Digital Agriculture Research Articles (Page 1)

Agriculture is one of the chief anchors of the Zimbabwe’s economy with a contribution of more than 9% to the GDP. The agricultural sector has faced unprecedent challenges which impacted the smallholder farmers’ ability to maximise their yields and, hence, revenues. Various challenges are impacting smallholder farmers' ability to effectively maximise their productivity on their land. The issues include limited access to markets, limited advisory services from agriculture extension officers, limited capital as well as logistics and procurement limitations. The digital agriculture platforms have emerged as the solution to tackle bottlenecks in agriculture extension services, access to markets, weather services, finance, procurement services and logistics. Welthungerhilfe (WHH) has been developing digital apps for smallholder farmers such as Kurima Mari and Agri-Share. These apps have been adopted with the objective of propelling the achievement of the zero hunger United Nations Sustainable Goals (SDG) through increased productivity, which culminates in higher yields and revenues. This study’s aim is to establish the extent to which WHH digital platforms are able to promote food security and sustainable agriculture through enhanced productivity and improved livelihoods amongst smallholder farmers. A qualitative approach to data collection through the use focus groups and structured questionnaires is employed. The findings reveal the capability of the digital agricultural platforms to solve key challenges faced by smallholder farmers. The study also establishes the relationship between the use of digital platforms and an increase in agriculture productivity, resulting in improved livelihoods of farmers. The study identifies the challenges faced by the farmers in utilising digital agric platforms. It also shows opportunities that can be exploited by the farmers through technology in driving sustainable agriculture.

The study was conducted to determine the effectiveness of using ICT tools for disseminating rice production information, to compare among the ICT tools and to explore the contribution of the selected characteristics of the farmers to their use of ICT tools. Data were collected from 340 rice farmers from a population of 2988 from four (4) selected upazillas of Bangladesh. Findings revealed that half of the farmers (50%) perceived moderate effective while 46.5 % and 3.5 % perceived high and low effectiveness of the ICT tools for disseminating rice production information. Based on the descending order of Standardized Effectiveness Index (SEIty) among the ICT tools ‘call center’ ranked first followed by ‘social media’, ‘mobile application’ and ‘internet browsing’. The multiple regression analysis showed that ICT using experience, ICT contact, knowledge of using ICT tools, and attitude towards using ICT tools had significant positive contribution to their perceived effectiveness of using ICT tools for dissemination rice production information. The findings may contribute in policy formulation for improving agricultural extension services and promoting digital agriculture in Bangladesh.

Against the backdrop of unbalanced urban-rural development in China, the digital transformation of rural areas has become a key path to address the dilemmas of rural development, improve agricultural production efficiency, and promote industrial upgrading. Taking Dayi County of Sichuan Province as a case study, this research conducts on-site investigations into the application of digital technology in local fields such as cultural tourism, people's livelihoods, and digital agriculture. It comprehensively evaluates the application effects and development potential of digital technology in agricultural production, rural governance, cultural inheritance, and other aspects, explores the practical benefits of the digital economy for rural development, and aims to provide theoretical basis and practical references for improving rural digital policies. The study finds that some rural areas have established relatively complete digital infrastructure and industrial systems, laying a foundation for sustainable development. However, there are still obvious deficiencies in the development of digital cultural tourism, the digital inheritance of traditional culture, and the popularization and application of intelligent equipment. In the future, it is necessary to strengthen policy support and technological investment targeting these weak links, while focusing on development strategies based on regional differences to promote the balanced development of rural digital transformation. It is worth noting that the application of digital technology must be combined with the actual needs of local areas to truly play its positive role in promoting rural revitalization, and efforts should be made to meet the needs of different regions.

