Search Results (493)

The key strategic point for facilitating domestic circulation is to enhance and expand household consumption. Based on a survey of 1080 farming households in Hunan, Hubei, and Jilin Provinces, this study examines the impact of digital finance use on the scale and structural upgrading of household consumption among farmers. The findings indicate that digital finance use effectively expands the scale of household consumption and promotes structural upgrades. The results remain robust through various endogenous and robust methods. Heterogeneity analysis reveals that the benefits of digital finance use are greater for middle- to high-income groups and those with lower education levels, indicating the presence of a digital divide effect. Furthermore, the construction of village communities, skill training, improvements in village logistics services, and the availability of medical clinic facilities can enhance the consumption-promoting effects of digital finance use. Mechanism analysis shows that digital finance primarily operates through alleviating credit constraints, enhancing risk prevention, and improving financial returns to influence the scale and structural upgrading of household consumption. This study provides policy insights for rural revitalization and unlocking the consumption potential of rural residents. Full article

Evidence of the effects of postnatal exposure to organophosphates (OPs) on children’s neurodevelopment remains limited but crucial. This cross-sectional study evaluated exposure to OPs and neurobehavioral performance in 172 preschool children. Urinary dialkyl phosphate (DAP) metabolites, biomarkers for exposure to OPs, were measured. The neurobehavioral assessments included motor skills, memory, and cognitive function, measured using the Purdue pegboard test, digit span test, object memory test, and visual-motor integration. Multiple linear regression models were employed to explore the associations between urinary DAP metabolite levels and neurobehavioral performance, adjusting for potential confounders. Findings revealed that children of farming parents had higher urinary levels of dimethylphosphate (DMP) (Beta = 0.730, 95% CI = 0.138, 1.322, p value = 0.016) and diethylphosphate (DEP) (Beta = 0.668, 95% CI = 0.044, 1.291, p value = 0.036). Additionally, high fruit consumption correlated with increased urinary DEP levels (Beta = 0.398, 95% CI = 0.063, 0.733, p value = 0.020). Critically, elevated urinary DEP was associated with poorer fine motor coordination, affecting performance in the Purdue pegboard test for the dominant hand (Beta = −0.428, 95% CI = −0.661, −0.194, p value < 0.001), the preferred hand (Beta = −0.376, 95% CI = −0.603, −0.149, p value = 0.001), and both hands (Beta = −0.524, 95% CI = −0.773, −0.276, p value < 0.001). These findings highlight the role of parental occupation and diet in children’s OP exposure and suggest that OP exposure negatively impacts fine motor coordination. Targeted interventions, such as promoting organic diets, enhancing workplace safety, and ongoing biomonitoring, are vital to reduce neurodevelopmental risks for vulnerable populations. Full article

The article examines modern approaches to energy management in the context of the development of Industry 5.0 with a particular focus on cybersecurity. Key tenets of Industry 5.0 are discussed, including the integration of advanced technologies with intelligent energy management systems (IEMSs) and the growing need to protect data in the face of increasing cyber threats. The challenges faced by small and medium-sized enterprises (SMEs) using solutions based on renewable energy sources, such as photovoltaic farms, are also analyzed. The article presents examples of IEMS applications and discusses methods for securing these systems, offering an overview of cyber threat protection tools in the context of modern energy management. The analysis carried out provided information that will help businesses make rational decisions and contribute to shaping the state’s macroeconomic policy on cybersecurity and energy savings. The results of this research can also help develop more effective strategies for managing technology and IT infrastructure, which is crucial in the digital age of Industry 5.0. Full article

