Estimation of Sensor Data Fusion and Evapotranspiration for Enhanced Performance in Smart Drip and Conventional Irrigation Systems

631 Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 631-638 p ISSN: 2635-3342; e ISSN: 2635-3350 Original Research Article Estimation of Sensor Data Fusion and Evapotranspiration for Enhanced Performance in Smart Drip and Conventional Irrigation Systems *1Odo, K.O., 2Abonyi, D.O., 1Okoro, C.K. and 1Omosun, Y. 1Department of Electrical and Electronic Engineering, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria. 2Department of Electrical and Electronic Engineering, Enugu State University of Science and Technology, Enugu State, Nigeria. *[email protected]; abonyi.dor[email protected]u.ng http://doi.org/10.5281/zenodo.18062111 ARTICLE INFORMATION ABSTRACT Article history: Received 04 Nov. 2025 Revised 06 Dec. 2025 Accepted 09 Dec. 2025 Available online 30 Dec. 2025 Accurate estimation of crop water requirements is essential for improving irrigation efficiency in modern agriculture. This study examined the role of Sensor Data Fusion (SDF) and evapotranspiration (ETo) estimation in enhancing the performance of a Smart Drip Irrigation System (SDIS). Sensor measurements of soil moisture, soil temperature, and relative humidity for five (5) months (April, 2025 – August, 2025) were integrated to compute monthly SDF values, while ETo was estimated to evaluate crop water demand. Results showed that SDIS consistently achieved lower SDF values (56.992 – 77.062) than the Conventional Irrigation System (CIS), which recorded noticeably higher values (70.472 – 78.544) over the same period. Similarly, SDIS produced lower ETo values (504.97523.71 mm/month) compared with the higher CIS values (532.91 – 596.72 mm/month), indicating reduced evaporative losses and improved irrigation precision. These outcomes demonstrated that SDIS more accurately captured real-time field conditions and aligned irrigation scheduling with actual evapotranspiration needs. Overall, the integration of sensor data fusion significantly enhanced ET estimation, minimized water wastage, and supported sustainable water management. The findings affirmed SDIS as a more efficient and adaptive alternative to conventional irrigation systems, supporting its adoption in modern precision agriculture. © 2025 RJEES. All rights reserved. Keywords: Sensor data fusion Evapotranspiration Wireless sensor networks Conventional irrigation system Smart drip irrigation system 1. INTRODUCTION Agriculture has long been central to human development, yet contemporary production systems must now meet rising food demand while minimizing environmental impacts. These pressures have accelerated the adoption of advanced technologies capable of improving productivity and resource 632 K.O. Odo et al. / Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 631-638 efficiency (Matthew et al., 2019). Among essential agricultural inputs, water remains a limiting and increasingly contested resource; thus, reliable water management is fundamental to sustainable crop production. Precision irrigation, particularly drip irrigation, has become critical for delivering water efficiently to crop root zones while limiting losses from evaporation, percolation, and runoff (Gennaro et al., 2024). Conventional irrigation practices, which often rely on timers or fixed schedules, do not account for the spatial and temporal variability of soil moisture, crop phenology, and atmospheric demand. As a result, they frequently apply water inefficiently, lowering overall water-use efficiency (WUE). Advances in wireless sensor networks, internet of things (IoT) platforms, and data-driven analytics have enabled a new generation of irrigation systems that respond dynamically to real-time environmental and crop signals. Smart drip irrigation systems, in particular, have demonstrated strong potential to optimize water delivery due to their ability to integrate distributed sensing with automated control (Kumar et al., 2024). Drip irrigation is widely recognized as one of the most water-efficient methods for delivering water directly into the root zone, minimizing evaporation and percolation losses relative to surface or sprinkler systems. However, even drip systems can underperform if not managed adaptively: applying fixed schedules or threshold-based control can lead to over-watering or under-watering under varying soil and climate conditions. To