Search Results (592)

The agricultural industry is at a critical juncture, experiencing global pressures in the form of climate volatility, a shortage of labor, and an increase in production costs. Although artificial intelligence (AI) has the potential for revolution due to its predictive analytics and self-controlled machinery, it has not achieved widespread and even distribution for use, especially among small-to-medium-sized farms in the Midwestern United States. This study formulates and empirically examines a comprehensive socio-technical model to determine the drivers and barriers to the adoption of AI in this agricultural region. Based on a synthesized framework of the “Unified Theory of Acceptance and Use of Technology” (UTAUT) and “Task–Technology Fit” (TTF), the study incorporates agriculture-specific contextual factors such as “environmental risk, access to broadband, economic constraints, and policy support”. The analyses of the 489 farmers in the U.S. Midwest were conducted through the “partial least squares structural equation modeling” (PLS-SEM) “SmartPLS v.3.9”. The findings provide full empirical evidence of the proposed model, which supports 11 hypothesized relationships. The key results show that the strongest positive predictors of adoption intention are “performance expectancy, effort expectancy, and trust”. On the other hand, data security concerns and financial restrictions are strong deterrents. The paper also outlines the significant facilitating functions of the broadband infrastructure and policy support in building farmer perceptions of technology’s ease-of-use and facilitating conditions. These lessons can provide policymakers, ag-tech developers, and extension agencies with a roadmap on how to create more equitable and contextual interventions that overcome the rural digital divide and create resilient data-driven farming systems. Full article

This research paper examines the environmental impact of natural and synthetic fur coats, focusing exclusively on the processing and manufacturing stages. Using one coat weighing approximately 5 kg as the functional unit, a comparative Life Cycle Assessment (LCA) is conducted from raw material processing to final garment production, explicitly excluding animal farming. The analysis includes key processes such as cleaning, tanning, dyeing, and sewing for natural fur, and polymer production, fabric formation, dyeing, and finishing for synthetic fur. Data from international academic literature (Google Scholar and Scopus) are used to evaluate CO₂ emissions, energy and water consumption, chemical inputs, and waste generation. Results indicate that synthetic fur production is energy-intensive but requires relatively low water use, whereas natural fur processing involves high water consumption and chemical treatments, resulting in significantly higher emissions—often reaching hundreds to thousands of kg CO₂e per coat. The study further investigates the role of embedded IoT systems in improving efficiency within tanneries and textile manufacturing. Real-time monitoring and automated dosing systems can reduce emissions and chemical use by approximately 10–20%. Case studies of a smart tannery and an IoT-enabled synthetic fur production line illustrate potential implementation pathways. Although such optimizations can reduce environmental impacts, the findings clearly show that natural fur processing remains considerably more carbon-intensive than synthetic alternatives. This research highlights the importance of integrating digital technologies into industrial processes and suggests directions for future work based on real-world operational data. Full article

Background: Agroecology is increasingly discussed as a strategic response to the combined challenges of drought, ecological degradation, and rural vulnerability. In Morocco, this debate has become particularly urgent because agriculture now operates under persistent hydro-climatic stress, declining water availability, and strong territorial disparities between rainfed, irrigated, mountain, and oasis systems. Methods: This article is based on a structured critical review combined with an interpretive bibliometric synthesis of Moroccan and North African literature on agroecology, water stress, agricultural transition, and food-system resilience. The review was organized through conceptual framing, targeted source selection, thematic screening, and integrative synthesis. Results: Morocco is not an agroecological blank slate. Practices compatible with agroecological transition already exist across the country, including crop diversification, legume rotations, crop–livestock integration, biological regulation, organic amendments, and multifunctional production systems. However, previous reviews have mainly documented practices, projects, or sustainability initiatives without fully explaining why these remain weakly connected, poorly scaled, and insufficiently institutionalized under Moroccan conditions. This review shows that the principal barrier is not the absence of relevant practices but the absence of a coherent transition architecture capable of aligning water governance, farm economics, advisory systems, public incentives, territorial differentiation, and market valorization. The Moroccan case reveals a central paradox: agroecology is most necessary precisely where the structural conditions for its adoption are most fragile. To capture this contradiction, the paper proposes the concept of a Hydro-Agroecological Transition Trap, defined as a condition in which worsening water stress simultaneously intensifies the need for agroecological redesign and reduces the ability of farms and institutions to implement it. Conclusions: The manuscript concludes by proposing a six-pillar transition framework for Morocco based on water-smart agroecology, territorially differentiated pathways, participatory innovation, transition finance and risk-sharing, market construction, and multidimensional assessment. The originality of the study lies in shifting the analysis from a shortage of practices to a shortage of transition architecture, thereby contributing to international debates on agroecological scaling under chronic hydro-climatic stress. Full article