Abstract This study examined how and why extension workers shared farming videos with farmers, revealing divergent appropriation patterns and their implications for digitization in agriculture. 294 extension workers in Bihar (India) were asked to circulate three wheat agronomy videos with farmers. Extension workers’ circulation of these videos was observed using link tracking, phone surveys, and follow-up interviews. Results were analyzed using a novel analytic framework based in affordance theory. Extension workers varied widely in how much, how, and why they shared the farming videos. This variation was underpinned by extension workers’ differing incentives and goals. In other words, extension workers heterogeneously appropriated—rather than homogeneously deployed—the practice of sharing farming videos. Some but not all of these appropriations were desirable from the perspective of service managers. For theory, extension workers’ appropriations of farming videos demonstrate that prevalent conceptualizations of digital agricultural technologies do not account for the adaptation of these technologies by farmers and other actors in agricultural innovation systems. For digital agriculture evaluators, the findings caution against the prevalent focus on averaging effects of interventions and highlight the need to examine the variability of these effects within and across interventions. For extension service managers, the findings emphasize the importance of engaging extension actors with farmer-aligned incentives and goals. This study was limited in focusing on the video-sharing behaviors of human extension actors and not on algorithmic extension actors, like YouTube or farming advisory chatbots powered by large language models. However, the findings have implications for both: just as human actors variably appropriate digital tools, algorithmic extension actors also embed implicit goals that shape how agricultural information circulates. Future research should examine the goals and behaviors of these algorithmic actors that have increasing influence in agricultural innovation systems.

The agricultural sector is transforming through digital technologies and data-driven tools, creating both opportunities and ethical challenges. This perspective paper explores the ethical dilemmas that arise where agricultural data intersect with trust, equity and sustainability. Farmers’ trust issues with data-driven technologies are closely linked to how data are collected, processed and used. We emphasise the importance of data governance, highlighting the need for trust, transparency, accountability and participatory governance within agricultural data systems. The paper also discusses equity concerns, such as the disparity between smallholder farmers and large-scale agribusinesses, ambiguities surrounding data ownership and the risk of marginalising certain communities within the digital agriculture paradigm. Environmental sustainability is another key focus. While data analytics can support more efficient and climate-smart farming, they also raise questions about long-term resource management and environmental impacts. Incorporating multiple disciplinary perspectives, the paper underscores ongoing issues related to power dynamics, governance, the innovation gap and the importance of embracing diversity. Drawing from these perspectives, we offer conclusions and explore how these approaches might be integrated to address the outlined ethical challenges. Finally, we highlight key ethical issues framed as vital to shaping the future of agricultural data. This paper aims to provide ethical frameworks and recommendations for the responsible use of agricultural data.

Digital agriculture in India: A review of ICT interventions for sustainable farming

Localkart Hyperload E-Commerce presents a novel mobile platform aimed at revolutionizing agricultural business processes through advanced information and communication technologies. The platform enables farmers to manage product listings, integrate real-time market rate data, and receive customer feedback, while offering customers a seamless interface for product discovery, peer reviews, and secure payment processing. Modern mobile development techniques utilizing Flutter and Dart combine with robust backend services provided by Firebase, including Authentication, Firestore. Integration of chatbot for providing help to Customers and the recommendations ensures a highly interactive and responsive user experience. By addressing technical and operational challenges inherent in digital agriculture, Android Farmer App empowers rural stakeholders and enhances overall market efficiency.

Dragon fruit (Hylocereus spp.), an increasingly popular tropical fruit, has attracted global interest because of its distinct appearance, nutritional value, and health advantages. As cultivation has spread from its native Central and South American regions to tropical and subtropical regions around the world, there is an increasing demand for sustainable production approaches to address environmental, economic, and social issues. This study provides current knowledge on three different types of dragon fruits—H. megalanthus, H. undatus, and H. costaricencis—and their biology, agronomic requirements, and worldwide production trends, highlighting the critical need for management solutions. Conventional practices, which frequently rely on chemical fertilizers and pesticides, are compared with new sustainable approaches such as organic amendments, high tunnel systems, and agroforestry. This review examines regional challenges like climate variability, pest and disease issues, and pollination limits and explores innovative solutions to boost production and resilience. Socioeconomic factors, including market trends, farmer education, and policy frameworks, are recognized as key influences on the adoption of sustainable practices. This article highlights important research gaps, including the need for genetic improvement, digital agriculture tools, and strong certification mechanisms. This review integrates diverse views and provides actionable ideas for researchers, policymakers, and practitioners seeking to increase productivity, environmental stewardship, and long-term sustainability in dragon fruit agriculture.

