Development Of Digital Agriculture Research Articles

Effects and mechanisms of digitalization to enhance the resilience of the hog breeding industry: evidence from China

IntroductionWith the rapid development of digital agriculture, digitalization has gradually become a key factor affecting the resilience of China’s pig farming industry.MethodsFrom both the test results and the theoretical point of view, the individual fixed-effect model is more suitable for this paper’s study. Therefore, based on the panel data of 31 provinces in China from 2011 to 2022, this study constructs an individual fixed-effect model to examine how digitization affects the resilience of China’s pig farming industry.Results and discussionThe findings indicated that digitization significantly enhanced the resilience of China’s hog breeding industry, especially in potential growth areas. Improving breeding technology and scale is a crucial avenue for digitalization to bolster the industry’s resilience. Digitalization exerts a double threshold effect on the industry’s resilience, with its promotional impact only realized within a specific range. The article proposes policy suggestions, such as strengthening infrastructure construction and giving play to regional advantages.

Winter Wheat Yield Prediction Based on the ASTGNN Model Coupled with Multi-Source Data

Timely and accurate prediction of winter wheat yields, which is crucial for optimizing production management, maintaining supply–demand balance, and ensuring food security, depends on interactions among numerous factors, such as climate, surface characteristics, and soil quality. Despite the extensive application of deep learning models in this field, few studies have analyzed the effect of the large-scale geospatial characteristics of neighboring regions on crop yields. Therefore, we present an attention-based spatio-temporal Graph Neural Network (ASTGNN) model coupled with geospatial characteristics and multi-source data for improved accuracy of winter wheat yield estimation. The datasets used in this study included multiple types of remote sensing, meteorological, soil, crop yield, and planting area data for Anhui, China, from 2005 to 2020. The results showed that multi-source data led to higher prediction performance than single-source data, and enabled accurate prediction of winter wheat yields three months prior to harvest. Furthermore, the ASTGNN model provided better prediction performance than two traditional crop yield prediction models (R2 = 0.70, RMSE = 0.21 t/ha, MAE = 0.17 t/ha). Therefore, ASTGNN enhances the accuracy of crop yield prediction by incorporating geospatial characteristics. This research has implications for improving agricultural production management, promoting the development of digital agriculture, and addressing climate change in agriculture.

Mechanisms and Impact Effects of Digital Agriculture Development on Agricultural Eco-Efficiency in China

The green development of agriculture is an essential way to achieve high-quality agricultural development, and the development of digitalization has given new momentum to the green development of agriculture. In this study, based on the panel data of 30 provinces in China from 2011 to 2022, we measure the agricultural eco-efficiency and the level of digital agriculture development in China using the Super-SBM model with global reference and the entropy value method, respectively. The impact of the level of digital agriculture development on agricultural eco-efficiency is explored with the help of a regression model, and the mediating role of pesticide and fertilizer inputs in this impact pathway is explored using a mediating effects model. The study found that: (1) the level of digital agriculture development positively and significantly affects agricultural eco-efficiency to a relatively large extent; (2) the effect of digital agriculture development on the improvement of agriculture eco-efficiency is significantly heterogeneous in different regions; (3) pesticide and fertilizer inputs have a mediating role in this impact pathway. Therefore, the application and promotion of digital agriculture technology should be strengthened to build a green agricultural production and management system, so as to promote high-quality and sustainable development of Chinese agriculture.

Using spectral vegetation indices and machine learning models for predicting the yield of sugar beet (Beta vulgaris L.) under different irrigation treatments

Yield prediction is essential for production planning, resource management, and competitive advantages. The use of vegetation indices and machine learning for yield prediction is rapid, practical, and accurate and contributes to the development of digital agriculture. Although many studies have been carried out on yield estimation, the number of studies on sugar beet and irrigation applications is limited. To solve this problem, in this study yield prediction of sugar beet under different irrigation treatments was carried out using spectral reflection-based vegetation indices obtained by proximal measurements and developed machine learning models. The multilayer perceptron (MLP), random forest (RF), k-nearest (kNN), bagging (BAG), support vector regression (SVR), gaussian processes (GP), and CustomNet (CN) predictors were used in yield prediction. The irrigation treatments included I100 (application of irrigation to increase the available moisture to field capacity by around 45–50% of the water holding capacity at the root depth), I75 (75% of the water applied in the control group), I50 (50% of the water applied in the control group), and I125 (25% of the water applied in the control group). The combinations of vegetation indices were determined using Pearson correlation and principal component analysis. OSAVI, SAVI, and NDVI had the most significant influence on the yield prediction of sugar beet. Among the combinations used, the kNN1 model (including all attributes as input) showed high R2 values of 0.99 for training and 0.64 for testing. In addition, the kNN3 model (including Treatment, NDVI, NWI, and OSAVI as inputs) had R2 values of 0.97 for training and 0.65 for testing, indicating successful outcomes. However, the MLP1 and SVR1 models had lower results with test R2 values of 0.53 and 0.56, respectively. The results demonstrate the successful application of machine learning and vegetation indices for yield prediction. The proposed approaches could encourage agricultural experts and researchers in future work for yield mapping and identification.