We utilized remote sensing and ground cover data to predict soil organic carbon (SOC) content across a vast geographic region. Employing a combination of machine learning and deep learning techniques, we developed a novel data fusion approach that integrated Digital Elevation Model (DEM) data, MODIS satellite imagery, WOSIS soil profile data, and CHELSA environmental data. This combined dataset, named GeoBlendMDWC, was specifically designed for SOC prediction. The primary aim of this research is to develop and evaluate a novel optimization algorithm for accurate SOC prediction by leveraging multi-source environmental data. Specifically, this study aims to (1) create an integrated dataset combining remote sensing and ground data for comprehensive SOC analysis, (2) develop a new optimization technique that enhances both machine learning and deep learning model performance, and (3) evaluate the algorithm’s efficiency and accuracy against established optimization methods like Jaya and GridSearchCV. This study focused on India, Australia, and South Africa, countries known for their significant agricultural activities. We introduced a novel optimization technique for both machine learning and deep neural networks, comparing its performance to established methods like the Jaya optimization technique and GridSearchCV. The models evaluated included XGBoost Regression, LightGBM, Gradient Boosting Regression (GBR), Random Forest Regression, Decision Tree Regression, and a Multilayer Perceptron (MLP) model. Our research demonstrated that the proposed optimization algorithm consistently outperformed existing methods in terms of execution time and performance. It achieved results comparable to GridSearchCV, reaching an R² of 90.16, which was a significant improvement over the base XGBoost model’s R² of 79.08. In deep learning optimization, it significantly outperformed the Jaya algorithm, achieving an R² of 61.34 compared to Jaya’s 30.04. Moreover, it was 20–30 times faster than GridSearchCV. Given its speed and accuracy, this algorithm can be applied to real-time data processing in remote sensing satellites. This advanced methodology will greatly benefit the agriculture and farming sectors by providing precise SOC predictions. Full article

Efficient agricultural input is crucial for agricultural green production and sustainable development. The swift evolution of information and communication technologies has diversified the avenues through which farmers access information. However, how different information channels affect farmers’ production input remain poorly understood. Leveraging a two-way fixed-effects model and the Karlson-–Holm–Breen (KHB) method, this study delves into the mechanisms underlying the influence of both traditional and modern information channels on farmers’ inputs of seeds, chemical fertilizers, and pesticides (SCFP) based on over 15,000 sample of Chinese farmers. The findings reveal the following: (1) modern information channels significantly decrease farmers’ SCFP input, whereas traditional channels exhibit the opposite effect; (2) environmental pollution perception acts as a mediator in the influence of both traditional and modern information channels on farmers’ SCFP input; (3) traditional information channels significantly promote farmers’ SCFP input in the grain production and marketing balance areas, and modern information channels inhibit farmers’ SCFP input in major grain-producing areas; and (4) traditional and modern information channels have an impact on farmers’ SCFP input in the western region, but not in the central region. To promote sustainable agricultural development, government departments should enhance rural Internet access, diversify information sources, advocate for eco-farming, ensure regional digital equity, and enhance green agri-tech promotion. Full article

Facilitating the sustained and stable growth of farmers’ income is crucial for achieving sustainable development in forest regions. As an emerging driving force, the digital economy has demonstrated substantial potential in enhancing farmers’ income and promoting regional economic prosperity in forest areas. Based on survey data from 1043 households across 10 counties in Guizhou Province, China, this study empirically examined the direct and indirect effects of digital economy participation on income growth among farmers in forest regions. The findings revealed that, first, participation in the digital economy significantly contributed to income growth for these households. This effect remained robust across various estimation methods, restricted sample tests, and when replacing dependent variables. Second, forestry management and its diversification played a mediating role in the relationship between digital economy participation and farmers’ income. Participation in the digital economy indirectly influenced income growth by fostering forestry management activities and their diversification. Third, the heterogeneity analysis indicated that digital economy participation had a significant positive impact on the income growth of pure farming households, part-time farming households, and households that had previously escaped poverty. This discovery underscored the unique role of the digital economy in alleviating poverty and preventing its recurrence. The conclusions of this study provide essential theoretical and practical guidance for empowering forestry development through the digital economy and advancing the digital transformation of the forestry industry. More critically, this research presents a novel pathway for the deep integration of the digital economy with forestry, jointly fostering income growth for farmers in forest regions, which holds significant implications for achieving rural sustainable development. Full article

