Digital Agriculture Research Articles

The Impact of Digital Agriculture on Green Productivity in Agriculture: Evidence from China

The Impact of Digital Agriculture on Green Productivity in Agriculture: Evidence from China

AgDataBox-Map - A web application for creating thematic maps and management zones: users' evaluation

Agriculture is confronted with the imperative to enhance output while simultaneously ensuring increased profitability and minimizing ecological repercussions. Consequently, digital technologies have emerged as essential instruments for the acquisition of data, the analysis of information, and the facilitation of decision-making. The digital platform AgDataBox (ADB, https://adb.md.utfpr.edu.br/map) provides no-cost computational resources for agricultural professionals, scholars, and service entities, with a focus on Precision Agriculture (PA) by integrating data, software, protocols, and methodologies to facilitate agricultural progress through the utilization of open-access technologies. The purpose of this study was to examine how users assess the ADB-Map. The AgDataBox Map (ADB-Map) application works with spatial data to create thematic maps and management zones to subsidize PA and digital agriculture. The ADB-Map functionalities are divided into different layers, composed of (i) a back-end, which contains algorithms and rules of business operation, and (ii) a front-end, which is the interface of interaction with the user. By applying the evaluation methods suggested by NBR ISO/IEC 14598-6 across all areas, the ADB-Map achieved scores above 85% (the standard mandates a minimum of 70%), and an overall score of 92% was achieved. Hence, we believe users widely accept the software.

Human-Centered Robotic System for Agricultural Applications: Design, Development, and Field Evaluation

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.

Factors that affecting firm performance mediated by digital transformation in Indonesia’s state-owned enterprises plantation and forestry industries

Digital transformation refers to the integration of digital technology into all areas of a business, fundamentally changing the way it operates and adding value to a firm's performance. This can involve adopting new technologies, reorganizing business processes, and creating a digital culture within the organization. One of the State-Owned Enterprises (SOE) in Indonesia, PT Perkebunan Nusantara III (Persero) with a focus on agriculture and main commodities, namely Palm Oil and Rubber, is carrying out a digital transformation initiative with the aim of improving firm performance. Along the way, the company faced many obstacles related to implementing digital transformation and the large investment costs incurred. This research aims to test the influence of Digital Transformation Leadership, IT Capability, Digital Mindset, Work Culture Change, Organizational Agility, Human Capital and IT Alignment with Business and test the influence of these variables on Firm Performance with Digital Transformation as a mediating variable. This research uses quantitative research methods by distributing online questionnaires via Google Form to 234 company employees who use Digital Farming, GIS and SCADA applications. Data was analyzed using the SEM-PLS analysis method with Smart-PLS Software version 3.3.9. The results of the research show that IT Capability, Work Culture Change, IT Alignment With Business have a positive effect on Digital Transformation. Apart from that, this research also shows the positive influence of Digital Transformation Leadership on Digital Transformation which is mediated by IT Capability and Digital Mindset on Digital Transformation which is mediated by Work Culture Change. Then, research also shows that Digital Transformation has a significant effect on Firm Performance. the research outcomes can serve as valuable insights for what factors influence firm performance mediated by digital transformation in the SOE plantation and forestry industry business cluster in Indonesia.

Leaf Identification in High-Density LiDAR-RGB Data

Abstract. Leaf detection through automated segmentation of 3D data is becoming a crucial technique in many applications of digital agriculture. Some 3D segmentation techniques that can be mentioned are based on normal differences and median normalised vector growth. However, applying these approaches to high canopy density data remains challenging. In this study, we propose a processing flow for leaf detection in high canopy density LiDAR-RGB point clouds. First, a noise removal technique inspired by Moving Least Squares (MLS) was applied to the LiDAR point cloud, and a RGB colour was assigned to each point by combining computer vision and photogrammetric methods. Moreover, once the data were suitable for leaf detection, the branches were filtered using the Statistical Outlier Removal (SOR) filter based on an analysis of the statistical behaviour of the neighbourhood. Afterwards, an unsupervised DBSCAN (Density-Based Clustering Non-Parametric Algorithm) method was used to segment similar points. Finally, the points within each cluster were identified as leaf or non-leaf using the RGB values implemented by our method; ground points were filtered out using a maximum height threshold. As a result, the leaf, non-leaf, and ground point identifiers were correct in 98.9% of cases, with the branch filtering technique SOR proving effectiveness in removing branches with low information loss and without additional complex point densification steps in reconstruction. This SOR-based solution overcomes major challenges in semantic segmentation (leaves and branches) in high-density data and potentially contributes to precision agriculture.