The Implementation of the Digital Economy in Promoting Rural Revitalization

The government in recent years has adopted the e-commerce poverty alleviation policy and used e-commerce platforms to promote the sales of rural products, increase farmers’ incomes, and help rural revitalization. This policy has played a positive role in improving the livelihood of farmers and promoting the construction of rural infrastructure. At the same time, the integration of agriculture with the Internet has brought new vitality to the rural economy, promoted agricultural modernization and knowledge, and improved productivity and farmers’ income. The government has introduced the Internet of Things and cloud computing technology to help agricultural production become large-scale, intensive, and intelligent, which improves labor efficiency and provides support for rural revitalization. In addition, the digital economy has given rise to new agricultural operators, such as e-commerce and agricultural cooperatives, providing new models and sales channels for the development of rural industries. Driven by the rural revitalization strategy, the rapid development of digital agriculture, rural e-commerce, and smart tourism has brought huge growth to the rural economy

The impact of digital economy development on agricultural carbon emissions——empirical research based on Chinese data

ABSTRACT This study examines the impact of digital economy development on carbon emissions in Chinese agriculture and its underlying mechanisms. Empirical analysis is conducted using panel data from 31 provinces in China for the years 2013 to 2021, employing a fixed-effects model.The study reveals that the development of the digital economy significantly reduces agricultural carbon emissions, with this effect showing a non-linear increasing trend. The impact of digital economy development on agricultural carbon emissions varies with climate types, and the reduction effect is more pronounced in regions with lower rainfall. Furthermore, the development of the digital economy lowers the intensity of agricultural carbon emissions by improving logistics efficiency and promoting industrial upgrading and transformation.This paper provides a theoretical basis and policy recommendations for the nation to reduce agricultural carbon emissions and promote green transformation. It suggests that the government should strengthen support and guidance for the development of the digital economy, actively promote the digitalization of agriculture, including the formulation of differentiated strategies for the development of digital agriculture, improvement of regulations and standards for agricultural product logistics, and enhancement of the application and innovation in smart agriculture.

Research on the Spatial Dynamic Evolution of Digital Agriculture—Evidence from China

Digital agriculture serves as a pivotal means of ushering in innovative agricultural practices and achieving sustainable agricultural development. Although agricultural digitalization has received increasing attention, the unbalanced development and regional disparities of digital agriculture are still key obstacles to sustainable agricultural development. Based on the data of 31 provinces in China from 2013 to 2021, this study evaluates the development level of digital agriculture in China, and further analyzes the distribution pattern, spatial characteristics, and transition probabilities of digital agriculture from a regional perspective. The index system of the digital agriculture development level is constructed from five aspects: infrastructure, talent resources, agricultural informatization, the digitization of agricultural production processes, and agricultural production efficiency. Among these, infrastructure and talent resources reflect the resources needed for the development of digital agriculture; agricultural informatization and the digitization of the agricultural production process indicate the role of digitization in the process of agricultural development; and the agricultural production efficiency is the goal of the digital agriculture development, which is a critical criteria of its evaluation. The weighted analysis method of objective sequential analysis, which combines the dynamic level of indicators and sequential relationships, is used to assign weights to the indicators. In addition, to address the regional disparities in the development level of digital agriculture, kernel density estimation, Moran’s index, and (spatial) Markov chain analysis are applied to analyze the spatial dynamic evolution of digital agriculture in China. The findings reveal substantial regional disparities in digital agriculture development within China, particularly in the Western region, where development lags behind. Moreover, this study offers actionable policy recommendations for policymakers to strengthen regional infrastructure and talent cultivation, as well as other aspects of digital agriculture development, to mitigate regional differences and provide reference for other emerging countries.