The purpose of this study was to investigate the impact of climate change on smallholder farmers in South Africa, particularly focusing on the relationship between agriculture and weather patterns. Understanding this connection is crucial for helping farmers adapt to changing climate conditions and improve their resilience and sustainability. This research analyses 33 years of climate data (1990–2023) from the Belfast weather station to identify long-term climate trends, seasonal shifts, and the frequency of extreme weather events. Statistical analysis, including the Mann–Kendall test, revealed significant changes in temperature, rainfall, and the intensity of extreme weather events, indicating that climate change is already affecting the region. Specifically, the research highlighted significant damage to agricultural infrastructure, such as greenhouses, due to climate-related wind events. This study emphasises the importance of using digital technologies to monitor weather patterns in real-time, aiding in decision-making, and enhancing agricultural efficiency. Additionally, it calls for further research into the social impacts of climate variability, including its effects on community cohesion, migration, and access to social services among smallholder farmers. These findings provide a foundation for developing effective interventions to support the resilience of smallholder farming communities in the face of climate change. Future studies need to consider how climate variability affects farmers’ abilities to access markets, both in terms of transport and product quality. Full article

Many of the world’s less developed regions may not be able to improve the well-being of rural residents through agricultural revitalization because of their remoteness from agricultural markets. Using the county-level data set of China’s underdeveloped old revolutionary base areas from 2010 to 2021, this paper takes the policy planning of rural e-commerce as event intervention to investigate the driving role of the digital product market on agricultural economic development. Empirical results show that rural e-commerce planning policy has significantly promoted the agricultural added value of the pilot counties, and the digital market is the key driving factor of the agricultural economic growth in these underdeveloped areas. Both food production and livestock output have increased significantly as a result of e-commerce policies. Considering the potential bias of the bidirectional fixed effect estimators of staggered differences-in-differences (DID), this study uses heterogeneous robust estimators to verify the growth effect of the agricultural economy. Specifically, digital agricultural markets have significantly promoted agricultural mechanization and significantly improved agricultural total factor productivity. Moreover, empirical evidence does not support transmission mechanisms for off-farm employment and agricultural entrepreneurship. The findings can help less developed countries and regions develop policies to expand the agricultural markets with digital dividends, thereby promoting the development of the agricultural economy. Full article

This paper introduce advancements in agricultural robotics in response to the increasing demand for automation in agriculture. Our research aims to develop humancentered agricultural robotic systems designed to enhance efficiency, sustainability, and user experience across diverse farming environments. We focus on essential applications where human labor and experience significantly impact performance, addressing four primary robotic systems, i.e., harvesting robots, intelligent spraying robots, autonomous driving robots for greenhouse operations, and multirobot systems, as a method to expand functionality and improve performance. Each system is designed to operate in unstructured agricultural environments, adapting to specific needs. The harvesting robots address the laborintensive demands of crop collection, while intelligent spraying robots improve precision in pesticide application. Autonomous driving robots ensure reliable navigation within controlled environments, and multirobot systems enhance operational efficiency through optimized collaboration. Through these contributions, this study offers insights into the future of agricultural robotics, emphasizing the transformative potential of integrated, experience-driven intelligent solutions that complement and support human labor in digital agriculture. Full article

Accurate identification of individual cow identity is a prerequisite for the construction of digital farms and serves as the basis for optimized feeding, disease prevention and control, breed improvement, and product quality traceability. Currently, cow identification faces challenges such as poor recognition accuracy, large data volumes, weak model generalization ability, and low recognition speed. Therefore, this paper proposes a cow identification method based on YOLOX-S-TKECB. (1) Based on the characteristics of Holstein cows and their breeding practices, we constructed a real-time acquisition and preprocessing platform for two-dimensional Holstein cow images and built a cow identification model based on YOLOX-S-TKECB. (2) Transfer learning was introduced to improve the convergence speed and generalization ability of the cow identification model. (3) The CBAM attention mechanism module was added to enhance the model’s ability to extract features from cow torso patterns. (4) The alignment between the apriori frame and the target size was improved by optimizing the clustering algorithm and the multi-scale feature fusion method, thereby enhancing the performance of object detection at different scales. The experimental results demonstrate that, compared to the traditional YOLOX-S model, the improved model exhibits a 15.31% increase in mean average precision (mAP) and a 32-frame boost in frames per second (FPS). This validates the feasibility and effectiveness of the proposed YOLOX-S-TKECB-based cow identification algorithm, providing valuable technical support for the application of dairy cow identification in farms. Full article

