The Role of Digital Agriculture in Transforming Rural Areas into Smart Villages

The chapter frames rural development challenges—limited internet and electricity access, inadequate healthcare, education, transportation, and market opportunities—as key barriers to inclusive growth. Global disparities are highlighted (e.g., only 47% of rural homes have internet vs. >80% urban; over 1.3 billion lack electricity; 56% of rural residents lack healthcare vs. 22% in urban). The authors motivate the smart village concept as a participatory, regionally adaptive approach aligned with sustainable development goals. They argue that digitally enabled agriculture can boost productivity, incomes, and service delivery in rural areas and thus catalyze the transformation of rural settlements into smart villages.

The chapter reviews the evolution and interpretations of the smart village concept, citing ENRD’s definition emphasizing participatory, context-specific strategies where digitalization is an enabler rather than an end. Case studies illustrate diverse pathways: personalized elderly care in Poland without advanced telecoms; community crowdsourcing in Vuollerim, Sweden; mixed results of the Millennium Village Project due to contextual mismatches; and strong EU policy momentum (CORK 2.0, EU Action for Smart Villages, Venhorst and Bled declarations, CAP/EAFRD/EAGF support). Global initiatives include climate-smart agriculture training in Sri Lanka (FAO’s Save and Grow), rural tourism programs (Korea, Indonesia, Romania, China), Italy’s National Strategy for Inner Areas, and Finland’s digitization efforts addressing digital competence gaps. The review underscores agriculture’s centrality to rural economies and the transition from Agriculture 2.0 to 4.0, while noting persistent obstacles: weather risks, water scarcity, diseases, market volatility, aging farmers, limited connectivity, and low digital literacy. Precision agriculture is presented as a key smart village pillar, drawing on IoT, AI/ML, bio/nano, drones, and blockchain, contingent on infrastructure and skills.

This is a conceptual and synthesis-based chapter proposing a smart village model centered on digital agriculture applications in four domains: (1) precise irrigation, (2) detecting and controlling crop diseases and pests, (3) soil mapping/fertility analysis and fertilization, and (4) weed management. The methodology involves reviewing enabling technologies (IoT sensor networks, WSNs, edge/cloud computing, GSM/LoRa/WiFi communications, AI/ML, machine vision, UAVs, GIS/GPS, remote/proximal sensing) and design patterns (e.g., four-layer IoT architecture—devices, communication, services, application; irrigation scheduling based on soil-, weather-, and plant-based indicators; decision support systems for variable rate nutrient application). The model integrates renewable energy options (solar/wind pumps) for remote irrigation, plant/soil sensing for decision automation, and targeted actuation (smart sprayers, variable-rate equipment, robotics) to optimize inputs. Figures (not reproduced) depict the proposed smart village methodology and a digital soil mapping framework.

- Smart irrigation and scheduling systems using low-cost WSNs can substantially improve resource efficiency: in one evaluation, water use decreased by 59.61%, electricity consumption by 67.35%, with crop yield increasing by 22.58% compared to conventional irrigation [47]. - Intelligent, laser-guided sprayers reduced pesticide usage in fruit crops by: Apple 58.7%, Peach 30.6%, Blueberry 47.9%, Black raspberry 52.5% [54; Table 1]. - Potential global loss of crop yield to pests is approximately 18%, highlighting the value of early detection and targeted control [48]. - Hyperspectral imaging-based weed identification achieved high accuracies (tomato 95%, black nightshade 94%, pigweed 99%), demonstrating feasibility of spectral approaches for site-specific weed management [69]. - IoT-enabled precision irrigation (soil moisture, leaf/plant stress, weather forecasts) and long-range communications (GSM, LoRa) support deployment in remote areas, reducing over-/under-irrigation and labor. - AI/ML for pest identification (e.g., transfer learning for jute pests), IoT smart traps with machine vision, and drone-based detection/spraying workflows enable earlier, more precise interventions while lowering chemical inputs and human exposure [49–55]. - Digital soil mapping with optical, electrical/electromagnetic, electrochemical, and mechanical sensors, combined with GIS/GPS and ML/statistical models, supports management zone delineation and variable rate nutrient application to improve yield and reduce environmental impacts [57–63]. - Robotics and automation (laser weeding, electro- or thermal-based weed control, centimeter-accurate micro-dosing sprayers) can reduce chemical dependence, labor, and operational costs, particularly in row crops [70–73].

The synthesis indicates that digital agriculture can operationalize the smart village concept by transforming core rural production systems. Data-driven irrigation and fertilization enhance input-use efficiency, conserve water and energy, and raise yields—benefits critical where resources and infrastructure are constrained. Early pest and disease detection via sensing and ML reduces blanket pesticide application, improving environmental and human health outcomes while cutting costs. Weed management innovations—from vision-guided variable-rate spraying to autonomous mechanical/laser solutions—limit herbicide use and labor demands. Integrating renewable energy with IoT mitigates unreliable grid access in remote regions. However, effective implementation hinges on participatory, context-specific design that accounts for local socio-economic patterns, skills, and infrastructure, echoing broader smart village lessons that digitalization is an enabling tool within a holistic rural development strategy.

Digital agriculture is a key driver for transforming rural areas into smart villages by improving access to information and tools, raising productivity, optimizing resource use (soil, water, energy, labor), and fostering economic growth. The chapter outlines a conceptual smart village model built on four digital agriculture pillars—precise irrigation, pest/disease management, soil fertility mapping and variable-rate fertilization, and weed management—implemented with IoT, AI/ML, remote sensing, GIS, and automation. These technologies can increase yields, reduce costs and environmental impacts, and create conditions attractive to investment and new businesses. Continued advancement and deployment tailored to local contexts can accelerate rural revitalization.

The chapter highlights several constraints that can limit impact and scalability: heterogeneous rural contexts preclude one-size-fits-all designs; insufficient internet connectivity and energy access; high upfront costs for sensors/equipment; gaps in digital skills and literacy (especially among older farmers); and sociocultural resistance to new practices. Technical challenges include limited labeled datasets for ML (weed/pest detection), environmental variability (lighting, occlusion) affecting vision systems, coverage/latency trade-offs in communications, satellite image resolution limits vs. UAV coverage, and early-stage maturity or row-crop bias in many robotic weeding solutions. As a conceptual synthesis, the work compiles evidence from prior studies rather than presenting primary empirical evaluation of a unified field deployment.