio-economic challenges. Full article

Bacterial and viral diseases significantly reduce soybean (Glycine max) yield and quality. RNA modifications, particularly N⁶-methyladenosine (m⁶A), are increasingly recognized as having a regulatory role in plant–pathogen interactions, but the m⁶A methylome of soybean during viral and bacterial infection has not yet been characterized. Here, we performed transcriptome sequencing and MeRIP-seq (methylated RNA immunoprecipitation followed by high-throughput sequencing) of soybean leaves infected with Soybean mosaic virus (SMV) and/or Pseudomonas syringae pv. glycinea (Psg). In general, m⁶A peaks were highly enriched near stop codons and in 3′-UTR regions of soybean transcripts, and m⁶A methylation was negatively correlated with transcript abundance. Multiple genes showed differential methylation between infected and control plants: 1122 in Psg-infected plants, 539 in SMV-infected plants, and 2269 in co-infected plants; 195 (Psg), 84 (SMV), and 354 (Psg + SMV) of these transcripts were both differentially methylated and differentially expressed. Interestingly, viral infection was predominantly associated with hypermethylation and downregulation, whereas bacterial infection was predominantly associated with hypomethylation and upregulation. GO and KEGG enrichment analysis revealed shared processes likely affected by changes in m⁶A methylation during bacterial and viral infection, including ATP-dependent RNA helicase activity, RNA binding, and endonuclease activity, as well as specific processes affected by only one pathogen. Our findings shed light on the role of m⁶A modifications during pathogen infection and highlight potential targets for epigenetic editing to increase the broad-spectrum disease resistance of soybean. Full article

This paper presents a low-cost, fully on-premise Edge Artificial Intelligence (AI) system designed to support real-time pest and disease detection in open-field chili pepper cultivation. The proposed architecture integrates AI-Thinker ESP32-CAM module (ESP32-CAM) image acquisition nodes (“Sticks”) with a Raspberry Pi 5–based edge server (“Module”), forming a plug-and-play Internet of Things (IoT) pipeline that enables autonomous operation upon simple power-up, making it suitable for aging farmers and resource-limited environments. A Leaf-First 2-Stage vision model was developed by combining YOLOv8n-based leaf detection with a lightweight ResNet-18 classifier to improve the diagnostic accuracy for small lesions commonly occurring in dense pepper foliage. To address network instability, which is a major challenge in open-field agriculture, the system adopted a dual-protocol communication design using Hyper Text Transfer Protocol (HTTP) for Joint Photographic Experts Group (JPEG) transmission and Message Queuing Telemetry Transport (MQTT) for event-driven feedback, enhanced by Redis-based asynchronous buffering and state recovery. Deployment-oriented experiments under controlled conditions demonstrated an average end-to-end latency of 0.86 s from image capture to Light Emitting Diode (LED) alert, validating the system’s suitability for real-time decision support in crop management. Compared to heavier models (e.g., YOLOv11 and ResNet-50), the lightweight architecture reduced the computational cost by more than 60%, with minimal loss in detection accuracy. This study highlights the practical feasibility of resource-constrained Edge AI systems for open-field smart farming by emphasizing system-level integration, robustness, and real-time operability, and provides a deployment-oriented framework for future extension to other crops. Full article

This study evaluates the impact of a decision support system (DSS) on the sustainability of potato production in Cyprus. Field experiments were conducted over three consecutive years (2022–2024), comparing conventional farming with smart farming (SF) guided by the DSS. Thirteen sustainability indicators were assessed, revealing clear trade-offs between input-use efficiency and yield stability. While SF significantly reduced input use—fertilizers, pesticides, irrigation water, and labor—average yields declined by 21%. These results provide new evidence on the environmental and economic implications of SF in high-input farming systems, supporting efforts to promote more sustainable agricultural practices in Cyprus and beyond. Full article

