A sensor is a device or module that detects physical events or changes and transmits the data to a processor for analysis
(Paul et al., 2022). It converts phenomena like light, temperature, or pressure into measurable signals, typically digital
(Javaid et al., 2021). As shown in Fig 1, a sensor comprises a sensing unit, conversion unit and output unit (
NMAB, 1995). The sensing unit detects the parameter, the conversion unit transforms it into an electrical signal and the output unit processes and transmits it, possibly including amplification or analog-to-digital conversion.
Advancements in sensors have improved performance, reliability and miniaturization across fields such as medicine, engineering and agriculture
(Zhang et al., 2023b). Recent developments include AI and ML integration (El
Khediri et al., 2024), low-power sensors, edge computing for real-time processing
(Oliveira et al., 2024) and wireless communication
via Bluetooth, Wi-Fi and LoRaWAN
(Ketshabetswe et al., 2019). These advances, along with lower costs, have increased accessibility and ease of deployment.
Sensors are central to IoT systems and are widely used in agriculture, healthcare, transportation and smart cities
(Poongodi et al., 2020; Chataut et al., 2023; Gubbi et al., 2013). They enable real-time data collection, supporting productivity and sustainability in agriculture by optimizing fertilizer, pesticide and water use through monitoring soil and crop conditions (
Roper, 2021). They also help mitigate environmental stress and pest outbreaks, improving yields and reducing losses
(Rajak et al., 2023).
Sensor performance in agriculture is defined by static and dynamic characteristics. Static parameters include accuracy, resolution, sensitivity, linearity and drift (
Vyas and Thakur, 2020;
NMAB, 1995). Dynamic characteristics describe time-based responses-zero-order systems respond instantly, first-order systems approach output gradually and second-order systems may oscillate before stabilizing (
Selvolini and Marrazza, 2023). Sensors may be active or passive, depending on whether they require external input.
Understanding these characteristics is key to selecting appropriate sensors for IoT applications
(Patel et al., 2020). Classification of sensors-by measured parameters (
e.
g., temperature, pressure), operating principles (
e.
g., resistive, capacitive), or application domain-supports optimal selection, performance benchmarking, integration and scalability. It also provides a structured framework for researchers to explore sensor functionality and trade-offs, enhancing innovation and usability.
Classification of sensors used in agriculture
Sensors in agriculture are categorized by working principle, energy requirements, materials, detection methods and applications (
Sajja and Bhowmik, 2023). Key types include resistive, proximity, inductive, optical, electric and biosensors. Strain gauges, a type of resistive sensor, alter resistance under mechanical stress and are used to assess structural integrity of agricultural buildings (
Yoder and Adams, 2022). Capacitance proximity sensors evaluate harvest yields, while inductive proximity sensors detect ferromagnetic materials in soil
(Moheimani et al., 2022). Optical sensors, such as photodetectors, convert light into electrical signals, enabling calculation of vegetation indices for assessing crop vigor and growth dynamics
(Candiago et al., 2015). Devices like SPAD and ClorofiLOG require direct leaf contact for measurements
(Jurisic et al., 2021). Electrical sensors measure soil properties like resistance and capacitance; for instance, soil electrical conductivity sensors assess field variability, potentially replacing detailed lab analyses (
Visconti and de Paz, 2016). Electric and electromagnetic soil sensors are valued for rapid response, low cost and durability
(Gupta et al., 2019).
Biosensors detect toxins and pathogens, aiding in continuous crop monitoring and early disease detection
(Wang et al., 2022). Immunosensors, utilizing antibodies or antigens, rapidly detect pesticide residues (
Jeevula and Sireesha, 2021). Chemical sensors measure properties like NPK levels, pH and gas concentrations, ensuring optimal nutrient ratios for crops
(Agrahari et al., 2021).
Based on energy requirements, sensors are classified as active or passive. Active sensors, such as RADAR, GPS and LiDAR, emit signals and require external power (
Mulindi, 2023). LiDAR provides three-dimensional data on crop health and terrain
(Farhan et al., 2024). Passive sensors, like thermocouples, generate signals directly from environmental changes; for example, infrared thermocouples measure leaf temperature to assess plant physiological states
(Cheng et al., 2020).
Advancements in materials and technology have led to the development of CMOS, infrared, ultrasonic, microwave, electromechanical, radar, electrochemical and MEMS-based sensors (
White, 1987). CMOS image sensors, combined with infrared proximity sensors, have been used for wildlife monitoring
(Camacho et al., 2017). Ultrasonic and microwave technologies are employed in food processing and obstruction detection (
Singla and Sit, 2021;
Bhargava et al., 2021).
Sensors are also categorized by output signals: analog or digital. Analog sensors, like thermal remote sensors, estimate soil and crop water stress, detect diseased crops and map soil texture
(Khanal et al., 2017). Digital sensors, such as thermometers, provide discrete outputs; for instance, infrared photodiode thermometers enable long-term, contactless monitoring of cattle body temperature
(Murugeswari et al., 2022).
In agricultural applications, sensors monitor parameters like soil moisture, temperature, humidity and crop conditions.