Agriculture stands at a pivotal juncture in the twenty-first century, confronting the converging crises of climate change, biodiversity loss and rising food demand, even as it is increasingly recognised as part of the solution. This paper assesses the transformative potential of integrating three emerging paradigms—digital agriculture, regenerative agriculture and decommoditised agriculture—into a unified approach capable of delivering productivity, ecological restoration and economic viability. Digital agriculture deploys artificial intelligence, Internet of Things (IoT) networks and remote sensing to optimise inputs and sharpen decision-making. Regenerative agriculture seeks to rebuild soil function, enhance biodiversity and restore ecosystem processes through holistic, adaptive management. Decommoditised agriculture reorients value chains from bulk markets towards quality-differentiated systems that privilege direct producer–consumer relationships, value-added processing and regional market development, enabling price premiums and community resilience. We examine their convergence through the “3N” lens—net-zero greenhouse gas emissions, nature-positive outcomes and nutrition-balanced food systems. Integration creates clear complementarities: digital tools monitor, verify and optimise regenerative practices; regenerative systems provide the ecological foundation for sustainable intensification; and decommoditised models supply economic incentives that reward stewardship and nutritional quality. Persistent barriers include the digital divide, data governance, technical complexity and fragmented policy settings. Realising the benefits will require technology democratisation, interdisciplinary research, enabling regulation and farmer-centred innovation processes. We conclude that converging digital, regenerative and decommoditised approaches offers a credible and necessary pathway to resilient, sustainable and equitable agri-food systems.

Purpose The study aims to address persistent inefficiencies in agricultural product trading—namely, information asymmetry, market opacity, and trading inefficiency—that hinder supply chain profitability and disrupt market stability. It explores how intelligent systems, empowered by large language models (LLMs), can enhance transaction transparency, optimize decision-making, and improve market performance. Design/methodology/approach This research integrates large-scale language models (LLMs) into an online auction framework through dynamically adaptive intelligent agent systems. These agents simulate real-world market behaviors and leverage machine learning algorithms to enhance auction decision-making. An empirical evaluation is conducted using three experimental groups, with performance assessed across multiple quantitative metrics. Statistical significance is tested using Tukey’s HSD post-hoc analysis. Findings The results reveal that the LLM-based agent system significantly outperforms the other two benchmark approaches on all performance indicators. The system demonstrates superior capability in mitigating the effects of information asymmetry, improving pricing accuracy, and facilitating more efficient and transparent transactions. Research limitations/implications Geographic and temporal data limitations may impact generalizability; further research is needed. Practical implications The proposed model offers actionable insights for designing next-generation online trading platforms in the agricultural sector. By embedding LLMs into intelligent agent systems, market participants—including farmers and buyers—can benefit from enhanced information flow, reduced transaction costs, and more balanced supply-demand interactions. Social implications Promotes fair transactions, reducing market information asymmetry for sustainable agricultural development. Originality/value This study is among the first to incorporate LLMs into auction-based agricultural trading systems, demonstrating the transformative potential of AI in reshaping market mechanisms. The research bridges artificial intelligence with smart agriculture, contributing a novel framework that advances both theoretical understanding and practical innovation in digital agriculture and e-marketplace design.

The rapid growth of transactions in Indonesian agricultural marketplaces leaves a gap between large data volumes and suboptimal marketing strategies. As a novel contribution, this study explicitly applies the Apriori algorithm to the context of agricultural e-marketplaces in Indonesia, serving as a validated case study. The primary data used in this analysis is a collection of historical transaction data obtained from one such marketplace. The dataset described in this case study includes 10 transaction histories involving 22 product items. This study aims to transform untapped historical data into business-strategy insights. By setting minimum support and confidence to 50%, the analysis successfully identified significant association rules. The strongest rule indicated a co-purchase pattern between specific products with a confidence value of 60.6% and a Lift Ratio of 11.1, indicating a robust positive correlation. This rule was then successfully implemented into a functional recommendation feature. Validation testing demonstrated complete consistency between the system results and manual calculations. This case study demonstrates the effectiveness of Apriori and provides a benchmark for developing similar technologies to improve sales and user experience in Indonesia's digital agriculture sector.

