Enterprise AI Analysis
Mobile Technology in Agriculture: Global Research Trends and Applications
This detailed analysis, powered by advanced AI, distills the core insights and strategic implications of the research for enterprise applications, focusing on agricultural productivity, sustainability, and technological integration.
Executive Impact Summary
Mobile technology is transforming agriculture, but its adoption faces hurdles like digital literacy, unreliable connectivity, high costs, and gender-specific barriers. This study provides a comprehensive bibliometric analysis (2018-2024) of global research trends, identifying key themes such as mobile applications, IoT, and precision agriculture. India, China, and the United States lead in contributions, with solutions focusing on public-private partnerships, policy, and international collaboration. Enterprises can leverage these insights to strategically invest in advanced mobile strategies integrated with IoT, blockchain, and AI for sustainable, cost-effective agricultural systems, requiring multi-disciplinary expert involvement.
Total Documents Analyzed
Annual Growth Rate
Avg. Citations Per Document
International Co-authorships
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Global Trends
Technological Focus
Challenges & Solutions
Most Productive Countries
India leads with 304 articles (14.8% of total), primarily focusing on domestic research (6.3% multi-country collaborations). China (149 articles, 7.2%) shows strong global engagement (23.5% multi-country collaborations), and the USA (88 articles, 4.3%) boasts the highest percentage of international collaboration (28.4%). Germany demonstrates the highest multi-country collaboration percentage (41.9%) among top contributors.
Yearly Publication Trend
Research output in mobile agriculture has shown a consistent upward trajectory, from 149 publications in 2018 to a peak of 401 in 2023, with 357 in 2024. This trend signifies growing academic and practical interest driven by rapid technological innovations and the urgent need to address global agricultural challenges like food security and climate change.
Highly Cited Papers & Key Technologies
Foundational works include "A Survey on the Role of IoT in Agriculture for Smart Farming" (580 citations) and "IoT and Agriculture Data Analysis for Smart Farm" (578 citations). Unmanned Aerial Vehicles (UAVs) are critical (370 citations). Recent shifts emphasize intelligent systems for plant health monitoring, with papers on "Deep Machine Vision Framework for Plant Stress Phenotyping" and "Deep Convolutional Neural Networks for Crop Disease Classification" receiving significant citations.
Most Frequent Keywords & Thematic Evolution
Key terms are "Agriculture," "Internet of Things," "Mobile applications," "Crops," and "Smartphones." Emerging technologies like "Agricultural robots" and "Deep learning" highlight automation and AI. Thematic analysis shows "IoT," "mobile applications," and "crops" as motor themes (pivotal and well-developed). "Deep learning" and "convolutional neural networks" are emerging themes, indicating a shift towards data-driven, predictive analytics in agriculture.
Obstacles to Mobile Technology Adoption
Farmers face significant barriers: digital literacy gaps, unreliable connectivity in remote areas, and high costs. Additionally, women farmers encounter specific cultural and gender-related barriers to accessing and utilizing mobile technology, contributing to uneven access and hindering widespread adoption.
Identified Solutions & Future Directions
Critical solutions include Public-private partnerships, supportive policy frameworks, and international collaboration. Future research should focus on implementing advanced mobile strategies, integrating them with IoT, blockchain, and Artificial Intelligence for cost-effective environmental and food security solutions. This requires multi-discipline expert involvement and a clear alignment with Sustainable Development Goals (SDGs), along with local adaptation strategies and gender-sensitive policy planning.
2058
Documents published between 2018-2024, highlighting robust research growth in mobile agriculture technology.
Enterprise Process Flow
Topic/Scope Definition
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Keyword & Search String
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Scopus Retrieval (2154 docs)
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Inclusion/Exclusion (2058 docs)
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Bibliometric & Science Mapping Analysis
| Country | Key Strength | Multi-Country Collaboration Rate |
|---|---|---|
| India | Dominant Publication Output (304 articles) | 6.3% |
| China | Strong Global Engagement & Research Volume | 23.5% |
| USA | High International Collaboration & Impact | 28.4% |
Case Study: IoT & Precision Agriculture Integration
The research highlights that the Internet of Things (IoT) and precision agriculture are motor themes, forming the core of innovation in mobile agriculture. IoT enables real-time data acquisition from sensors, facilitating data-driven decision-making for optimal resource management. Precision agriculture leverages these insights, often supported by technologies like Unmanned Aerial Vehicles (UAVs) and deep learning for targeted crop monitoring, pest management, and irrigation. This integration drives significant gains in efficiency, reduces waste, and boosts overall agricultural productivity, addressing global food security challenges.
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Phased Implementation Roadmap
A structured approach to integrating mobile technology and AI into your agricultural enterprise, ensuring successful adoption and measurable impact.
Phase 1: Discovery & Strategy Alignment (Weeks 1-4)
Assess current agricultural processes and technology infrastructure. Identify key pain points, define clear objectives, and establish measurable KPIs for mobile and AI integration. Conduct stakeholder workshops to align on strategy and secure buy-in.
Phase 2: Pilot Program & Data Integration (Months 2-6)
Implement mobile applications and IoT sensors in a controlled pilot area. Focus on data collection (soil moisture, crop health, weather) and integration with existing systems. Train core users and gather feedback for iterative refinement of mobile interfaces and data dashboards.
Phase 3: Scaling & Advanced AI Deployment (Months 7-18)
Expand successful pilot solutions across broader agricultural operations. Deploy advanced AI models for predictive analytics (yield forecasting, disease detection) and automation (irrigation scheduling). Explore blockchain for supply chain transparency and traceability.
Phase 4: Continuous Optimization & Training (Ongoing)
Establish continuous monitoring and feedback loops to optimize mobile and AI solutions. Provide ongoing training and support for farmers and staff to maximize adoption and utilization. Stay abreast of emerging technologies to maintain competitive advantage and drive sustainable growth.
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