This study developed and evaluated a cloud-based smart irrigation platform (DSmart Farming) integrating low-cost sensors and IoT technology for automated irrigation control in community greenhouses of Puen Jai Insee, organic group in Sa Kaeo Province. The system combined soil moisture, air temperature, and relative humidity sensors, with a LoRa32-based control unit in each greenhouse and a central web-based management application linked to a MariaDB database on a cloud server. Five vegetable crops, including cherry tomato, broccoli, cabbage, Chinese kale, and kale, were grown over two distinct seasons under four irrigation strategies in a completely randomized design with three replications: three smart irrigation treatments based on soil moisture thresholds (on/off at 40/50%, 45/55%, and 50/60%) and a farmer-managed conventional irrigation control. The smart irrigation system maintained root-zone moisture within the target range (approximately 50–60%) and moderated greenhouse microclimate, preventing daytime temperatures from exceeding 40 °C, in contrast to 40–45 °C peaks in the conventional greenhouses. Across crops, smart irrigation increased yields by 20–29% while reducing water use by 41–60% compared to conventional practice, leading to income increases of 20–56%, depending on the crop. Bacterial soft rot caused by Pectobacterium carotovorum subsp. carotovorum occurred only under conventional irrigation, whereas no soft rot or other major diseases were detected in smart-irrigated greenhouses. These results demonstrate that the DSmart Farming system can enhance water use efficiency, avoid disease incidence, and improve the productivity and profitability of organic greenhouse vegetable production in water-limited smallholder systems. Full article

Agricultural soils are a major source of greenhouse gas (GHG) emissions, particularly carbon dioxide (CO₂) and methane (CH₄), highlighting the need for cost-effective and field-applicable monitoring solutions. This study developed and evaluated a low-cost real-time monitoring system for soil CO₂ and CH₄ emissions by integrating surface emission chambers, low-cost gas sensors, a solar-powered energy supply, and IoT-based wireless communication. Three acrylic chambers with different heights (40, 60, and 80 cm) were fabricated to investigate the influence of chamber geometry on measurement performance. System performance was assessed through simultaneous measurements against a Biogas 5000 analyzer under simulated conditions and during field deployment in a sugarcane cultivation area in Khon Kaen Province, Thailand. Relative agreement was used to compare the developed system with the reference instrument. The results showed that relative agreement varied with chamber height for both gases. Under simulated conditions, the 80 cm chamber achieved the highest overall relative agreement for CO₂ and CH₄, underscoring the importance of sufficient headspace volume in chamber-based measurements. Field experiments confirmed the system’s capability for continuous CO₂ monitoring in an agricultural environment. However, CH₄ emissions were not detected during the study period, likely due to drought-induced, well-aerated soil conditions. The developed system demonstrated stable autonomous operation, low energy consumption, and ease of installation, making it suitable for long-term field applications. Overall, the proposed platform provides a practical and scalable approach for real-time soil GHG monitoring and offers strong potential for integration into precision agriculture and climate-smart farming systems to support GHG mitigation strategies. Full article

Despite producing much of the world’s food, small-scale farms face severe resource shortages, climate risks, and infrastructure gaps. While digital advances ranging from IoT sensing to AI-driven analytics offer pathways to improve productivity, adoption remains uneven. This integrative review synthesizes evidence on smart-farming technologies specifically for smallholders, identifying primary barriers, enabling conditions, and design principles for successful deployment. Unlike broader smart-farming reviews, the article explicitly evaluates small-farm suitability, evidence quality, and implementation architecture rather than technological capability alone. The synthesis shows that adoption is consistently constrained by clustered barriers, notably high capital and maintenance costs, limited technical capacity, and unreliable electricity or internet access. It also finds that evidence is strongest for modular, offline-capable monitoring and alerting tools, while evidence for durable gains from highly integrated full-platform systems remains thinner and more pilot-dependent. To advance equitable innovation, the review proposes a fit-for-context deployment logic centered on co-design, local repair and advisory capacity, and financing and policy support aligned with small-farm realities. Overall, smart farming can strengthen productivity, resilience, and environmental performance on small farms, but only when technologies are embedded in inclusive service models and implementation systems. Full article