address this, smart drip irrigation systems integrate wireless sensor networks (WSNs) and advanced control logic. WSNs provide real-time, spatially distributed measurements such as soil moisture, soil temperature, air humidity, and microclimate variables which are essential for capturing field heterogeneity. The use of WSN in precision agriculture has been well documented, especially for environmental monitoring, and automated actuation, (Musa et al, 2024). Wireless sensor networks (WSNs) are an advancing technology and are extensively utilized in a range of applications, such as environmental monitoring (Faseth et al., 2010), healthcare systems (Hussain et al., 2013), industrial process monitoring (Misra et al., 2015), smart agriculture (Mohan et al., 2020), home automation (Lee et al., 2010), traffic management (Jain et al., 2011). When supported by multi-sensor inputs, these systems can more accurately identify plant water needs, minimize overor under-irrigation, and enhance both crop performance and resource conservation (Ruipeng et al., 2024). A core variable underpinning such adaptive irrigation is evapotranspiration (ET) the combined loss of water through soil evaporation and plant transpiration. ET provides a physiologically meaningful indicator of crop water demand; however, its accurate estimation remains challenging due to the need to integrate heterogeneous meteorological, soil, and canopy data across varying scales. Recent progress in ET inversion algorithms and remote sensing, combined with in-field sensor networks and data-fusion methods, has improved the precision and temporal resolution of ET estimation for on-farm decision-making (Derardja et al., 2024). Sensor fusion plays a key role in this context by combining diverse, spatially sparse measurements such as soil moisture data, plant stress indicators, and microclimate variables into a coherent state estimate for irrigation control. Techniques ranging from statistical fusion and Kalman filtering to machinelearning ensembles have been shown to effectively reconcile noise, uncertainty, and missing data, enabling more reliable moisture estimation and more targeted irrigation scheduling. Field studies integrating sensor-fusion techniques with low-power wireless networks confirm that fused datasets improve irrigation decisions and can produce substantial water savings relative to single-sensor approaches, particularly in drip systems where localized delivery benefits from fine-scale, zone-specific information (Gong et al., 2022). Despite these advancements, efficient water management remains a persistent challenge, especially in regions experiencing heightened climate variability and limited freshwater availability. Smart drip irrigation systems (SDIS), while promising, still face limitations when driven by fragmented sensor data and inaccurate or absent ET inputs. Many existing systems depend on single-sensor thresholds or simplified rules that do not adapt effectively to dynamic environmental conditions, resulting in suboptimal water use. Inadequate integration of multi-sensor data further reduces system reliability and weakens the capacity for real-time, demand-driven irrigation. Consequently, there is a pressing need for an SDIS framework that effectively integrates sensor data fusion with accurate ET estimation to 633 K.O. Odo et al. / Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 631-638 enhance irrigation accuracy, improve water-use efficiency, and support intelligent, adaptive irrigation control. To further situate these challenges within existing scientific efforts, it is important to examine some of the previous studies that have explored smart irrigation technologies, sensor-based monitoring, and evapotranspiration estimation. Derardja et al. (2024) provided a comprehensive review of temperaturebased remote sensing ET advances and highlight improvements in mapping ETa using thermal sensors and new temperature-based algorithms. Lakhiar et al., (2024) synthesized advances in wireless sensors and precision irrigation scheduling, noting that multi-sensor fusion, cloud analytics, and ET-based scheduling are converging trends for water-saving SDIS. These reviews contextualize where fusion and ET estimation fit into broader precision-agriculture ecosystems. Odo et al, (2024) investigated the use of wireless sensor networks as a vital tool for improved agricultural practices in Nigeria. The