This study first analyses the use of digital technologies in agriculture from two different perspectives. Then, correlation analyses using these data aim to show which countries focus on which of the two perspectives are ahead in agricultural exports. In the study, publications containing the word ‘agriculture’ in the title, abstract, or keywords section of the Scopus database and the keywords ‘digital technology’ and ‘production’ in the all-fields section were selected and recorded as publications in which digital technologies in agricultural production were investigated. Then, in the Scopus database, publications containing the word ‘agriculture’ in the title, abstract or keywords section and the words ‘digital technology’ and ‘marketing’ in the all-fields section were kept in a separate category and recorded as publications in which digital technologies in marketing of agricultural products were investigated. These two data sets were subjected to correlation analysis with export data obtained from OECD databases, and interactions on the axes of agriculture, digital technologies, production, and marketing were revealed. After selecting the OECD member countries with export data from all the data obtained, the number of academic publications of these countries with the specified conditions is given in separate tables. The correlation analysis on OECD member countries revealed a statistically significant and strong positive relationship between the average agricultural export volumes for 2020-2024 and the number of scientific publications containing the terms agriculture, digital technologies, and production indexed in the Scopus database. The Pearson correlation coefficient was calculated as r = 0.813, and the significance level was p = 0.004. When a similar correlation analysis was conducted with publications containing agriculture, digital technologies, and marketing terms, the Pearson correlation coefficient was r = 0.958 with a significance level of p < 0.001. This finding indicates that countries that produce higher levels of academic output in agriculture (publications containing both production and marketing terms) tend to have higher export performance in the agricultural sector. The results suggest that there may be a significant relationship between academic productivity and economic output in international agricultural trade.

Agricultural new-quality productive capacity is a key driver for enhancing food security and achieving agricultural modernization. Based on panel data from 30 provinces and unicipalities in mainland China from 2013 to 2022, this study constructed a dual fixed-effects and mediation effects model and employed empirical analysis methods to validate the impact of agricultural new-quality productive capacity on food security and its underlying mechanisms. Research results show that developing new high-quality production capacity in agriculture greatly improves food security, but with heterogeneity. In grain-producing regions and eastern regions, the enabling effect of agricultural new-quality productive capacity on food security is more pronounced. The mediating effect suggests that the development level of rural industrial chains and supply chains, as well as digital agriculture, play a mediating role in the process by which agricultural new-quality productive capacity promotes food security.

In the context of rural informatization and sustainable development, more and more people are accessing global agricultural knowledge systems, environmental policy frameworks, and digital extension services through English based information platforms. However, the limited English proficiency of rural populations poses a significant obstacle to their participation in these knowledge ecosystems. The authors propose an innovative framework based on multimodal data fusion and intelligent feedback mechanism to improve the English listening comprehension ability of rural students, enabling them to participate in digital agriculture and environmental information systems. Through comparative experiments, the research results indicate that the improvement of English listening skills directly enhances the ability of rural learners to acquire, interpret, and apply knowledge from global agricultural and environmental portals. This article contributes to the broader goal of rural revitalization that integrates education, digital infrastructure, and sustainable agriculture.

ABSTRACT The Farmers Portal is an integrated, multi- functional digital platform developed to empower farmers by addressing critical challenges such as limited access to expert advice, fragmented market information, inefficient resource management, and underutilization of government welfare schemes. Agriculture continues to sustain millions globally, yet farmers face persistent challenges such as pest infestations, crop diseases, unpredictable weather, and fluctuating market prices. The portal addresses these issues by providing real-time crop guidance, expert consultation, market intelligence, e- commerce functionality, and detailed information on government schemes. The system utilizes a relational database to securely manage users, crops, products, orders, and government programs, ensuring scalability, consistency, and reliable data retrieval. The development of this portal follows a structured methodology involving requirement gathering, system design, database architecture, and web- based implementation using PHP,MySQL/MariaDB, HTML, CSS, and JavaScript. Evaluation of the system indicates significant improvements in agricultural decision-making, productivity, and farmer income. Farmers reported enhanced access to expert guidance, timely market data, and government support, while the e- commerce module allowed direct sales to consumers, minimizing intermediaries. The platform exemplifies the potential of digital technologies to transform traditional agriculture into a data-driven, sustainable, and economically viable practice. Future enhancements include IoT integration for real-time monitoring, AI-driven predictive analytics for yield and market trends, mobile application development, and multilingual support to broaden accessibility.

