IoT and sensor networks for smart agriculture: Technologies, applications, and future directions

Introduction

In recent decades, agriculture has undergone significant transformation driven by rapid advancements in digital technologies. Traditional farming practices, though effective at small scales, face increasing challenges from climate variability, water scarcity, soil degradation, declining arable land, and rising food demand. These pressures necessitate sustainable and efficient solutions, leading to the emergence of smart agriculture based on the integration of the Internet of Things (IoT) and sensor-based technologies. IoT enables real-time monitoring, data-driven decision-making, and automation in farming operations. Precision agriculture, a key application, optimizes inputs such as water, fertilizers, and pesticides, improving productivity while reducing environmental impacts. Soil-based NPK sensors are particularly important for assessing soil fertility and enabling precise nutrient management (Kumar et al., 2025) [8] . IoT systems typically consist of interconnected devices that monitor parameters such as soil moisture, temperature, humidity, and crop health, supported by cloud computing and data analytics for predictive decision-making.Wireless sensor networks (WSNs) further enhance these capabilities by providing distributed, real-time environmental monitoring through sensor nodes with sensing, processing, and communication functions (Jawad et al., 2017) [5] . These systems support precision farming by enabling optimized irrigation, nutrient management, and early detection of pests and diseases, thereby improving sustainability.

Unlike conventional farming methods that rely on uniform input application and manual practices, smart agriculture integrates IoT, artificial intelligence (AI), and data analytics to improve efficiency and scalability (Dhanaraju et al., 2022) [2] . AI-based systems further enhance predictive capabilities for crop yield, disease detection, and resource management (Kahar et al., 2024) [6] . IoT applications also extend to aquaculture and greenhouse management, optimizing environmental conditions and resource use (Liu et al., 2025) [9] . Despite these benefits, challenges such as high costs, energy constraints, data security, interoperability issues, and limited technical expertise persist. This review provides an overview of IoT and WSN applications in agriculture, highlighting advancements, challenges, and future research directions for sustainable and intelligent farming systems.

Fundamentals of IoT in Agriculture

Agriculture has traditionally relied on manual observation, empirical knowledge, and experience-driven decision-making to manage crops and livestock. While such approaches have sustained agricultural production for centuries, they are increasingly inadequate in addressing the complexities of modern agriculture. Rapid population growth, climate variability, declining natural resources, and the need for higher productivity have necessitated the adoption of advanced technological solutions. In this context, the Internet of Things (IoT) has emerged as a transformative technology that enables data-driven and automated agricultural practices.IoT in agriculture refers to a network of interconnected physical devices embedded with sensors, actuators, communication modules, and processing units that continuously monitor environmental and operational parameters. These devices collect real-time data related to soil conditions, weather parameters, crop health, and livestock status, which are subsequently transmitted to cloud or edge computing platforms for storage, analysis, and decision-making. The integration of IoT technologies allows farmers to gain actionable insights, optimize resource utilization, and enhance productivity while minimizing environmental impacts.

Furthermore, the incorporation of artificial intelligence (AI) and machine learning (ML) techniques into IoT systems facilitates predictive analytics, anomaly detection, and automation of agricultural operations (Kahar et al., 2024; Jawad et al., 2017; Navarro et al., 2020; Alahmad et al., 2023) [6,1,11,5] .