authors integrated various sensors to gather critical environmental data such as soil moisture, soil temperature, humidity and water levels, providing valuable insights for precision agriculture. By automating irrigation and improving crop monitoring, wireless sensor networks helped optimize water usage, reduced resource wastage and increased crop yields. Despite the good work done by these authors, their research was limited to only one month. Although notable progress has been made, key gaps persist in current research. There is limited systematic evaluation of how various data-fusion algorithms and their uncertainties influence field-level control performance and water-use efficiency. Likewise, the integration of reference crop evapotranspiration (ETo) estimation with fused soil and plant signals to support adaptive, crop-stagespecific irrigation decisions remains underexplored. Therefore, this research investigates how a smart drip irrigation system (SDIS) that employs continuous sensor data fusion can optimize irrigation scheduling and enhance evapotranspiration monitoring compared to conventional methods used in modern precision agriculture. 2. MATERIALS AND METHODS 2.1. Materials The materials required for the research were categorized into hardware and software components. Hardware materials include drip tape, emitters, end caps, drip irrigation connectors, tomato crop, solenoid valve, 12 V Relay, ESP32, soil moisture sensors, soil temperature sensors, DHT22 sensor, ultrasonic sensors, smart phone, HP laptop, Wi-Fi module, 50W solar panel, 10A charge controller, 12 V DC battery and Buck converter while Software materials include the Arduino nano microcontroller IDE and MATLAB computing software 2.2. System Overview The Smart Drip Irrigation System (SDIS) was designed to automate irrigation scheduling by integrating multi-sensor data fusion with evapotranspiration (ET) estimation for adaptive water control. The system architecture consists of four functional modules: (i) sensing and data acquisition, (ii) wireless data transmission, (iii) data fusion and ET estimation, and (iv) irrigation control and actuation. The overall system block diagram is shown in Figure 1. Figure 1: Block diagram of the system The overall system architecture for the smart irrigation framework integrated four major functional blocks sensing and data acquisition, wireless data transmission, data fusion and evapotranspiration (ET) estimation, irrigation control and actuation all working together to optimize water use and enhance crop productivity. 634 K.O. Odo et al. / Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 631-638 The process began with the sensing and data acquisition unit, which forms the foundation of the system. This unit consisted of a network of field-deployed sensors that continuously monitor environmental and soil parameters relevant to plant water requirements. Typical sensors included soil moisture sensors, soil temperature sensors, and relative humidity sensors. These sensors provide real-time information about the microclimatic and soil conditions within the cultivated area. The collected signals were conditioned, digitized, and pre-processed by a microcontroller. This preprocessing step often included averaging and timestamping to ensure data reliability before transmission. The wireless data transmission block served as the communication bridge between the field sensors and the central processing unit or cloud server. In modern implementations, low-power and long-range wireless technologies such as Wi-Fi was adopted to ensure reliable and energy-efficient data transfer. The gateway node aggregates data from multiple sensor nodes and forwards it via an appropriate communication protocol (HTTP) to MySQL online server for further analysis. At the data fusion and ET estimation stage, the received sensor data were analyzed, validated, and integrated to improve accuracy and completeness. Data fusion algorithms such as weighted averaging was employed to combine multiple sensor readings, thereby minimizing errors caused by sensor noise, calibration drift, or data loss. The data collected from the farm was used to estimate the evapotranspiration (ET) rate, which is a key indicator of crop water demand. The irrigation control and scheduling unit interpreted the estimated ET values and sensor feedback to determine optimal irrigation timing and volume. Finally, the actuation