The agricultural sector is predominantly male, with approximately 30% of farms in the EU operated by women. The European Union Rural Pact, the Agri-Food Pact for Skills, and the Common Agricultural Policy have catalysed an increase in agricultural 4.0 research, with the role of women emerging as a subfield of sustainable agriculture. The primary objective of this paper is to evaluate the current literature on women’s roles in smart agriculture, examining the advantages of their participation as a digitally competent workforce that could catalyse improvements in productivity and resilience in rural areas and promote women’s empowerment. A bibliometric study was conducted utilising the Scopus database to fulfil the research objective. This led to the incorporation of 253 articles into the sample. The records were examined using performance analysis and bibliographic coupling (science mapping), facilitated by Biblioshiny 5.0 and VOSviewer 1.6.20 software. The primary findings elucidate essential concepts, predominant study themes, and the temporal progression of the research domain. The identification of numerous women’s role and socio-economic constraints affecting women, which are overlooked in the creation and implementations of technology advancements. Additionally, a research agenda was developed, alongside practical implications for managers and policymakers, to aid the formulation of inclusive agriculture 4.0 projects. Full article

This paper presents the design and implementation of an IoT-based automation system for indoor hydroponic plant cultivation using the Nutrient Film Technique. The system employs an ESP32-based controller with multiple sensors and actuators. These enable real-time monitoring and control of pH, TDS, temperature, humidity, light, tank level, and flow conditions. A modular five-layer architecture was developed. It combines the MING stack, which includes MQTT communication, InfluxDB time-series storage, Node-RED flow processing, and Grafana visualization. The system also includes a Flutter-based mobile app for remote access. Key features include temperature-compensated calibration, hysteresis-based control algorithms, dual-mode operation, TLS/ACL security, and automated alarm mechanisms. These features enhance reliability and safety. Experimental results showed stable pH/TDS regulation, dependable actuator and alarm responses, and secure long-term data logging. The proposed open-source and low-cost platform is scalable. It provides a solution for small-scale producers and urban farming, bridging the gap between academic prototypes and production-grade smart agriculture systems. In comparison to related works that mainly focus on monitoring, this study advances the state of the art. It combines continuous time-series logging, secure communication, flow verification, and integrated safety mechanisms to provide a reproducible testbed for future smart agriculture research. Full article

Early and accurate date palm disease detection is the key to successful smart farming ecosystem sustainability. In this paper, we introduce DoST-DPD, a new Dual-Stream Transformer architecture for multimodal disease diagnosis utilizing RGB, thermal and NIR imaging. In contrast with standard deep learning approaches, our model receives ontology-based semantic supervision (via per-dataset OWL ontologies), enabling knowledge injection via SPARQL-driven reasoning during training. This structured knowledge layer not only improves multimodal feature correspondence but also restricts label consistency for improving generalization performance, particularly in early disease diagnosis. We tested our proposed method on a comprehensive set of five benchmarks (PlantVillage, PlantDoc, Figshare, Mendeley, and Kaggle Date Palm) together with domain-specific ontologies. An ablation study validates the effectiveness of incorporating ontology supervision, consistently improving the performance across Accuracy, Precision, Recall, F1-Score and AUC. We achieve state-of-the-art performance over five widely recognized baselines (PlantXViT, Multi-ViT, ERCP-Net, andResNet), with our model DoST-DPD achieving the highest Accuracy of 99.3% and AUC of 98.2% on the PlantVillage dataset. In addition, ontology-driven attention maps and semantic consistency contributed to high interpretability and robustness in multiple crop and imaging modalities. Results: This work presents a scalable roadmap for ontology-integrated AI systems in agriculture and illustrates how structured semantic reasoning can directly benefit multimodal plant disease detection systems. The proposed model demonstrates competitive performance across multiple datasets and highlights the unique advantage of integrating ontology-guided supervision in multimodal crop disease detection. Full article