Architecture of Agricultural IoT Systems

IoT-based agricultural systems are generally structured into a multi-layered architecture that ensures efficient data acquisition, transmission, processing, and application. The most widely adopted framework consists of three primary layers: the perception layer, network layer, and application layer. Each layer plays a critical role in enabling seamless integration of sensing, communication, and intelligent decision-making processes. The perception layer, also known as the sensing layer, forms the physical interface between the agricultural environment and the digital system. It comprises a wide range of sensors deployed across the field to capture real-time data on soil moisture, temperature, humidity, light intensity, pH levels, and crop health indicators. Advanced sensing technologies, such as capacitive soil moisture sensors, multispectral and hyperspectral imaging sensors, and biosensors for pathogen detection, have significantly enhanced the accuracy and reliability of data collection. The network layer is responsible for transmitting the collected data from sensor nodes to centralized processing systems. This layer utilizes various wireless communication protocols, including ZigBee, LoRaWAN, NB-IoT, Wi-Fi, and cellular networks, depending on the application requirements, transmission range, and power constraints. Low-power widearea network (LPWAN) technologies, particularly LoRaWAN and NB-IoT, are widely preferred in agricultural applications due to their ability to support long-range communication with minimal energy consumption. The application layer represents the intelligence of the IoT system, where data is processed, analyzed, and transformed into actionable insights. This layer incorporates cloud computing platforms, edge computing devices, and AI-based analytics tools to perform tasks such as predictive modeling, anomaly detection, and decision support. [22] ; Zhang et al., 2024 [28] ; Duguma & Bai, 2024 [30] Network Layer Data transmission and connectivity ZigBee, LoRaWAN, NB-IoT, Wi-Fi, 4G/5G, satellite Low-power, long-range, energy-efficient communication

Remote monitoring, alerts, smart irrigation

Khan et al., 2024 [29] ; Nawaz & Babar, 2025 [31] ; Cisse et al., 2024 [24] Application Layer Data processing and decision-making Cloud, edge computing, AI/ML, big data, dashboards Predictive analytics, automation, DSS, digital twins Precision irrigation, yield prediction, livestock monitoring Kumar et al., 2024 [22] ; Zhang et al., 2024 [28] ; Khan et al., 2024 [29] Integration Layer Interoperability and data integration APIs, IoT platforms, middleware, blockchain Secure sharing, traceability, smart contracts Supply chain tracking, farm-tomarket traceability Li et al., 2021 [26] ; Al-Najadi et al., 2025 [27] ; Seetaram et al., 2024 [25] Control Layer Automated actuation and control

Actuators, valves, drones, robotics

Autonomous control, adaptive systems

Smart irrigation, fertilization, greenhouse automation

Li et al., 2021 [26] ; Al-Najadi et al., 2025 [27] ; Olugbemi & Ola, 2022 [22] 2.

Applications of IoT in Agriculture

The application of IoT in agriculture spans a wide range of domains, transforming conventional farming into a highly efficient and technology-driven system. One of the most prominent applications is precision agriculture, which involves the use of sensor data and analytics to optimize the application of inputs such as water, fertilizers, and pesticides. By considering spatial and temporal variability within fields, precision agriculture enhances resource efficiency and crop productivity. Another important application is climate-smart agriculture, where IoT systems are used to monitor microclimatic conditions and predict weather-related risks. These systems enable farmers to take proactive measures against adverse climatic events such as droughts, floods, and temperature extremes, thereby improving resilience to climate change. IoT technologies are also widely used in livestock monitoring, where wearable sensors and tracking devices are employed to monitor animal health, behavior, and location. This enables early detection of diseases, improves animal welfare, and enhances productivity in livestock farming systems.

In the domain of post-harvest management, IoT systems play a crucial role in reducing losses by monitoring storage conditions such as temperature, humidity, and gas composition. Smart storage systems ensure optimal conditions for preserving agricultural produce, thereby extending shelf life and reducing spoilage. Furthermore, greenhouse automation represents a significant advancement in controlled environment agriculture.

IoT-enabled systems continuously monitor and regulate environmental parameters such as temperature, humidity, light intensity, and CO₂ levels to maintain optimal growing conditions. Automated control systems adjust these parameters in real time, ensuring consistent crop growth and higher yields.

Overall, the integration of IoT technologies across these applications demonstrates their transformative potential in enhancing agricultural productivity, sustainability, and resilience. As technological advancements continue, IoT is expected to play an increasingly vital role in addressing global food security challenges and promoting smart farming practices.

Sensor Networks in Agriculture

Wireless Sensor Networks (WSNs) constitute a fundamental component of IoT-based agricultural systems, enabling continuous, real-time, and spatially distributed monitoring of environmental and crop-related parameters. These networks consist of interconnected sensor nodes deployed across agricultural fields to collect high-resolution data on soil conditions, microclimate variations, crop health, and livestock status. By facilitating data-driven decision-making, WSNs play a crucial role in advancing precision agriculture, optimizing resource utilization, and improving overall farm productivity.