Adoghe et al., (2017) developed a solar-powered automated weather station with meteorological sensors to support agricultural decision-making.
Palaparthy et al., (2018) created a low-cost graphene oxide humidity and soil moisture microsensor for in-situ field measurements. An automatic rain sensor helps farmers protect their crops from unexpected sudden rains during post-harvest operations (
Rajesh, 2019). Smart Environment Monitoring systems incorporate sensors like gas and particulate matter sensors for air quality assessment (
Ullo and Sinha, 2020). Electronic nose and electronic tongue have a wide range of application in the food and dairy industry (
Heema and Gnanalakshmi, 2022).
Raina et al., (2024) developed an energy-efficient device for monitoring onion quality using gas, temperature and humidity sensors. Application of GPS sensors in precision agriculture is given Table 1.
Operating principles of sensors in modern agriculture
Sensors operate based on principles such as amperometric, capacitive, pressure, MEMS, piezoelectric, thermochemical, inductive, magnetic, thermoelectric, optical, ultrasonic, radiometric, flexible and nanotechnology. Amperometric sensors measure current from enzymatic reactions; for example, they detect algal toxins in water
(Wu et al., 2019) and ethylene in agricultural products (
Caprioli and Quercia, 2014). Capacitive sensors detect changes in capacitance;
Kulmány et al. (2022) developed an automated capacitive soil moisture sensor. A soil moisture meter based on a capacitive type sensor is calibrated by
Balaji and Pandiarajan (2022) for sandy clay loam soil conditions.
Pressure sensors convert pressure into electrical signals and are essential in monitoring fluid or gas pressure in various systems. MEMS sensors integrate mechanical and electronic components on a microscale, allowing cultivation of plants under different stress conditions and monitoring plant-pathogen interactions
(Sharma et al., 2019). Piezoelectric sensors generate electric charge under mechanical stress and are used in precision seeders to detect impacts
(Rossi et al., 2023).
Chemical sensors detect chemical compounds through interactions leading to changes in physical properties; they are used in gas leak detection and environmental monitoring (
Wang, 2016). Inductive sensors operate on electromagnetic induction to detect metal objects and are applied in soil water content prediction
(Kachanoski et al., 1988). Magnetic sensors detect changes in magnetic fields based on the Hall effect or magnetoresistance and are used in position sensing and speed detection
(Elzwawy et al., 2024).
Thermoelectric sensors, like thermocouples, convert heat to electric energy and are used in temperature measurements .Optical sensors detect changes in light properties and are applied in imaging systems; hyperspectral sensors capture light across wavelengths for crop health assessment
(Lu et al., 2018). Infrared sensors are used in non-destructive testing and precision diagnostics in agriculture (
Bhan and Dhar, 2019).
Ultrasonic sensors use sound waves to detect objects and measure distance, aiding in irrigation systems for precision agriculture
(Camacho et al., 2022). Beyaz and Gerdan in 2020, reported the use of ultrasonic sensors for the classification of potato. LiDAR technology provides precise 3D data for crop health and terrain analysis
(Rivera et al., 2023).
Sensor fabrication approaches and material-based classification for agricultural applications
Sensor development is significantly influenced by the choice of materials and fabrication techniques, which determine sensitivity, durability and application scope. Advanced materials such as nanomaterials, polymers, silicon and graphene have improved sensor performance
(Bhandari et al., 2023). Nanomaterials, characterized by high surface area and distinct electrical properties, are commonly used in chemical and biological sensors to enhance sensitivity and selectivity
(Laxmi et al., 2022). Integration of nanoscale mechanical devices with nanoelectronics has expanded research in sensor-based systems (
Wang, 2024). In agriculture, nanomaterials improve crop productivity and soil health, influence pollutant behavior and are utilized in both biotic and abiotic remediation strategies
(Usman et al., 2020). They enhance nutrient uptake efficiency and support early disease detection. Silver and iron nanoparticles are applied for disinfection in livestock and poultry operations. Nanosensors, including smart dust and gas sensors, provide rapid environmental pollution assessment
(Chakraborty et al., 2024). Nano biosensors detect pathogens, fertilizers, soil pH and moisture, contributing to reduced dependence on chemical inputs and optimized nutrient use (
Kaushal and Wani, 2017). Electrochemical, voltammetric and amperometric nanosensors have been developed for detecting pathogenic bacteria (
Ahmed and Patel, 2023).
Polymers offer stretchability, conductivity and biocompatibility, making them suitable for wearable sensors. A chitosan-based water ink enables fabrication of stretchable strain sensors through direct application and drying at room temperature, producing sensors with a gauge factor of 64 for strains between 1% and 8% and stretchability up to 60%. These can be directly applied to plant surfaces, allowing in-situ fabrication and reducing energy and time requirements
(Presti et al., 2024). Wearable sensors enable continuous, real-time monitoring of plant physiological biomarkers, translating microenvironmental signals into electrical outputs
(Carella et al., 2024; Chen et al., 2023). Graphene, with high electrical conductivity, flexibility and mechanical strength, is suitable for flexible sensors and high-performance electronics
(Nag et al., 2018; Alam et al., 2022; Lau, 2023).