The Impact of Digitalisation on Employment in Agriculture in Ukraine and Slovakia

The digital transformation is strategically important for the agricultural sector development, food security and employment in the agricultural sector and related industries. The study aimed to assess the impact of digitalisation on employment in agriculture in Ukraine and Slovakia. The study found that Ukraine and Slovakia have successfully implemented digital technologies in agriculture, which has led to positive results for agricultural production and sector competitiveness. Both countries use digital platforms to collect, process, and analyse data, contributing to efficient resource management and making agriculture more accurate and productive. However, Ukraine has a low correlation between the number of people employed in agriculture and investments in digital transformation, possibly due to the specifics of agriculture and infrastructure constraints. In Slovakia, however, the level of dependence is moderate, indicating more efficient use of digital tools in agriculture. For the further development of digital agriculture in both countries, it is crucial to consider the specifics of infrastructure, financing, and farmers’ readiness to implement digital solutions. Attention should also be paid to simplifying bureaucratic procedures and supporting research and innovation, which will help to increase the benefits of the digital transformation of agriculture and further improve the productivity and competitiveness of the sector.

Constraints and Countermeasures of Digital Economy Empowering High-quality Development in Chinese Agriculture

With the wide application of networking, informatization, and digitization technologies in agriculture, as well as the improvement of farmers’ modern information skills, the digital economy has become an important engine driving high-quality development and digital transformation in agriculture. Digital agriculture is an essential component of the national strategy for “Digital China” and an effective measure to achieve the deep integration of informatization and agricultural modernization. Driven by digital technologies, digital agriculture in rural areas shows tremendous development potential, huge market demand, and broad application prospects. The era of digital-driven agriculture and rural areas “leapfrogging” is approaching. However, the digital economy empowering high-quality development in agriculture also faces various constraints, such as lagging infrastructure construction, slow development of digital agriculture, insufficient endogenous driving forces, and difficulties in practical applications. To address these issues, the government should increase investment, clarify directions, promote industry academia research collaboration, and strengthen core technologies. These measures will enable the digital economy to better empower the high-quality development of Chinese agriculture.

The Impact of China's Digital Agriculture on Carbon Emissions under the Dual Carbon Target

This article measures carbon emissions and digital agriculture in 30 provinces and cities in China from 2011 to 2020, and empirically analyzes their influencing factors. The results indicate that the total carbon emissions and the overall level of digital agriculture development in China are showing an increasing trend; Under different quantiles, the impact of population size and digital agriculture development level on inter provincial carbon emissions decreases, the impact of industrial upgrading level first decreases and then increases, and the impact of economic development level and greening level increases; The population size will promote the rise of carbon emissions in the local area and suppress carbon emissions in neighboring areas, the level of economic development will promote the rise of carbon emissions in the local and neighboring areas, the level of digital agriculture development will suppress the rise of carbon emissions in the local area, and the level of industrial upgrading and greening will suppress the rise of carbon emissions in the local and neighboring areas. Therefore, it is recommended to promote China's accelerated progress towards carbon neutrality goals through rational allocation of energy elements, enhancement of ecosystem carbon absorption capacity, and vigorous development of digital agriculture.

DIGITAL AGRICULTURE – BASICS AND PREREQUISITES FOR DEVELOPMENT

Nowadays, the results achieved both from an ecological and an economic point of view are extremely important for the agricultural sector. Higher environmental performance cannot be achieved if the competitiveness of agricultural sectors declines. To increase the sustainability of the sector, better economic results must be achieved, which will lead to an opportunity to increase investments to protect the environment. This is where the need comes from, in this new, developing era, to think about "new" agriculture responding to the new circumstances. Without turning our backs on the past, both with its mistakes and its achievements. Vast agronomic knowledge can be integrated into digital innovation and mobilized to workforce the r better economic and environmental performance of farms and for the benefit of citizens and consumers. The report summarizes the main technological innovations. The aim is to outline the parameters and possibilities of modern agriculture to respond to the developing digital world. Based on the definition of the review, summaries and conclusions are made about the development of digital agriculture in the world

The Impact of the Digital Economy on High-Quality Development of Agriculture: A China Case Study

With the progress of information technology, digital technology has rapidly penetrated into all sectors of the national economy and is accelerating the reconstruction of the economic development model, which has become a new engine to drive economic growth and lead industrial development. Based on the panel data of China’s provinces from 2013 to 2020, this paper empirically tested the effect and mechanism of digital economic development on high-quality agricultural development by constructing an evaluation index system for high-quality agricultural development with economic, social, and ecological benefits. The results show that the development of the digital economy has promoted the high-quality development of agriculture, and the promotion effect in the eastern region is stronger than that in the central and western regions. In addition to direct promotion, digital economy also promotes high-quality agricultural development by promoting the development of green agriculture. We should actively promote the construction of digital economy and promote the deep integration of digital economy and agriculture. Secondly, government should improve the digital economy governance to create a good legal environment for the green development of agriculture and, at the same time, help farmers establish digital economic awareness and train farmers in digital economy vocational skills. Finally, digital agriculture development policies should be formulated according to local conditions.