Precision agriculture and the increasing automation efforts in animal husbandry requires continuous and complex monitoring of the animals. Rumen bolus sensors, which are cutting-edge pieces of technology and a rapidly developing research field, present an exceptional opportunity for monitoring the health status, physiological parameters, and estrus of the animals. The objective of this paper is to provide a comprehensive overview of the development process of a new sensor development. We address the issues of conceptual design, an overview of applicable sensor modalities, mechanical design, power supply design, applicable hardware solutions, applicable communication solutions and finally the sensor detection algorithms proved in field tests. In conclusion, we present a summary of the current opportunities in the field and provide an analysis of the foreseeable trends. Full article

The Greater Amanzule Peatlands (GAP) in Ghana is an important biodiversity hotspot facing increasing pressure from anthropogenic land-use activities driven by rapid agricultural plantation expansion, urbanisation, and the burgeoning oil and gas industry. Accurate measurement of how these pressures alter land cover over time, along with the projection of future changes, is crucial for sustainable management. This study aims to analyse these changes from 2010 to 2020 and predict future scenarios up to 2040 using multi-source remote sensing and machine learning techniques. Optical, radar, and topographical remote sensing data from Landsat-7, Landsat-8, ALOS/PALSAR, and Shuttle Radar Topography Mission derived digital elevation models (DEMs) were integrated to perform land cover change analysis using Random Forest (RF), while Cellular Automata Artificial Neural Networks (CA-ANNs) were employed for predictive modelling. The classification model achieved overall accuracies of 93% in 2010 and 94% in both 2015 and 2020, with weighted F1 scores of 80.0%, 75.8%, and 75.7%, respectively. Validation of the predictive model yielded a Kappa value of 0.70, with an overall accuracy rate of 80%, ensuring reliable spatial predictions of future land cover dynamics. Findings reveal a 12% expansion in peatland cover, equivalent to approximately 6570 ± 308.59 hectares, despite declines in specific peatland types. Concurrently, anthropogenic land uses have increased, evidenced by an 85% rise in rubber plantations (from 30,530 ± 110.96 hectares to 56,617 ± 220.90 hectares) and a 6% reduction in natural forest cover (5965 ± 353.72 hectares). Sparse vegetation, including smallholder farms, decreased by 35% from 45,064 ± 163.79 hectares to 29,424 ± 114.81 hectares. Projections for 2030 and 2040 indicate minimal changes based on current trends; however, they do not consider potential impacts from climate change, large-scale development projects, and demographic shifts, necessitating cautious interpretation. The results highlight areas of stability and vulnerability within the understudied GAP region, offering critical insights for developing targeted conservation strategies. Additionally, the methodological framework, which combines optical, radar, and topographical data with machine learning, provides a robust approach for accurate and detailed landscape-scale monitoring of tropical peatlands that is applicable to other regions facing similar environmental challenges. Full article

Continuous crop monitoring enables the early detection of field emergencies such as pests, diseases, and nutritional deficits, allowing for less invasive interventions and yielding economic, environmental, and health benefits. The work organization of modern agriculture, however, is not compatible with continuous human monitoring. ICT can facilitate this process using autonomous Unmanned Ground Vehicles (UGVs) to navigate crops, detect issues, georeference them, and report to human experts in real time. This review evaluates the current state of ICT technology to determine if it supports autonomous, continuous crop monitoring. The focus is on shifting from traditional cloud-based approaches, where data are sent to remote computers for deferred processing, to a hybrid design emphasizing edge computing for real-time analysis in the field. Key aspects considered include algorithms for in-field navigation, AIoT models for detecting agricultural emergencies, and advanced edge devices that are capable of managing sensors, collecting data, performing real-time deep learning inference, ensuring precise mapping and navigation, and sending alert reports with minimal human intervention. State-of-the-art research and development in this field suggest that general, not necessarily crop-specific, prototypes of fully autonomous UGVs for continuous monitoring are now at hand. Additionally, the demand for low-power consumption and affordable solutions can be practically addressed. Full article