The increasing frequency of extreme climate events challenges farmland nutrient management, yet single-year fertilization assessments fail to capture system adaptability. This study quantifies interannual changes in partial factor productivity (PFP) of rice, wheat, and rapeseed under contrasting climate years (typhoon–high temperature in 2024 vs. drought in 2025) using fixed-point monitoring data from 160 farming entities in the middle and lower Yangtze River, China. Fertilization rates, yields, and PFP were analyzed with paired t-tests and Kruskal–Wallis tests. Rice PFP increased significantly from 21.48 to 23.54 kg kg⁻¹ (p < 0.001) as yields rebounded under normal climate, while wheat PFP dropped sharply from 16.50 to 12.89 kg kg⁻¹ (p < 0.001) under drought, with farmers reducing fertilizer by only 1.1% despite a 22.7% yield loss. Rapeseed PFP remained persistently low (<7 kg kg⁻¹) with no significant changes. Family farms and cooperatives achieved higher PFP than ordinary farmers (p < 0.05). These findings demonstrate that fertilizer use efficiency is highly climate-sensitive and that single-year assessments are misleading. We recommend a dynamic, climate-smart fertilization framework integrating disaster type, crop species, and site-specific thresholds (e.g., real-time weather monitoring to adjust topdressing timing). Full article

Precision monitoring of crops using drone or unmanned aerial vehicle (UAV) technology is rapidly growing as a climate-smart agriculture practice in rice farming systems in Sri Lanka and globally. In rice fields, the Leaf Color Chart (LCC) is traditionally used for manual comparison of a leaf to the standard LCC categories in the field to determine the fertilizer condition of the plant. However, this lacks autonomous monitoring, rapid monitoring of larger fields, scalability, and the digital transformation of the scores with sprayer drones for targeted fertilizer application. Drones with multispectral cameras could pose a greater rapid and digitalized solution for delineation of leaf color instead of LCC, in the field. Thus, this paper presents a novel attempt of digitization of conventional LCC levels 3 and 4, rice plant leaf greenness levels in the field, with classification and production of a spatial map using drone multispectral images and machine learning algorithms. The experimental setup consisted of ground sampling of LCC levels 3 and 4 from farmer fields and acquisition of drone imagery data above the field with a DJI Phantom 4 Multispectral UAV, from which fifteen vegetation indices related to crop spectra were extracted. The vegetation indices were then employed for training (70%) and testing (30%) with machine learning algorithms: Random Forest (RF), as well as SVM-linear and SVM-RBF, focusing on LCC 3–4 class classification. The results showed good classification performance, with the RF algorithm reporting a test accuracy of 98.2%, outperforming SVM-linear (82.5%) and SVM-RBF (87.5%). The RF model outputs SR, EVI, MSR, NDVI, and TCARI as feature importance indices for the classification of LCC levels 3 and 4 in the rice field. The findings of this proposed method greatly encourage the adaptation of drone technology for real-time monitoring of rice leaf fertilizer levels linked to LCC levels three and four, and spatial identification of the zones across the field. This imposes greater advancement towards climate-smart rice cultivation, targeted fertilizer application and rice field landscape pattern change analysis, underpinning the importance of field digitization. Full article

Globally, greenhouse gas (GHG) emissions have risen dramatically due to accelerated industrialization, excessive fossil fuel extraction, and agricultural activities, leading to global warming and ecosystem collapse. Achieving net-zero carbon emissions has therefore become a crucial global priority. Despite substantial international efforts, only a small number of countries have achieved carbon neutrality so far, with the majority aiming to do so by 2050 or 2060. Progress remains hindered by fragmented international coordination and inadequate integration of mitigation and adaptation co-benefits. However, agriculture is a major carbon emitter with significant mitigation potential. Attaining local carbon neutrality in agricultural landscapes is highly costly and strongly impacted by the spatial heterogeneity of GHG emissions and the diversity of available mitigation possibilities. This sector remains a major contributor to methane (CH₄) and nitrous oxide (N₂O) emissions, mainly through enteric fermentation and fertilizer use, and thus must be prioritized in global carbon neutrality strategies. Tactics such as improved livestock management, reduced use of synthetic fertilizers, conservation agriculture, afforestation, and renewable energy adoption can reduce emissions. These technical approaches should be supported by effective policy instruments, like carbon taxes, cap-and-trade schemes, low-carbon practice subsidies, and regulatory frameworks. Together, these measures can enable a transition toward long-term sustainability in agriculture by balancing emissions with removals through enhanced carbon sinks and credible offset mechanisms. Full article