Future agriculture will depend on smart systems and digital technologies to improve food production and sustainability. Data-driven methods, such as artificial intelligence, will become integral to agricultural research and development, transforming how decisions are made and how sustainability goals are achieved. Reliable, high-quality data is essential to ensure that research users can trust their conclusions and decisions. To achieve this, a standard for assessing and reporting data quality is required to realise the full potential of data-driven agriculture. Two practical and empirical data quality assessment tools are proposed—a trial data quality test (primarily for data contributors) and a trial data quality statement (for data users). These tools provide information on data qualities assessed for contributors to the submitted trial data and those seeking to use the data for decision support purposes. An action case study using the Online Farm Trials platform illustrates their application. The proposed data quality framework provides a consistent approach for evaluating trial quality and determining fitness for purpose. Flexible and adaptable, the DQF and its tools can be tailored to different agricultural contexts, strengthening confidence in data-driven decision-making and advancing sustainable agriculture. Full article

LoRa-based IoT systems are increasingly used in smart farming, greenhouse monitoring, and large-scale agricultural sensing, where long-range, energy-efficient communication is essential. However, estimating link quality metrics such as PRR, RSSI, and SNR typically requires continuous packet transmission and sequence logging, an impractical approach for power-constrained field nodes. This study proposes a deep learning-driven framework for real-time prediction of link- and network-level performance in multihop LoRa networks, targeting the LoRaDisC protocol commonly deployed in agricultural environments. By integrating Bayesian surrogate modeling with Random Forest-guided hyperparameter optimization, the system accurately predicts PRR, RSSI, and SNR using multivariate time series features. Experiments on a large-scale outdoor LoRa testbed (ChirpBox) show that aggregated link layer metrics strongly correlate with PRR, with performance influenced by environmental variables such as humidity, temperature, and field topology. The optimized model achieves a mean absolute error (MAE) of 8.83 and adapts effectively to dynamic environmental conditions. This work enables energy-efficient, autonomous communication in agricultural IoT deployments, supporting reliable field sensing, crop monitoring, livestock tracking, and other smart farming applications that depend on resilient low-power wireless connectivity. Full article

The hard bottom layer in paddy fields significantly impacts the driving stability, operational quality, and efficiency of agricultural machinery. Continuously improving the precision and efficiency of unmanned, precision operations for paddy field machinery is essential for realizing unmanned smart rice farms. Addressing the unclear influence patterns of hard bottom contours on typical scenarios of agricultural machinery motion and posture changes, this paper employs a rice transplanter chassis equipped with GNSS and AHRS. It proposes methods for acquiring motion state information and hard bottom contour data during agricultural operations, establishing motion state expression models for key points on the machinery antenna, bottom of the wheel, and rear axle center. A correlation analysis method between motion state and hard bottom contour parameters was established, revealing the influence mechanisms of typical hard bottom contours on machinery trajectory deviation, attitude response, and wheel trapping. Results indicate that hard bottom contour height and local roughness exert extremely significant effects on agricultural machinery heading deviation and lateral movement. Heading variation positively correlates with ridge height and negatively with wheel diameter. The constructed mathematical model for heading variation based on hard bottom contour height difference and wheel diameter achieves a coefficient of determination R² of 0.92. The roll attitude variation in agricultural machinery is primarily influenced by the terrain characteristics encountered by rear wheels. A theoretical model was developed for the offset displacement of the antenna position relative to the horizontal plane during roll motion. The accuracy of lateral deviation detection using the posture-corrected rear axle center and bottom of the wheel center improved by 40.7% and 39.0%, respectively, compared to direct measurement using the positioning antenna. During typical vehicle-trapping events, a segmented discrimination function for trapping states is developed when the terrain profile steeply declines within 5 s and roughness increases from 0.008 to 0.012. This method for analyzing how hard bottom terrain contours affect the position and attitude changes in agricultural machinery provides theoretical foundations and technical support for designing wheeled agricultural robots, path-tracking control for unmanned precision operations, and vehicle-trapping early warning systems. It holds significant importance for enhancing the intelligence and operational efficiency of paddy field machinery. Full article