A Single-Tree Point Cloud Completion Approach of Feature Fusion for Agricultural Robots

With the continuous development of digital agriculture and intelligent forestry, the demand for three-dimensional modeling of trees or plants using agricultural robots is also increasing. Laser radar technology has gradually become an important technical means for agricultural robots to obtain three-dimensional information about trees. When using laser radar to scan trees, incomplete point cloud data are often obtained due to leaf occlusion, visual angle limitation, or operation error, which leads to quality degradation of the subsequent 3D modeling and quantitative analysis of trees. At present, a lot of research work has been carried out in the direction of point cloud completion, in which the deep learning model is the mainstream solution. However, the existing deep learning models have mainly been applied to urban scene completion or the point cloud completion of indoor regularized objects, and the research objects generally have obvious continuity and symmetry characteristics. There has been no relevant research on the point cloud completion method for objects with obvious individual morphological differences, such as trees. Therefore, this paper proposes a single-tree point cloud completion method based on feature fusion. This method uses PointNet, based on point structure, to extract the global features of trees, and EdgeConv, based on graph structure, to extract the local features of trees. After integrating global and local features, FoldingNet is used to realize the generation of a complete point cloud. Compared to other deep learning methods on the open source data set, the CD index using this method increased by 21.772% on average, and the EMD index increased by 15.672% on average, which proves the effectiveness of the method in this paper and provides a new solution for agricultural robots to obtain three-dimensional information about trees.

Technological Revolution in the Field: Green Development of Chinese Agriculture Driven by Digital Information Technology (DIT)

According to the Plan for Rural Development of Digital Agriculture (2019–2025), accelerated integration of digital technologies and agriculture is crucial to promoting high-quality agriculture in China. The application of DIT in agricultural activities will not only help improve the efficiency of agricultural production, but also promote the green development of agriculture and the achievement of the Dual Carbon Target (DCT). In order to further clarify the comprehensive effects of the application of DIT in agricultural systems and provide routes for government decision-makers to assist in reducing agricultural emissions by DIT, this paper adopts the logical deductive method and starts with the application status to draw out the specific paths of low-carbon transformation in DIT-driven agriculture, while further discussing the potential issues in the process and corresponding solutions. DIT is a double-edged sword. It can promote the green and low-carbon transformation of agriculture by implementing precision operation, environmental monitoring, optimizing carbon emission accounting, and supervising the carbon market. However, at the same time, it may face problems such as unbalanced rural development and excessive financialization of the carbon market. Therefore, we should be optimistic but cautious about the application of DIT in reducing agricultural emissions. We can address potential problems by strengthening government-led investment, broadening channels for capital investment, strengthening skills training for farmers, and enhancing the regulation of trading in carbon sink markets.

Research on the Impact of Digital Agriculture Development on Agricultural Green Total Factor Productivity

In this study, to discover how the growth of digital agriculture has impacted agricultural green total factor productivity (AGTFP), we take panel data from 2011 to 2019 for 30 Chinese provinces as the research object, measure the growth of AGTFP and digital agriculture development using the SBM-ML and entropy method, and use a fixed effect model to analyze the effects of digital agriculture development on AGTFP. The results demonstrate that (1) from the time-series characteristics, digital agriculture presented a steady growth state from 2011 to 2019; (2) during the study period, the technical efficiency index was slightly lower than the technological progress index in the AGTFP index, meaning that there is room for further development; (3) the relationship between the growth of digital agriculture and AGTFP presents an inverted U-shaped curve, with human capital playing a moderating role. Finally, corresponding countermeasures are proposed in four aspects: strengthening the construction of organizational mechanisms, building a standardized base for digital agriculture output, enhancing the traceability and certification of agricultural products, and improving social services in the agricultural industry.