block translated the control signals into physical actions. This unit used solenoid valve for water delivery. When triggered, the actuator regulated the flow of water to the farm, ensuring that each area receives the right amount of irrigation. Overall, the integration of these blocks created a closed-loop intelligent irrigation system capable of automatically monitoring, analyzing, and responding to environmental conditions in real time. By linking sensor-based monitoring with data fusion, ET estimation, and precise control, the system enhanced water use efficiency, reduced manual intervention, and supported sustainable agricultural practices. The key parameter indicators used for evaluating the performance of crop yield and water usage in a tomato farm are: 1. Reference evapotranspiration (ETo): It is the process by which water is transferred from the land to the atmosphere by evaporation from the soil and other surfaces and by transpiration from plants. Prediction of soil moisture is essential for effective irrigation management. The reference evapotranspiration is calculated based on the latest modification of the original Blaney-Criddle equation given in Equation 1 as: 𝐸𝑇𝑂= 𝑃(0.46𝑇𝑚𝑒𝑎𝑛 + 8) (1) where ETo is the reference crop evapotranspiration (mm/day) T is the mean daily temperature (°C) and P is the total rainfall (mm) The mean rainfall for the period of 5 months is given in Equation 2 as: P = 𝑅𝑎𝑖𝑛𝑓𝑎𝑙𝑙 (𝑚𝑚) 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑑𝑎𝑦𝑠 (2) 2. Sensor data fusion (SDF): Sensor data fusion (SDF) refers to the process of combining data from multiple sensors to produce more accurate, reliable and useful information than would be possible by using the data from individual sensors separately. This technique is widely used in various fields such as wireless sensor networks, robotics, autonomous vehicles and environmental monitoring. One of the commonly used mathematical equations in sensor data fusion is the weighted average. The weighted average method combines sensor readings based on their weights, which typically reflect the reliability or accuracy of each sensor. When combining data from multiple sensors (soil moisture, soil temperature, humidity, ultrasonic sensors), a sensor data fusion in Equations 3 and 4 can be calculated using the weighted average as: 𝑆𝐷𝐹 = ∑𝑤𝑒𝑖𝑔ℎ𝑡𝑒𝑑 𝑎𝑣𝑒𝑟𝑎𝑔𝑒𝑖 𝑛 𝑖=1 (∑𝑤𝑖𝑥𝑖 𝑛 𝑖=1 ) ∑𝑤𝑖 (3) 635 K.O. Odo et al. / Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 631-638 𝑆𝐷𝐹 = (𝑊𝑆𝑀𝑥𝑋𝑆𝑀)+(𝑊𝑆𝑇𝑥𝑋𝑆𝑇)+(𝑊𝐻𝑥𝑋𝐻)+(𝑊𝑈𝑆𝑥𝑋𝑈𝑆) 𝑊𝑆𝑀+𝑊𝑆𝑇+𝑊𝐻+𝑊𝑈𝑆 (4) where 𝑥𝑖= measurement value from the i-th sensor, 𝑤𝑖= weight assigned to the i-th sensor. Considering the critical importance of soil moisture, soil temperature and humidity to tomato plant growth, the recommended average weight based on typical agronomic practices are 𝑊𝑆𝑀= 0.45, 𝑊𝑆𝑇 = 0.25,𝑊𝐻= 0.15 and 𝑊𝑈𝑆 = 0.10 respectively. 2.3. Experimental Setup The experimental setup for the smart drip irrigation system is based on a wireless sensor network (WSN). It consists of a network of sensors that monitor soil moisture content, temperature, and humidity levels. The sensor nodes are linked to a central node (an Arduino Microcontroller Board), which is in charge of gathering data from the sensors and transmitting it to the ESP32 Wi-Fi module, which is responsible for uploading the data to the online Database for cloud-based data storage. To operate the hardware, a software program was created to run on the Arduino and ESP32 microcontrollers. The hardware components of the system were positioned close to the tomato crops in the farm to feel its surroundings, and it was wirelessly connected to the main station through an ESP32 microcontroller that serves as the main wireless data transmission channel. The main data collection station collects the data and processes the sensor values based on the C++ instructions that have been programmed into it. It also enabled the Arduino to initiate a serial communication with the ESP32 through the serial ports. Also based on the C++ program instruction, the Arduino microcontroller was able to evaluate the sensor reading, especially the reading from the soil moisture sensor, and used the result of this evaluation as a guide to either turn on or turn off the solenoid valve through a relay module. 