Non-destructive prediction of harvest timing is increasingly important in greenhouse melon cultivation, yet image-based methods alone often fail to reflect environmental factors affecting fruit development. Likewise, environmental or fertigation data alone cannot capture fruit-level variation. This gap calls for a multimodal approach integrating both sources of information. This study presents a fusion model combining RGB images with environmental and fertigation data to predict optimal harvest timing for melons. A YOLOv8n-based model detected fruits and estimated diameters under marker and no-marker conditions, while an LSTM processed time-series variables including temperature, humidity, CO₂, light intensity, irrigation, and electrical conductivity. The extracted features were fused through a late-fusion strategy, followed by an MLP for predicting diameter, biomass, and harvest date. The marker condition improved detection accuracy; however, the no-marker condition also achieved sufficiently high performance for field application. Diameter and weight showed a strong correlation (R² > 0.9), and the fusion model accurately predicted the actual harvest date of 28 August 2025. These results demonstrate the practicality of multimodal fusion for reliable, non-destructive harvest prediction and highlight its potential to bridge the gap between controlled experiments and real-world smart farming environments. Full article

Climate change brings considerable danger to India’s economic progress, with the agricultural sector and farmers’ livelihoods being particularly vulnerable. Himachal Pradesh is especially susceptible owing to its reliance on climate-sensitive economic activities and limited capacity to adapt to climate variability. Strengthening adaptation strategies in Himachal Pradesh is crucial for fortifying the resilience of communities reliant on environmental resources for their sustenance and economic well-being. This study examines the extent of adoption of Climate-Smart Agricultural Practices (CSAPs), identifies the factors influencing their uptake, and assesses their impact on the livelihood vulnerability of farm households in the temperate region of Himachal Pradesh. Using a multistage random sampling framework, data were collected from 432 farm households through primary surveys and secondary sources. The analysis employs descriptive statistics, a composite livelihood vulnerability index, and Ordinal Logistic and Multiple Linear Regression models. Results show higher adoption of low-cost practices such as composting, fruit-based agroforestry, crop–livestock integration, and mulching, while capital-intensive practices like micro-irrigation were limited due to financial constraints. Adoption is positively influenced by education, extension access, farming experience, financial resources, and climate information exposure. Importantly, CSAPs adoption is found to significantly reduce livelihood vulnerability, indicating enhanced resilience and reduced exposure to climate-induced risks among farm households. The findings highlight climate-smart agriculture as an effective adaptation strategy and underscore the need for policies that strengthen extension services, improve access to credit, and promote affordable climate-smart technologies to enhance resilience in vulnerable hill regions. Full article

Modern agricultural operations generate high-volume and diverse data (historical and stream) from various sources, including IoT devices, robots, and drones. This paper presents a novel smart farming architecture specifically designed to efficiently manage and process this complex data landscape.The proposed architecture comprises five distinct, interconnected layers: The Source Layer, the Ingestion Layer, the Batch Layer, the Speed Layer, and the Governance Layer. The Source Layer serves as the unified entry point, accommodating structured, spatial, and image data from sensors, Drones, and ROS-equipped robots. The Ingestion Layer uses a hybrid fog/cloud architecture with Kafka for real-time streams and for batch processing of historical data. Data is then segregated for processing: The cloud-deployed Batch Layer employs a Hadoop cluster, Spark, Hive, and Drill for large-scale historical analysis, while the Speed Layer utilizes Geoflink and PostGIS for low-latency, real-time geovisualization. Finally, the Governance Layer guarantees data quality, lineage, and organization across all components using Open Metadata. This layered, hybrid approach provides a scalable and resilient framework capable of transforming raw agricultural data into timely, actionable insights, addressing the critical need for advanced data management in smart farming. Full article

European wine production is undergoing a significant transformation, with precision viticulture (PV) emerging as a vital strategy for its long-term viability. Viticulture can benefit from the integration of digital tools and smart technologies. The VTskills project responds to this shift by promoting the adoption of environmentally, socially, and economically sustainable practices in viticulture among SME actors to achieve the objectives of the Green Deal, CAP, Farm-to-Fork, and Biodiversity strategies. One of the main goals of the VTskills project is to develop new e-learning courses for HEI and VET trainees to enhance their skills and entrepreneurial activity in an innovative environment. This paper presents the VTskills e-learning platform for sustainable precision viticulture. Full article