Recent Advancements in Agriculture Robots: Benefits and Challenges

In the development of digital agriculture, agricultural robots play a unique role and confer numerous advantages in farming production. From the invention of the first industrial robots in the 1950s, robots have begun to capture the attention of both research and industry. Thanks to the recent advancements in computer science, sensing, and control approaches, agricultural robots have experienced a rapid evolution, relying on various cutting-edge technologies for different application scenarios. Indeed, significant refinements have been achieved by integrating perception, decision-making, control, and execution techniques. However, most agricultural robots continue to require intelligence solutions, limiting them to small-scale applications without quantity production because of their lack of integration with artificial intelligence. Therefore, to help researchers and engineers grasp the prevalent research status of agricultural robots, in this review we refer to more than 100 pieces of literature according to the category of agricultural robots under discussion. In this context, we bring together diverse agricultural robot research statuses and applications and discuss the benefits and challenges involved in further applications. Finally, directional indications are put forward with respect to the research trends relating to agricultural robots.

The carbon emission reduction effect of digital agriculture in China.

Carbon emission reduction is gaining increasing attention worldwide. This paper focuses on how the development of digital agriculture contributes to agricultural carbon emission reduction. To this end, the spatial characteristics, spillover effects, and driving factors of digital agriculture on agricultural carbon emissions are explored using panel data of 31 regions in China from 2011 to 2019 using a spatial econometric model and STIRPAT model with the extension of an ARDL method that was utilized to demonstrate the linkage amid variables. The results show that digital agriculture development reduces agricultural carbon emissions. Firstly, the results remain robust after estimation using the replacement weight method and the explanatory variable substitution method. Agricultural technological progress, agricultural industry structure, and rural education level all contribute to the reduction of agricultural carbon emissions in a region. Secondly, agricultural carbon emissions in the neighboring regions have a negative relationship with the agricultural industry structure in the region and a positive relationship with rural education level and agricultural technological level. Finally, strengthening the exchange of digital agriculture between regions and leveraging the intermediary effect of digital inclusive finance can effectively enhance the carbon emission reduction effect.

Study on Digital Reconstruction Scheme of Wenzhou Cultural Auditorium

With the frequent emergence of Internet virtual reality application technology in the market, the field of virtual reality has been the focus of social attention, the development of virtual reality application technology has been greatly improved. It is a hot topic in various fields to make use of information and Internet platforms to integrate the Internet with traditional industries, and make use of the advantages and characteristics of the Internet to create new development opportunities. It is an important part of the implementation of the "digital village" strategy to study the digital reconstruction plan of the local cultural auditorium in Wenzhou City, which plays a substantial role in the development of digital agriculture, rural e-commerce and the promotion of digital precision empowerment.

Digital Agriculture in Viet Nam: Conditions and Prospect of Development

The real context of climate change and pandemic has emphasized the enormous significance of agriculture to society and paved a path to digitization. Each country's agricultural digitalization strategies must not only focus on the technological aspects of the production system but as well present an overview of how this field of study is establishing and developing. To address this issue, a research was carried out to identify priority research questions concerning digital agriculture in Viet Nam, but with a view to also informing international contexts. The study applied a combination of methods including descriptive statistics, review of related researches reflecting the application of digital technology in agriculture, as well as systematic and institutional approaches to create the conditions for the development of digital agriculture. Concurrently, taking into account the readiness limitation of economic actors' for digital transformation is also presented in this study. Viet Nam is in the early stages of digital transformation in agriculture. Digital readiness is critical to grasping and implementing existing technologies and transforming agriculture. In order for the digital transformation to come into play in a positive way, the institutional decisions of the authorities are crucial to the major challenges facing Viet Nam's agriculture, such as digital inequalities, human resources, financial, and infrastructure constraints and inadequate awareness of existing technologies.

Classification of Weeds of Paddy Fields using Deep Learning

Weed management is one of the important tasks in agriculture. Weeds in rice fields are usually managed using three ways - chemical herbicides, mechanical weeders, and manual weeding. Manual weeding becomes a problem when there is a shortage of agricultural laborers. Mechanical weeders are not suitable for direct-seeded rice fields. Chemical herbicides are not advisable especially when farmers do not know about site-specific weed management. Site-specific weed management is using the right herbicide in the right amount. Therefore, this paper investigates computer vision-based deep learning techniques with transfer learning classifying three types of weeds in paddy fields, namely sedges, grasses, and broadleaved weeds so that the right herbicide is recommended to the farmers. This would reduce the broadcast application and the overuse of the herbicides, thereby limiting the negative impact of the chemical herbicides on the environment. This research work shows promising results with an accuracy around 90% and thus encourages the development of digital agriculture.