Conservation agriculture (CA) is widely promoted as a climate-smart approach to improve productivity and resilience, especially among small-scale farmers who face socioeconomic and climate-related risks that threaten their livelihoods. However, evidence linking CA adoption to household food-security outcomes in South Africa remains limited. This study examines patterns and determinants of CA adoption and assesses its implications for household food security among small-scale farmers in three municipalities of Mpumalanga Province. A quantitative cross-sectional survey was conducted among 391 farmers selected through stratified random sampling. Data were collected using a structured questionnaire and analyzed using descriptive statistics, chi-square tests, Kruskal–Wallis tests, and binary logistic regression. Results show that CA adoption was widespread but largely partial, with most farmers adopting one or two principles rather than the full CA package. Access to CA-related resources and information, household size, livelihood strategy, farm income, and farm size significantly influenced adoption. Higher adoption intensity was consistently associated with improved food-security outcomes, including increased production, lower food-insecurity severity, greater crop diversification, higher likelihood of year-round production, and increased market participation. The study concludes that conservation agriculture can contribute positively to multiple dimensions of household food security when adopted as an integrated system, but partial adoption yields limited benefits. Targeted extension support, improved access to resources, and context-specific interventions are required to enhance sustained and holistic CA adoption among small-scale farmers. Full article

With the rapid development of precision agriculture and smart farming management, accurate crop disease detection has become a critical tool for optimizing agricultural resource allocation, controlling operational costs, and supporting scientific plant protection strategies. However, real-world field environments are often characterized by strong background interference, multiple concurrent diseases, and fine-grained lesion differences, posing significant challenges to existing detection methods in practical agricultural Internet of Things (IoT) applications. In this paper, we propose Freq-spatial Context Dynamic Network(FCDNet), an efficient and cost-effective detection model tailored for multi-category strawberry disease recognition in complex field management scenarios. The proposed model integrates a Freq-Spatial Feature Module (FSFM), a Context Guide Fusion Module (CGFM), and a Task Align Dynamic Detection Head (TADDH), enabling enhanced expression of high-frequency micro-lesions, adaptive filtering of field background noise, and spatial alignment of classification and regression tasks, while maintaining a lightweight architecture suitable for low-cost agricultural edge devices. Extensive experiments conducted on the newly constructed Strawberry Disease Dataset-7(S7DD) demonstrate that FCDNet consistently outperforms existing mainstream methods, achieving an F1-score of 91.0% and an mAP@0.5 of 94.6%. The model’s architectural robustness and capacity for generalization are further substantiated by evaluations across diverse agricultural datasets using PlantDoc and ALDOD. Ultimately, FCDNet became a practical and cost-effective tool for real-time detection of strawberry diseases, directly supporting more accurate yield forecasting and risk management in smart agriculture systems. Full article

Lolium rigidum is among the most prevalent and noxious weeds in cereal and perennial cropping systems worldwide and has developed resistance to several herbicide modes of action. This study employed a sensor-based smart farming method for the early screening of herbicide resistance across three L. rigidum accessions in Greece, followed by dose–response experiments with clodinafop-propargyl, glyphosate, and mesosulfuron-methyl + iodosulfuron-methyl. In the preliminary screening, herbicides were applied at their highest recommended rates, whereas the dose–response experiments included five application rates (0, 1/4X, X, 2X, and 4X). The EM2 accession exhibited confirmed resistance to mesosulfuron-methyl + iodosulfuron-methyl, with a resistance index of 5.31 and a five-fold increase in the herbicide rate required compared to the susceptible EM1 accession. For clodinafop-propargyl, the GR50 value of the resistant EM3 accession (147.97 g a.i. ha⁻¹) was approximately 2.5-fold higher than that of the susceptible EM2 accession (60.28 g a.i. ha⁻¹). Glyphosate application provided only partial biomass reduction in resistant accessions, indicating reduced susceptibility. In parallel, TaqMan assays were developed and validated to detect target-site mutations linked to resistance against EPSPS-, ACCase-, and ALS-inhibiting herbicides, supporting the molecular interpretation of the observed resistance patterns. Overall, the results demonstrate that sensor-based smart farming approaches can provide a rapid and reliable tool for the early screening of herbicide resistance, enabling more informed crop protection strategies and supporting sustainable weed management. Further research across diverse soil types and climatic conditions is warranted to validate and extend the applicability of these approaches. Full article

