Rural areas globally share common problems: inadequate healthcare, education, living standards, wages, and market access. Smart villages, a concept gaining traction, aim to address these issues. Digital agriculture plays a crucial role in this transformation, significantly impacting traditional farming and rural economies by boosting agricultural output. This chapter explores current rural challenges and the impact of smart village initiatives. It presents a smart village model based on recent examples demonstrating how emerging digital agriculture trends enhance agricultural production. A significant digital divide exists between rural and urban areas, with only 47% of rural homes having internet access compared to over 80% in urban areas (EU Rural Review [1]). Furthermore, over 1.3 billion people lack access to electricity globally [2], leading to deficiencies in healthcare, transportation, education, and economic structures [3]. This creates a vicious cycle of rural decline [4]. Inclusive digital transformation through adaptive, resident-oriented, knowledge-based policies focusing on sustainable growth is crucial to address this and improve quality of life [34]. The smart village concept offers a promising approach, aligned with common agricultural policy (CAP) remodeling [4]. Rurality is a multifaceted concept, encompassing demographic, material, and symbolic aspects [78, 79]. While often viewed as a receding space in the context of modernity's urban focus, rural areas face numerous challenges across healthcare, education, and market access.

The paper reviews existing smart village models, starting with the European Network for Rural Development (ENRD) definition, emphasizing innovative solutions, local strengths, participatory approaches, and digital technologies to improve economic, social, and environmental conditions [6, 7, 8]. However, technology isn't the sole component; the overall policy context aligns with the UN's Sustainable Development Goals (SDGs) [9]. Examples include Poland's personalized healthcare initiative and Sweden's crowdsourcing approach for local development [10, 11]. The review also analyzes the Millennium Village Project (MVP), highlighting successes and failures across different regions, emphasizing the need for context-specific approaches [12, 13, 14]. The smart village concept gained momentum in the EU in 2016 and 2017, involving policy initiatives, thematic groups, and collaborations [9, 17, 18, 19, 20]. The review presents case studies from various countries, including Sri Lanka's climate-smart agriculture (CSA) initiatives [22, 23, 24], South Korea's rural tourism model [25], Italy's National Strategy for Inner Areas [29], and Finland's focus on digital infrastructure and competence [30]. The EU's agricultural sector's importance in the global economy and the need for innovation in precision agriculture (Agriculture 4.0) are discussed, emphasizing the challenges and opportunities for digital transformation within the agricultural sector [19, 31, 32, 33].

The paper proposes a smart village model focusing on digital agriculture applications. It does not detail a specific research methodology used to gather data or conduct experiments but rather presents a conceptual framework and case studies. The authors' methodology involves analyzing existing literature, drawing on examples of digital agriculture implementations in various contexts, and synthesizing this information to propose a model for smart villages. They explore the contribution of digital agriculture in four key areas: precise irrigation, pest and crop disease detection, soil mapping and fertility analysis, and weed management. The information is presented with a descriptive approach, leveraging numerous citations and examples from the available literature to support the discussion. There is no quantitative data collection or analysis performed in the study itself; rather, the paper uses available data points and statistics from secondary sources to illustrate its points.

The paper identifies several key areas where digital agriculture contributes significantly to creating smart villages: **Precise Irrigation:** Smart irrigation systems, using IoT, sensors, and data analytics, optimize water usage, conserve resources, and improve crop yields. These systems can automate water supply based on real-time data on soil moisture, weather, and crop health. Various technologies, including low-cost sensors, different wireless communication methods (Wi-Fi, GSM, LoRa), and irrigation scheduling tools, are discussed. The integration of renewable energy sources, such as solar and wind power, is also highlighted as a means to reduce energy consumption and operational costs. Studies show significant reductions in water and energy usage, along with yield increases, compared to traditional methods [47]. **Pest and Crop Disease Detection:** Digital agriculture offers advanced methods for early detection of pests and diseases, minimizing crop losses and reducing the need for excessive pesticide use. The paper discusses image processing techniques, smart traps, and AI-powered diagnostic tools based on smartphone applications. The use of drones for smart spraying is also examined, showing significant reductions in pesticide usage compared to conventional methods [54, 55]. Remote sensing techniques using visible and near-infrared (VIS-NIR) spectroscopy and thermal infrared sensors are also explored. **Soil Mapping, Fertility Analysis, and Fertilization:** Digital soil mapping, utilizing sensors, GIS, GPS, and remote sensing, allows for precise analysis of soil fertility and informed decision-making regarding fertilizer application. Various sensor types (optical, electromagnetic/electrical, electrochemical, mechanical) and their applications are discussed. IoT-based systems for soil analysis are also explored, facilitating rapid and accurate testing, especially beneficial in remote areas. Variable rate fertilization (VRNA), adapting fertilizer application based on soil conditions, is emphasized as a key aspect of precision agriculture, improving efficiency and reducing environmental impact. **Weed Management:** Smart weed management involves using technologies such as smart sprayers, incorporating machine vision and AI for precise weed identification and targeted herbicide application. This approach minimizes chemical usage, improves efficiency, and reduces environmental impact. Remote sensing, using satellite and UAV imagery with multispectral and hyperspectral sensors, provides large-scale weed mapping capabilities. The paper also discusses the increasing role of robotic weeders, both commercially available and under development, showcasing automated weeding systems that reduce the need for manual labor and herbicides.

The paper's findings demonstrate the significant potential of digital agriculture in transforming rural areas into thriving smart villages. The integration of advanced technologies across various aspects of agriculture leads to improved resource efficiency, increased productivity, and enhanced sustainability. By providing farmers with data-driven insights and tools for precision agriculture, digital agriculture empowers them to make better decisions, optimizing resource use and minimizing environmental impact. This not only increases their income but also improves the overall quality of life in rural communities by addressing challenges in healthcare, education, and economic development. The adoption of these technologies requires addressing limitations in digital infrastructure and literacy, particularly in developing countries. The success of smart village initiatives depends on a multi-faceted approach, integrating technological advancements with effective policy frameworks, community participation, and sustainable development goals.

Digital agriculture is a crucial component of the smart village movement, offering solutions to persistent challenges in rural areas. The four key areas highlighted—precise irrigation, pest and disease detection, soil fertility analysis, and weed management—demonstrate the transformative potential of technology-driven approaches. Future research should focus on developing cost-effective and user-friendly technologies, addressing digital literacy gaps, and creating supportive policy environments to facilitate widespread adoption of these advancements. The integration of digital agriculture with other smart village initiatives is crucial for holistic and sustainable rural development.

The paper primarily presents a conceptual framework and relies heavily on existing literature and case studies. It does not conduct original research with its own data collection and analysis. The applicability and effectiveness of the proposed smart village model may vary depending on specific geographical contexts, socio-economic conditions, and the availability of resources. The paper also focuses more on technological aspects and may not sufficiently address all the socio-economic and political factors that influence the success of smart village initiatives.