Cow behavior carries important health information. The timely and accurate detection of standing, grazing, lying, estrus, licking, fighting, and other behaviors is crucial for individual cow monitoring and understanding of their health status. In this study, a model called CAMLLA-YOLOv8n is proposed for Holstein cow behavior recognition. We use a hybrid data augmentation method to provide the model with rich Holstein cow behavior features and improve the YOLOV8n model to optimize the Holstein cow behavior detection results under challenging conditions. Specifically, we integrate the Coordinate Attention mechanism into the C2f module to form the C2f-CA module, which strengthens the expression of inter-channel feature information, enabling the model to more accurately identify and understand the spatial relationship between different Holstein cows’ positions, thereby improving the sensitivity to key areas and the ability to filter background interference. Secondly, the MLLAttention mechanism is introduced in the P3, P4, and P5 layers of the Neck part of the model to better cope with the challenges of Holstein cow behavior recognition caused by large-scale changes. In addition, we also innovatively improve the SPPF module to form the SPPF-GPE module, which optimizes small target recognition by combining global average pooling and global maximum pooling processing and enhances the model’s ability to capture the key parts of Holstein cow behavior in the environment. Given the limitations of traditional IoU loss in cow behavior detection, we replace CIoU loss with Shape–IoU loss, focusing on the shape and scale features of the Bounding Box, thereby improving the matching degree between the Prediction Box and the Ground Truth Box. In order to verify the effectiveness of the proposed CAMLLA-YOLOv8n algorithm, we conducted experiments on a self-constructed dataset containing 23,073 Holstein cow behavior instances. The experimental results show that, compared with models such as YOLOv3-tiny, YOLOv5n, YOLOv5s, YOLOv7-tiny, YOLOv8n, and YOLOv8s, the improved CAMLLA-YOLOv8n model achieved increases in Precision of 8.79%, 7.16%, 6.06%, 2.86%, 2.18%, and 2.69%, respectively, when detecting the states of Holstein cows grazing, standing, lying, licking, estrus, fighting, and empty bedding. Finally, although the Params and FLOPs of the CAMLLA-YOLOv8n model increased slightly compared with the YOLOv8n model, it achieved significant improvements of 2.18%, 1.62%, 1.84%, and 1.77% in the four key performance indicators of Precision, Recall, [email protected], and [email protected]:0.95, respectively. This model, named CAMLLA-YOLOv8n, effectively meets the need for the accurate and rapid identification of Holstein cow behavior in actual agricultural environments. This research is significant for improving the economic benefits of farms and promoting the transformation of animal husbandry towards digitalization and intelligence. Full article

Africa still struggles to end hunger, partly because not all Africans have access to nutritious food. Although studies have established the connection between digital technologies and food security, the reality in Africa is that, despite the laudable feat in the use of digital technologies, the accessibility and utilization of food still face challenges. Digital agriculture, or technology, is a data-driven innovation that predicts agricultural outcomes and guides food producers throughout the different phases of operations on the farm. The literature documents the efficacy of digital agriculture in food production and availability well, but it has hardly examined how it enhances food accessibility and utilization. And even though studies that have examined food accessibility and utilization have merely assessed income as a tool that guarantees food accessibility and utilization, not much attention has been paid to how digital resources can aid in the access to and utilization of food. Drawing on information asymmetry theory and the systematic qualitative method, this article investigates how digital agriculture, through the internet and mobile phones, enhances efforts towards the accessibility and utilization of food as prerequisites for the attainment of SDG 2 in Africa. The findings provide an understanding of the potential of digital technologies in promoting the accessibility and utilization of food. It advocates strategies through which stakeholders in the agricultural sector can utilize technology in ways that aid Africa’s strategic efforts to attain food security and zero hunger. Full article