The main scope of the present study is to assess the environmental and economic outcomes of applying distinct Climate-Smart Agricultural (CSA) practices in apple cultivation. Thus, four different CSA practices, including organic farming, cover crops, floral bands, and grazing, were selected, and their environmental and economic performance was evaluated and compared to that of a conventional apple orchard system (baseline). Specifically, Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) methodologies were applied to assess the environmental and economic sustainability of the studied systems, respectively. Among the studied practices, grazing exhibited the best environmental performance among the modeled scenarios (approximately 25% decrease in greenhouse gas emissions compared to the baseline under the assumed conditions), followed by organic farming that significantly decreased eutrophication- and ecotoxicity-related impacts. Similarly, organic farming and grazing exhibited the best economic performance in the concept of the present study, with the total profit per hectare rising to approximately 5300 € and 4300 €, respectively, compared to the value of 3700 € of the conventional apple orchard. The results suggest that the implementation of CSA practices has the potential to improve the environmental and economic performance of apple orchards under the modeled conditions. Full article

Dendrobium Sonia orchid cultivation constitutes a vital commercial industry in Thailand; however, production remains persistently threatened by fungal and bacterial diseases. This study proposes a robust automated framework for orchid disease classification under conditions characterized by high visual uncertainty. A comparative analysis was conducted across four Convolutional Neural Network (CNN) architectures: ResNet-50 and three lightweight counterparts—MobileNetV3-Large, EfficientNetV2-B0, and NASNet-Mobile. All models were optimized using transfer learning, Cosine Decay scheduling, and EarlyStopping on a real-world dataset acquired from commercial orchid farms in Thailand. Experimental results indicate that ResNet-50 attained the highest overall performance (Accuracy: 98.96%, Macro F1: 0.9894, AUC-ROC: 0.9996), while EfficientNetV2-B0 achieved comparable results among the lightweight architectures (Accuracy: 98.47%, Macro F1: 0.9846, AUC-ROC: 0.9985). Importantly, statistical evaluation using the Wilcoxon Signed-Rank Test across five independent trials revealed no statistically significant difference between ResNet-50 and all three lightweight models (p > 0.05). This confirms the practical viability of deploying compact architectures on mobile platforms within smart farming systems without sacrificing diagnostic accuracy. Moreover, integrating Grad-CAM++ enhances interpretability by producing visual explanations that align with expert pathological assessments. This transparency effectively mitigates decision-making ambiguity and strengthens farmer confidence in adopting AI-driven precision agriculture. Full article

Smart feedlots are increasingly adopting Precision Livestock Farming technologies to enable continuous, individual-animal monitoring and more proactive management in intensive beef production systems. This narrative review synthesises evidence from approximately 350 academic publications, of which 117 are formally cited, complemented by industry deployments and the authors’ experience in smart feedlot system development. We cover enabling digital infrastructure (power, sensing networks, wireless connectivity, and gateways), animal identification and sensing (RFID, automated weighing, wearables, and pen-side sensors), machine vision (RGB, thermal, and multispectral imaging from fixed and mobile platforms), and AI-based analytics and decision support for health, welfare, performance, and environmental management. Across the literature, key components have progressed beyond proof-of-concept toward operation under commercial constraints. Reported outcomes include reduced reliance on routine pen-rider observation and yard handling, earlier triage of emerging morbidity risk and behavioural change, and more standardised welfare auditing. Vision-based methods are repeatedly validated against trained human scorers in both on-farm and abattoir contexts, while automated weighing and image-based liveweight estimation support higher-frequency growth monitoring with low single-digit percentage error in representative studies. Precision feeding and targeted supplementation are associated with improved feed utilisation and reduced resource wastage, although effectiveness and adoption vary across animal classes and production stages. We identify priorities for robust, scalable deployment: resilient communications in harsh environments, appropriate edge–cloud partitioning under intermittent connectivity, and interoperable multi-sensor data fusion to deliver trustworthy alerts and actionable insights. Persistent barriers remain cost, durability, maintenance burden, integration and interoperability, data governance, and workforce capability. Full article