The integration of WSNs into agricultural systems allows for automated data acquisition and transmission through energyefficient wireless communication protocols. These sensor nodes, often equipped with low-power microcontrollers and radio communication modules, can operate in harsh environmental conditions and remote locations. The data collected by WSNs is transmitted to centralized systems or cloud platforms for processing and analysis, enabling farmers to respond proactively to changing field conditions. As a result, WSNs significantly enhance the efficiency, sustainability, and resilience of modern agricultural practices.

Types of Sensors in Agriculture

A wide variety of sensors are employed in agricultural WSNs to monitor physical, chemical, and biological parameters critical for precision farming. These sensors form the backbone of data acquisition systems and directly influence the accuracy and reliability of IoT-based agricultural solutions. Recent advancements in sensor technologies, including nanotechnology and spectroscopy, have significantly improved sensing precision, sensitivity, and real-time monitoring capabilities (Sahu & Victor, 2025) [15] .Modern smart agriculture systems increasingly rely on integrated sensing platforms capable of multi-parameter detection, enabling comprehensive field-level insights and improved decision-making (Musa et al., 2023) [16] .

Early developments in agricultural sensing primarily focused on environmental monitoring; however, recent innovations have expanded capabilities to include biochemical sensing, pathogen detection, and crop phenotyping (Ruiz-Garcia et al., 2009) [19] .

Advanced sensing techniques such as hyperspectral imaging and optical sensing enable non-destructive crop health assessment and early stress detection (S & Baulkani, 2022) [17] . Similarly, nanomaterial-based biosensors have enhanced the ability to detect plant pathogens at early stages with high sensitivity (Rahaman & Azharuddin, 2022) [18] . The integration of intelligent sensing with data analytics further supports precision agriculture practices and improves farm-level decision-making (Pandiyaraju et al., 2023) [20] . In addition, wearable and embedded sensing technologies have significantly improved livestock monitoring systems by enabling continuous tracking and predictive health analysis (Yinjun, 2024) [21] .

Sensor Network Topologies and Communication Protocols

The efficiency and reliability of WSNs in agriculture largely depend on the network topology and communication protocols employed. Network topology determines how sensor nodes are interconnected and how data is transmitted within the system. The selection of an appropriate topology is influenced by factors such as farm size, terrain, energy constraints, and required data reliability. In a star topology, all sensor nodes communicate directly with a central gateway or base station. This configuration is simple to implement and requires minimal coordination among nodes; however, it is limited by communication range and lacks redundancy. Failure of the central node can disrupt the entire network. In contrast, a mesh topology allows sensor nodes to communicate with multiple neighboring nodes, creating a decentralized and self-healing network. This topology enhances reliability and coverage, as data can be rerouted through alternative paths in case of node failure. Mesh networks are particularly suitable for large and heterogeneous agricultural environments. A hybrid topology, which combines elements of both star and mesh configurations, offers a balance between simplicity and reliability.

In addition to topology, communication protocols play a critical role in determining the performance of WSNs. ZigBee is commonly used for short-range, low-power communication in small-scale farms and greenhouse environments. LoRaWAN has gained significant popularity due to its long-range communication capabilities and low energy consumption, making it ideal for large and remote agricultural fields. NB-IoT, a cellular-based technology, provides wide-area connectivity with low bandwidth requirements and is suitable for large-scale deployments. Meanwhile, Wi-Fi and Bluetooth are primarily used in localized environments, such as greenhouses and farm buildings, where high data rates are required. The choice of communication technology involves trade-offs between range, power consumption, data rate, and cost. Therefore, selecting an appropriate combination of topology and communication protocol is essential for designing efficient and scalable agricultural WSNs. [8] ; Dhanaraju et al., 2022 [2] Soil pH Sensor Soil acidity/alkalinity Electrochemical probes, ion-selective electrodes

Continuous monitoring, IoT integration

Fertilizer optimization, soil health Alahmad et al., 2023 [1] ; Iluno & Nwaogwugwu, 2025 [4] Soil Nutrient Sensor N, P, K levels Ion-selective, optical sensing