This paper explores the possibilities for boosting the role of agricultural economics, in particular, the role of family-based farming, through the use of digital technologies. Overcoming the barriers to digital technology adoption is essential for driving meaningful transformation within agriculture. In our study, we conducted a regional pilot survey on attitudes of farmers towards digital agriculture in three countries within the NENA region. The methodology is based on existing documentation and strives to gather fresh insights by means of a survey questionnaire to identify and assess needs, gaps and challenges of adopting digital agriculture. Farmers survey revealed that the adoption of digital technologies has led to improved productivity, increased efficiency and reduced costs. The use of drones and satellite imagery optimized the use of fertilizers, water and other inputs. Similarly, the use of mobile apps and online marketplaces facilitated access to information, connection with buyers and access to financial services. Nonetheless, farmers highlighted difficulties of inadequate availability of digital infrastructure and services, insufficient digital skills, and apprehensions regarding data protection and confidentiality. This study addresses a gap in the literature by providing insights to design tailored programs and ensuring that digital agriculture is more accessible and beneficial. This study reports how farmers perceive digital tools, particularly, how does digital agriculture diffuse amongst farmers, and its impact on the resilience of farming communities. This research presents diverse pathways through which digital agriculture can tackle key issues within farming systems and contribute to shifts in social norms and behaviours that remains underrepresented in existing literature, particularly within the current digital agriculture landscape of the NENA region.

Soft compliance and hard laws: regulatory strategies against inequalities in digital agriculture

The economic conversion of agricultural land often disregards its environmental significance, threatening food security and ecosystem integrity. This study evaluated the potential of agricultural land resources within the Sustainable Food Agricultural Land Area (LP2B) in the Special Region of Yogyakarta Province, Indonesia, by quantifying their total economic value (TEV) and developing strategic recommendations for sustainable land management. Utilizing a mixed-method approach based on secondary data and SWOT analysis, the research revealed that Bantul Regency is the regency with the highest total economic value of agricultural land, and Gunungkidul is the regency with the lowest total economic value, exhibiting high TEV due to its robust agricultural productivity and ecological functions. The study emphasized indirect benefits—including flood and erosion control—that are undervalued in current land-use decision-making. Strategic solutions such as digital agriculture initiatives and farmer regeneration programs are proposed to mitigate land conversion and bolster rural resilience. These findings provide actionable insights for policymakers aiming to balance economic development and environmental sustainability in rapidly urbanizing regions.

The integration of machine learning (ML) algorithms with statistical analysis and user-friendly interfaces has become crucial for democratizing advanced analytics across various domains, particularly in digital agriculture. This paper presents ImMLPro (Intelligent Machine Learning Professional), a comprehensive Shiny-based web application that seamlessly integrates R programming, machine learning algorithms, and statistical analysis for continuous variable prediction. The platform addresses the growing need for accessible ML tools that eliminate coding barriers while maintaining analytical rigor. ImMLPro incorporates four state-of-the-art algorithms: Random Forest, XGBoost, Support Vector Machines (SVM), and Neural Networks, providing comparative analysis, hyperparameter optimization, and comprehensive visualization capabilities. The application's architecture facilitates real-time model training, performance evaluation, and result interpretation through interactive dashboards. Designed with digital agriculture applications in mind but applicable across domains requiring continuous variable prediction, ImMLPro represents a significant advancement in making complex ML algorithms accessible to nonprogramming experts. The platform's integration of R's statistical computing power with modern web technologies demonstrates the potential for bridging the gap between sophisticated analytical methods and practical implementation in agricultural decision-making and beyond.

Mapping banana and peach palm in heterogeneous landscapes remains challenging due to spatial heterogeneity, spectral similarities between crops and native vegetation, and persistent cloud cover. This study focused on the municipality of Jacupiranga, located within the Ribeira Valley region and surrounded by the Atlantic Forest, which is home to one of Brazil’s largest remaining continuous forest areas. More than 99% of Jacupiranga’s agricultural output in the 21st century came from bananas (Musa spp.) and peach palms (Bactris gasipaes), underscoring the importance of perennial crops to the local economy and traditional communities. Using a time series of vegetation indices from Sentinel-2 imagery combined with field and remote data, we used a hierarchical classification method to map where these two crops are cultivated. The Random Forest classifier fed with 10 m resolution images enabled the detection of intricate agricultural mosaics that are typical of family farming systems and improved class separability between perennial and non-perennial crops and banana and peach palm. These results show how combining geographic information systems, data analysis, and remote sensing can improve digital agriculture, rural management, and sustainable agricultural development in socio-environmentally important areas.