Digital agriculture in economic development in Vietnam

Digital agriculture, incorporating artificial intelligence, IoT, and big data, is becoming a crucial trend in Vietnam to address challenges posed by population growth and climate change. The Ministry of Agriculture and Rural Development has devised a Digital Transformation Plan to confront these challenges, particularly significant in a nation heavily reliant on agriculture. Our research employs a multi-method approach to assess the contribution of digital agriculture to Vietnam’s economic development, focusing on private enterprises and public-private collaborations. Results demonstrate that digital agriculture has enhanced resource management and agricultural productivity. This study provides detailed insights into the current status and potential of digital agriculture in Vietnam, highlighting the roles of private enterprises and public-private partnerships in the nation’s digital transformation.

A Temporal–Geospatial Deep Learning Framework for Crop Yield Prediction

With the rapid development of information technology, the demand for digital agriculture is increasing. As an important agricultural production topic, crop yield has always attracted much attention. Currently, artificial intelligence, particularly machine learning, has become the leading approach for crop yield prediction. As a result, developing a machine learning method that accurately predicts crop yield has become one of the central challenges in digital agriculture. Unlike traditional regression prediction problems, crop yield prediction has a significant time correlation. For example, weather data for each county show strong temporal correlations. Moreover, geographic information from different regions also impacts crop yield to a certain extent. For example, if a county’s neighboring counties have a good harvest, then this county is likely to have high yields as well. This paper introduces a novel hybrid deep learning framework that combines convolutional neural network (CNN), graph attention network (GAT) and long short-term memory (LSTM) modules to enhance prediction accuracy. Specifically, CNN is employed to extract the features from the input data for each county in each year. GAT is introduced to model the geographical relationships between neighboring counties, allowing the model to capture spatial dependencies more effectively. LSTM is used to extract the temporal information within many years. The proposed hybrid deep learning framework CNN-GAT-LSTM captures both the temporal and spatial relationships, thereby improving the accuracy of yield prediction. We conduct experiments on a nationwide dataset that includes data from 1115 soybean-producing counties in 13 states in the United States covering the years from 1980 to 2018. We evaluate the performance of our proposed CNN-GAT-LSTM model based on three metrics, namely root of the mean squared error (RMSE), R-squared (R2) and correlation coefficient (Corr). The experimental results demonstrate that the proposed model achieves significant performance improvements over the existing state-of-the-art model, with RMSE reduced by 5%, R2 improved by 6% and Corr enhanced by 4%.

Unlocking Innovation in Crop Resilience and Productivity: Breakthroughs in Biotechnology and Sustainable Farming

Recent advancements in agricultural biotechnology and sustainable practices are revolutionizing crop production and resilience, addressing global food security challenges. This review highlights cutting-edge innovations in plant sciences, including genetic engineering, genome editing technologies, and biotechnological interventions that enhance crop resistance to biotic and abiotic stresses. It discusses the integration of precision agriculture and digital farming tools that optimize resource use and boost productivity, providing data-driven insights for better crop management. Additionally, sustainable agricultural practices such as agroecology, organic farming, and integrated pest management are examined for their potential to reduce environmental impact while maintaining high yield. The review investigates the key practices like conservation tillage, crop rotation, and biological pest control, emphasizing their roles in enhancing soil health, biodiversity, and agricultural resilience. Significant contributions of biotechnological interventions, including genetic engineering and genome editing, are explored for developing stress-resistant and high-yield crop varieties. Moreover, the integration of precision agriculture and digital farming technologies is discussed for their potential to optimize resource utilization and improve crop management through advanced data analytics.

Internet of Things : A Way of Transforming Conventional Agriculture

The Internet of Things (IoT) is the network of various devices seamlessly exchanging data. IoT has initiated the 4th agricultural revolution known as Agriculture 4.0/Digital Agriculture (DA)/Precision Agriculture (PA)/Smart Agriculture (SA). All farming activities can be monitored and managed remotely. With IoT, agriculture is becoming more energy-efficient, precise and effective. IoT is emerging as a potential solution to current world problems. In this review paper, the application of IoT in various agricultural activities is discussed. The paper highlights how IoT is transforming the execution of various agricultural activities.