Real-time nutrient profiling

Precision fertilization, yield optimization Kumar et al., 2025 [8] ; Alahmad et al., 2023 [1] Temperature &

Humidity Sensor Climatic conditions Thermistors, capacitive sensors

Compact, real-time tracking

Climate monitoring, greenhouse automation Navarro et al., 2020 [11] ; Liu et al., 2025 [9] Light Sensor (PAR) Photosynthetically active radiation

Photodiodes, quantum sensors

Accurate measurement, calibration

Crop growth monitoring

Liu et al., 2025 [9] ; Alahmad et al., 2023 [1] Crop Health Sensor Chlorophyll, reflectance

Multispectral, hyperspectral imaging

Early stress detection, non-invasive

Disease detection, nutrient deficiency

Liu et al., 2025 [9] ; Navarro et al., 2020 [11] Biosensors Nwaogwugwu, 2025 [4] Livestock Sensors Activity, temperature, location RFID, GPS, wearables Real-time tracking, AI detection

Animal health monitoring

Navarro et al., 2020 [11] ; [9] ; Dhanaraju et al., 2022 [2] Water Quality Sensors pH, turbidity, DO Optical, electrochemical Real-time alerts Irrigation, aquaculture Liu et al., 2025 [9] ; Kumar et al., 2025 [8]

Applications of Sensor Networks in Agriculture

Sensor networks have enabled a wide range of applications in agriculture by providing continuous and accurate data for decision-making. One of the most significant applications is in smart irrigation systems, where soil moisture sensors, weather data, and evapotranspiration models are integrated to optimize water usage. Such systems can reduce water consumption by up to 30-40% while maintaining or improving crop yield, thereby contributing to sustainable water management. Another critical application is disease detection and crop health monitoring. Advanced optical sensors, multispectral imaging systems, and biosensors can identify early signs of plant stress, nutrient deficiencies, and pathogen infections. Early detection allows for timely intervention, reducing crop losses and minimizing the use of chemical pesticides. Environmental monitoring is also a key application of WSNs, where sensors continuously measure parameters such as temperature, humidity, solar radiation, and wind speed. This information is used to optimize microclimatic conditions, particularly in controlled environments such as greenhouses, thereby enhancing crop growth and productivity.

In precision fertilization, nutrient sensors provide real-time information on soil nutrient levels, enabling site-specific fertilizer application. This approach reduces fertilizer wastage, lowers production costs, and minimizes environmental pollution caused by nutrient runoff. Furthermore, WSNs play a vital role in livestock health management, where wearable sensors track animal behavior, feeding patterns, movement, and physiological parameters such as body temperature. The integration of these data with AI-based analytics enables predictive healthcare, early disease detection, and improved animal welfare, ultimately enhancing productivity in livestock farming systems. Overall, the application of sensor networks in agriculture demonstrates their transformative impact on modern farming practices. By enabling real-time monitoring, predictive analytics, and automated control, WSNs contribute significantly to the development of efficient, sustainable, and intelligent agricultural systems.

IoT-Enabled Agricultural Applications

The [22,24,23] . [22,24] . [24,23] . [25] ; Li et al., 2021 [26] ; Al-Najadi et al., 2025) [27] .

Crop Health Monitoring and Disease Management

Livestock Monitoring and Management

Livestock farming is an essential component of agricultural systems, requiring continuous monitoring of animal health, behavior, and environmental conditions. Traditional livestock management practices are labor-intensive and lack real-time monitoring capabilities, which can result in delayed disease detection and reduced productivity. IoT-based livestock monitoring systems address these challenges by integrating wearable sensors, GPS tracking technologies, and wireless communication networks. These systems enable continuous tracking of physiological parameters such as body temperature, heart rate, and activity levels, as well as behavioral patterns related to feeding and movement. Data collected from these sensors is analyzed using AI and machine learning algorithms to detect anomalies, predict diseases, and optimize livestock management practices. GPS-enabled devices also facilitate realtime location tracking, which is particularly useful for grazing management and theft prevention. In the Indian agricultural context, IoT-based dairy management systems have been increasingly adopted in regions such as Punjab and Haryana, where wearable sensors are used to monitor milk-producing cattle. These technologies have demonstrated improvements in milk yield, reproductive efficiency, and overall animal health, while reducing veterinary costs (Kumar et al., 2024) [22] .