Silicon remains the core material in sensor manufacturing, particularly in MEMS devices where it facilitates miniaturization and electronic integration
(Le et al., 2021). 3D printing allows rapid prototyping of sensors with complex geometries and multi-material integration (
Khosravani and Reinicke, 2020). MEMS sensors are fabricated using microfabrication techniques such as photolithography and etching, which provide precise microscale control over sensor features and are essential for mass production of semiconductor devices. Molecular imprinting is employed to produce highly selective sensors by forming polymer matrices with molecular recognition sites, enhancing their specificity for target molecules in chemical and biological sensing
(Dong et al., 2023). Advances in these fabrication techniques and materials have enhanced the performance, scalability and application scope of next-generation sensors
(Hossain et al., 2024a, b).
Leveraging sensors for carbon-neutral agricultural systems
IoT sensors enable real-time monitoring of soil moisture, pH and nutrients (
Basnet and Bang, 2018). Environmental sensors assess air, water and soil quality
(Narayana et al., 2024; Bainomugisha et al., 2024), helping reduce pollution and anthropogenic impact
(Bansal et al., 2022). Sensor-based irrigation systems improve field water management
(Vandome et al., 2024). Thermal infrared and microwave sensors on satellites estimate crop temperature, water stress and soil moisture
(Sishodia et al., 2020).
Hyperspectral sensors capture crop stress responses across wavelengths
(Chakhvashvili et al., 2024), supporting pest/disease detection and IPM strategies
(Terentev et al., 2022). Sensor networks provide early warning of plant diseases
(Singh et al., 2020). Tools like FP-XRF detect heavy metals in soil; other devices capture soil strength and moisture (
Mcgrath and Scanaill, 2013). IoT trackers verify carbon emissions
(Peng et al., 2024) and help manage livestock waste
(Zakirova et al., 2022). MEMS/NEMS technologies support nutrient and salinity monitoring
(Palaparthy et al., 2013). NPK sensors allow precise nutrient application
(Ameer et al., 2024).
Flexible proximity sensors using PDMS and PI overcome traditional design limitations. Fe
3O
4 nanoparticles enhance sensitivity
(Jin et al., 2023). Amperometric sensors detect ethylene to monitor ripening (
Caprioli and Quercia, 2014). AI, ML and IoT assist in predictive disease detection
(Delfani et al., 2024), drone-assisted health monitoring and automated machinery deployment
(Chamara et al., 2022). Sensor data supports precise agrochemical use (
Basnet and Bang, 2018).
Hemming et al., (2019) used sensors to monitor crop and environment parameters in greenhouses. Table 2 decpits major type of sensors and their features.
Electronic integration of sensors in agricultural systems: A case study
India’s agriculture faces constraints due to climate, urbanization and outdated practices. Fertilizer misuse degrades soil. Monitoring temperature, pH, moisture, EC and NPK is vital for yield. Optimal pH (6.5–7.0), moisture and nutrient levels support healthy crops. Technologies incorporating microcontrollers and wireless systems enable precision farming
(Chamara et al., 2022).
In a smart nursery system, sensors monitor conditions; edge computing supports local decisions. Fig 2 and Table 3. Actuators manage fertigation through drip and mist systems. Filtered data is sent to the cloud for analytics. NPK, pH, EC, temperature and humidity sensors inform decisions. Soil moisture at 60-70% optimizes yield and appearance, while low moisture increases pH and lowers quality. The system enables efficient resource use and remote control. The case study was conducted in Regional Agricultural Research Station (South zone), Vellayani, Thiruvananthapuram, during 2222-23 with the funding from the Kerala State Planning Board.
Operational constraints in the adoption of sensors
Sensor accuracy is affected by temperature, humidity and salinity
(Zhang et al., 2023c; Ariyaratne et al., 2023; Qi et al., 2024). Clay soils reduce moisture sensor reliability (
Corwin and Scudiero, 2016). Sensors in autonomous systems fail in bad weather
(Vargas et al., 2021). Calibration, interference and power efficiency limit performance. Flexible sensors sacrifice electrical performance for adaptability
(Zhou et al., 2022). Cross-sensitivity impacts data accuracy. Interoperability and power access are critical for IoT deployment
(Soussi et al., 2024). SERS biosensors need improvement to avoid interference
(Tang et al., 2024).
Adoption challenges include environmental impacts on lifespan, lack of interoperability, energy limitations, poor rural connectivity, cost barriers and inconsistent performance across crops and landscapes. Other issues include interference, incomplete datasets, scaling difficulties, complex data storage, low farmer training, policy gaps, data security and low ROI. Research into sustainable sensor materials is needed to meet food security goals.
Emerging trends in sensor technology
Sensor development is moving toward AI/ML-enabled smart sensors for real-time decisions. These technologies enhance automation and predictive control but raise security and power issues. Future trends include edge computing, quantum sensing, 5G and improved integration
(Rajora et al., 2024). Nanostructured materials and miniaturization improve performance
(Ahmad et al., 2024). Flexible and wearable sensors will grow in medical, environmental and agricultural domains. Research focuses on sensor compatibility, performance and sustainability.