Impact of inbound marketing on Consoagro junior company of the State University of Londrina

This work addressed the transition of Consoagro Empresa Júnior to Inbound Marketing in 2021, 2022, and 2023, highlighting the growing relevance of agricultural innovation and communication strategies. The adoption of Inbound Marketing was motivated by the need to acquire a strategy that would strengthen Consoagro's brand presence in the digital environment and assist in the passive acquisition of clients. With the general objective of analyzing the impacts of this transition, a quantitative approach was employed, using Consoagro as a case study. The methodology included document analysis and impact assessment over the years using Excel data spreadsheets and statistical analysis using the ANOVA and Tukey test in R software. After analyzing the results, it was possible to identify that the strategy not only strengthened Consoagro's online presence but also contributed to the promotion of digital agriculture. Facing challenges such as increasing market competition and the lack of prospecting, the company turned these difficulties into strategic opportunities, using the production of interesting and relevant content to attract and engage its audience effectively. In summary, this study highlighted the importance of innovation as an essential catalyst for facing contemporary challenges in the agricultural context. By integrating Inbound Marketing as part of a broader strategy of growth and adaptation, Consoagro not only improved its visibility and operational efficiency but also established a successful model for other junior companies in the agricultural sector seeking to differentiate and thrive in a competitive and dynamic environment.

Predicting oil accumulation by fruit image processing and linear models in traditional and super high-density olive cultivars

The paper focuses on the seasonal oil accumulation in traditional and super-high density (SHD) olive plantations and its modelling employing image-based linear models. For these purposes, at 7-10-day intervals, fruit samples (cultivar Arbequina, Fasola, Frantoio, Koroneiki, Leccino, Maiatica) were pictured and images segmented to extract the Red (R), Green (G), and Blue (B) mean pixel values which were re-arranged in 35 RGB-derived colorimetric indexes (CIs). After imaging, the samples were crushed and oil concentration was determined (NIR). The analysis of the correlation between oil and CIs revealed a differential hysteretic behavior depending on the covariates (CI and cultivar). The hysteresis area (Hyst) was then quantified and used to rank the CIs under the hypothesis that CIs with the maximum or minimum Hyst had the highest correlation coefficient and were the most suitable predictors within a general linear model. The results show that the predictors selected according to Hyst-based criteria had high accuracy as determined using a Global Performance Indicator (GPI) accounting for various performance metrics (R2, RSME, MAE). The use of a general linear model here presented is a new computational option integrating current methods mostly based on artificial neural networks. RGB-based image phenotyping can effectively predict key quality traits in olive fruit supporting the transition of the olive sector towards a digital agriculture domain.

Digital agriculture’s impact on carbon dioxide emissions varies with the economic development of Chinese provinces

Digital empowerment is a dynamic process of complementary digital technology and production factors and plays a critical role in achieving the dual carbon objectives of the carbon peak by 2030 and carbon neutrality by 2060. Here, we use the input-output method, employing panel data on environmental and social factors from 30 provincial-level regions in China from 2012 to 2021. We explore the impact of digital empowerment development on agricultural carbon dioxide emissions and underlying mechanisms. We found that the relationship between digital empowerment and carbon dioxide emissions is nonlinear and follows an inverted U-curve. The carbon dioxide emissions increase as digital empowerment increases and decrease when digital empowerment crosses the inflection point of 0.0862. Digital empowerment reduces agricultural carbon emissions by optimizing carbon-intensive factor inputs such as fertilizers and improving factor allocation efficiency. Our research provides evidence for policymakers to enable the promotion of digital empowerment of farmers across Chinese provinces.