Supply Chain and Post-Harvest Management

Post-harvest losses remain a significant challenge in agricultural systems, particularly in developing countries, where inadequate storage and inefficient supply chain infrastructure lead to substantial economic losses. IoT technologies provide effective solutions by enabling real-time monitoring and control of storage and transportation conditions. IoT-based systems utilize sensors to monitor temperature, humidity, moisture content, and gas composition (such as ethylene) throughout the supply chain. These systems generate real-time alerts when environmental conditions deviate from optimal thresholds, enabling timely corrective actions. Additionally, the integration of IoT with blockchain technology enhances traceability, transparency, and accountability in agricultural supply chains. The adoption of IoT-enabled post-harvest management systems has been shown to reduce losses by 20-30%, improve product quality, and ensure compliance with food safety standards. These advancements significantly enhance supply chain efficiency and market competitiveness (Olugbemi & Ola, 2022; Cisse et al., 2024) [24,23] . [25] Transportation GPS, temperature sensors Location, temperature tracking

IoT-enabled logistics platforms

Real-time monitoring Cold chain optimization Li et al., 2021 [26] Ripening Control Ethylene gas sensors Ripening stages AI-based prediction models Smart packaging systems Reduced wastage Al-Najadi et al., 2025 [27] Quality Assurance pH, moisture sensors Product quality indicators

DSS and cloud platforms

Automated quality control Improved product safety Seetaram et al., 2024 [25] Traceability IoT + blockchain End-to-end tracking

Distributed ledger systems

Transparent supply chain

Enhanced trust and export quality Li et al., 2021 [26] Market Integration IoT platforms, mobile apps

Demand and supply data Big data analytics Smart market linkage Better price realization Al-Najadi et al., 2025 [27]

Recent Trends in IoT and Sensor Networks in Agriculture

The rapid advancement of Internet of Things (IoT) technologies and wireless sensor networks (WSNs) has significantly accelerated the transformation of modern agriculture into a datadriven and intelligent system. Recent innovations in artificial intelligence (AI), edge computing, low-power embedded systems, and advanced communication protocols have enabled efficient monitoring, automation, and optimization of agricultural operations. These developments allow farmers to continuously monitor environmental and crop conditions, predict potential risks, and implement timely interventions.

Consequently, IoT-enabled agriculture is emerging as a key solution to address global challenges such as food security, climate variability, and resource scarcity (Kumar et al., 2024; Zhang et al., 2024) [28,22] .accelerated the adoption of smart farming technologies by promoting digital infrastructure and providing financial support to farmers. Empirical studies indicate that IoT-enabled agricultural systems in India can reduce input costs by 20-40% and increase crop yields by 15-30%, demonstrating their transformative potential (Kumar et al., 2024) [22] .

Data Analytics and Decision Support in Smart Agriculture

The rapid adoption of Internet of Things (IoT) technologies in agriculture has resulted in the generation of massive volumes of heterogeneous data from sensors monitoring soil properties, crop conditions, environmental parameters, livestock health, and post-harvest systems. However, raw data alone does not provide actionable insights unless it is processed, analyzed, and interpreted effectively. The integration of big data analytics, machine learning (ML), and cloud computing has significantly improved the ability of agricultural systems to respond dynamically to changing environmental and operational conditions (Wolfert et al., 2017; Liakos et al., 2018) [32,33] .

Big Data in Agriculture

Modern agricultural systems equipped with IoT devices generate large-scale and diverse datasets characterized by high volume, variety, velocity, and veracity. These datasets include soil moisture levels, pH values, nutrient concentrations, temperature, humidity, crop growth indicators, livestock health metrics, and storage conditions. The sheer volume of data generated daily from sensor networks presents challenges in terms of storage, processing, and analysis. Additionally, the variety of data sources, including structured sensor data, unstructured images, and textual information, requires advanced data integration techniques. Real-time data streams further increase complexity, as timely processing is essential for decision-making in critical operations such as irrigation and disease control In countries such as India, large-scale farms and agri-tech platforms increasingly rely on cloud-based infrastructures to aggregate and analyze agricultural data across regions, enabling both farmlevel and regional decision-making. The application of big data analytics in agriculture has been recognized as a key driver for improving efficiency and sustainability in modern farming systems (Wolfert et al., 2017) [32] . [33,34] .

Machine Learning Models

Decision Support Systems (DSS)

DSS integrate sensor data and analytics to provide recommendations for irrigation, fertilization, and pest management (Rose et al., 2016) [35] .

Data Visualization and Interaction

Dashboards, heat maps, and time-series tools enable intuitive data interpretation and real-time monitoring (Wolfert et al., 2017) [32] .

Predictive Analytics

Predictive models forecast crop yield, pest outbreaks, and irrigation needs, reducing uncertainty (Kamilaris & Prenafeta-Boldú, 2018) [34] .

Integrated Systems:

The integration of IoT, ML, and cloud computing supports endto-end agricultural optimization, including post-harvest quality assessment (Kahar et al., 2025; Wolfert et al., 2017) [32,6] . [1] ; Liu et al., 2025 [9] Data [5] ; Navarro et al., 2020 [11] Data [1] ; Liu et al., 2025 [9] 6.7 Challenges in Agricultural Data Analytics Despite the significant potential of data-driven agriculture, several challenges limit the effective utilization of IoT-generated data. Agricultural datasets are often characterized by high volume, heterogeneity, and real-time streaming requirements, which complicate data storage, integration, and processing.

Ensuring data quality and reliability remains a critical concern, as sensor inaccuracies, calibration drift, and environmental interference can lead to erroneous inputs and unreliable analytical outcomes. Additionally, limited computational infrastructure and connectivity constraints in rural regions restrict the deployment of advanced analytics frameworks, particularly those dependent on cloud-based processing. Data privacy and security also pose significant risks, as sensitive farm-level information requires robust protection mechanisms to prevent unauthorized access and misuse (Rahu et al., 2022; Alahmad et al., 2023) [12,1] .

Challenges and Limitations of IoT in Agriculture

The implementation of IoT-based agricultural systems faces a range of technical, economic, social, and regulatory challenges that hinder large-scale adoption. Technically, issues such as sensor inaccuracies, power constraints, and unreliable network connectivity in remote areas affect system performance and data continuity. [12,6,2] .

Conclusion

The last ten years have witnessed the development of Internet of Things (IoT) technology and sensor networks in the agricultural industry as a theoretical construct to a working implementation. The combination of low-cost sensors, wireless communication, cloud-based computing, and data analytics has made it possible to monitor precise, automated control of applications in areas of irrigation management, crop health management, greenhouse automation, livestock surveillance, and supply chain management. These practices have shown great efficiency of the resources, such as less water consumption, improved application of fertiliizers, less demand of labor, and healthy crop growth. Empirical evidence, especially in India and other locations, reports increment of yield of 10-30% and reduction in costs of 15-40%, and also enhanced environmental sustainability due to precise interventions. The integration of multi-sensor data also allows the pests, nutrient deficiency and water stress to be detected early. Nevertheless, its use is still limited due to barriers like low rural connectivity, expensive installation, standardisation, interoperability and issues related to the privacy of data. Also, a lack of digital literacy of farmers limits the use of decision support systems, even with the continued interest by ICAR and agricultural institutions. The hybrid connectivity models, new solutions, in which LPWAN can be used to complement 5G, semantic data frameworks to support interoperability, and new technologies, including blockchain to support supply chain visibility, edge computing to support realtime analytics, and federated learning to support privacypreserving data processing, are emerging solutions. All in all, smart and interconnected systems that connect sensing, analytics, and communication technologies are the future of agriculture. It will involve collaborations, standardization, and favorable policies that would facilitate sustainable, climateresilient, and high-productivity agricultural systems.

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