Awareness and perception on the use of smartphone applications for crop protection among undergraduate students in Southwestern Nigeria

Mobile applications in crop protection especially for speedy pest diagnosis, monitoring, and management are increasingly gaining global attention and support. An understanding of the awareness and perception of the youths, particularly students in tertiary institutions (who are heavy users of smartphones), on the use of smartphone applications for crop protection, is crucial for sharpening their orientations towards embracing the digital ecosystem in agriculture. The imperativeness is evident in the ongoing government drive towards promoting students’ engagement in digital agriculture as accommodated in the National Agricultural Technology and Innovation Policy (2022–2027). Consequently, this study investigated awareness and perception of mobile phone applications among crop and agricultural extension-related final-year students in selected Universities in Southwest Nigeria. A multistage sampling procedure was used in selecting 407 students. Data were obtained with the aid of questionnaires. Data were summarized using descriptive statistics such as frequency counts, percentages, mean and standard deviation using SPSS software. Findings show that very few (less than 8%) of the respondents were aware of the usage of specific smart/android phone applications for pest identification, disease diagnosis, and weather forecasting. Less than 4% ever used smart/android phones for pest identification, crop protection, and information sourcing on disease diagnosis and management. However, their perception of the need for pest and disease diagnosis as a foundation step to enhance crop protection (4.65 points on a scale of 5), and readiness to use android/smartphone applications for this purpose (MS = 4.45) was positive. There are enormous implications for increased awareness and knowledge sharing on the use of android/smartphone applications for crop protection and other interventions to promote digital agriculture among students in Nigeria.

Sensing and Perception in Robotic Weeding: Innovations and Limitations for Digital Agriculture

The challenges and drawbacks of manual weeding and herbicide usage, such as inefficiency, high costs, time-consuming tasks, and environmental pollution, have led to a shift in the agricultural industry toward digital agriculture. The utilization of advanced robotic technologies in the process of weeding serves as prominent and symbolic proof of innovations under the umbrella of digital agriculture. Typically, robotic weeding consists of three primary phases: sensing, thinking, and acting. Among these stages, sensing has considerable significance, which has resulted in the development of sophisticated sensing technology. The present study specifically examines a variety of image-based sensing systems, such as RGB, NIR, spectral, and thermal cameras. Furthermore, it discusses non-imaging systems, including lasers, seed mapping, LIDAR, ToF, and ultrasonic systems. Regarding the benefits, we can highlight the reduced expenses and zero water and soil pollution. As for the obstacles, we can point out the significant initial investment, limited precision, unfavorable environmental circumstances, as well as the scarcity of professionals and subject knowledge. This study intends to address the advantages and challenges associated with each of these sensing technologies. Moreover, the technical remarks and solutions explored in this investigation provide a straightforward framework for future studies by both scholars and administrators in the context of robotic weeding.

Data-Driven Innovations and Sustainability of Food Security: Can Asymmetric Information Be Blamed for Food Insecurity in Africa?

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.

A tool for integrating agrometeorological observation data for digital agriculture: A Minnesota case study

AbstractAgrometeorological data are essential for understanding production using digital agriculture techniques. However, integrating agrometerological observations from multiple sources remains a challenge. Often, digital agriculture scientists download and clean the same datasets many times. We present a prototype system that simplifies the process of collecting, cleaning, integrating, and aggregating data from meteorological data sources by providing a simplified user interface, database, and application programming interface. The prototype provides a standard interface for querying multiple geospatial formats (raster and vector) and integrates observation networks including the National Oceanic and Atmospheric Administration Global Historical Climatology Network (NOAA GHCN), NOAA NClim‐Grid (NOAA's Gridded Climate Normals), and Ameriflux BASE. The system automatically checks and updates data, saving storage space and processing time, and allows users to summarize data spatially and temporally. Provided as open source code and browser‐based user interface, the application and integration system can be run across Windows, Linux, and Mac environments to support broader use of multi‐source agrometeorology data.

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.

From profitability to trust: factors shaping digital agriculture adoption

From profitability to trust: factors shaping digital agriculture adoption

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.

Assessment of the regional development of the agricultural sector in the context of digital transformation

Abstract The current stage of agricultural development is characterized by its susceptibility to the latest changes caused by the introduction of digital technologies into the processes of organizing economic activity. This leads to the need for an economic assessment of the state of the industry to determine its readiness for transformation in the context of digitalization. This issue is particularly relevant for the regional economy, in which high-tech digital agriculture can become a source of growth in the ecosystem of innovative development of the agro-industrial complex of the country. Therefore, the purpose of the article is to study certain aspects of the functioning of agriculture in the Novgorod region, which act as determinants of the development of the industry in the context of digitalization. The paper identifies the factors influencing the digital transformation of agricultural organizations, as well as identifies the specifics of the functioning of the industry in the region during the transition to a digital business model.