3. RESULTS AND DISCUSSION The mean of 5 months of climatic (environmental) data for the smart drip irrigation system (SDIS) from April 2025 to August 2025 is shown in Table 1. The mean of 5 months climatic (environmental) data for the conventional irrigation system (CIS) from April, 2025 to August, 2025 is shown in Table 2. The average rainfall calculated using Equation 2 is 𝑃 = 24.994𝑚𝑚/𝑑𝑎𝑦𝑠 Table 1: Mean of 5 months of data for SDIS Months Soil moisture (%) Soil temperature (°C) Humidity (%) Days Rainfall (mm) April 70.72 27.12 89.34 18 438.3 May 83.32 26.72 81.67 15 388.2 June 84.18 26.53 82.05 17 416.8 July 83.29 26.61 80.08 10 272.4 August 83.25 28.16 78.64 5 108.9 Table 2: Mean of 5 months of data for CIS Months Soil moisture (%) Soil temperature (°C) Humidity (%) Days Rainfall (mm) April 87.35 28.96 89.14 18 438.3 May 98.10 31.25 98.70 15 388.2 June 93.27 32.32 95.24 17 416.8 July 94.39 34.51 97.35 10 272.4 August 96.40 33.50 98.46 5 108.9 3.1. Comparison of Reference Evapotranspiration for SDIS and CIS The graph of reference evapotranspiration for SDIS and CIS against number of months in shown in Figure 2. Evapotranspiration is a key factor in irrigation systems, reflecting the combined loss of water through evaporation (from soil and water surfaces) and transpiration (from plants). It is seen from the graph that the conventional irrigation system produced higher values of reference evapotranspiration than the smart drip irrigation system. The gradual decrease and increase in evapotranspiration in November for CIS and SDIS 636 K.O. Odo et al. / Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 631-638 indicates seasonal changes such as higher water requirements due to late season growth of tomato plants. The highest value of evapotranspiration for CIS is 596.72mm/month which was recorded in July, 2025 while the lowest value of evapotranspiration for SDIS is 504.97 mm/month, which was recorded in June, 2025. Figure 2: Graph of reference evapotranspiration against number of months It is an indication that the tomato plants experienced water stress due to over irrigation or under irrigation leading to reduced growth and lower yields. The low values of evapotranspiration recorded by the smart drip irrigation system mean that the system delivered less water thereby conserving resources and reducing costs. It also reduced the risk of plant water stress, thereby, promoting steady growth. Therefore, the system being guided by real-time data ensured precise water application based on actual crop needs. 3.2. Estimation of Sensor Data Fusion (SDF) using the Weighted Average Approach The graph of sensor data fusion for SDIS and CIS against number of months in shown in Figure 3. It is observed from the graph that the sensor data fusion values of the smart drip irrigation system are lower than the sensor data fusion values of conventional irrigation system. The CIS recorded the highest value of SDF in May, 2025, which is 78.544, whereas the lowest value of SDF for SDIS is 56.992, recorded in April, 2025. Figure 3: Graph of sensor data fusion against number of months It implies that the SDIS has a dynamic system that adjusted its operations based on environmental conditions but the gradual increase in August indicated seasonal changes such as higher water requirements due to lateseason growth phases. The SDIS showed significantly lower values during most months, indicating better 637 K.O. Odo et al. / Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 631-638 resource management and adaptability to environmental needs while conventional irrigation system consistently produced higher values, implying inefficiency and overuse of resources. Also, the lower fusion values implied efficient, sensor-driven water use, which is crucial for sustainability and precise farming while higher fusion values implied traditional systems are less efficient leading to overwatering resource wastage. The sensor data fusion values revealed that the SDIS offers superior performance, adaptability and efficiency compared to the CIS, which appeared to operate at higher resource levels and lower precision. These values serve as an indicator of system effectiveness and can guide decisions to improve irrigation practices in Nigeria. 4. CONCLUSION The results obtained from both the Sensor Data Fusion (SDF) and evapotranspiration (ETo) analyses demonstrated that the Smart Drip Irrigation System (SDIS) provided a significantly more efficient and adaptive irrigation approach than the Conventional Irrigation System (CIS). The consistently lower SDF values recorded in SDIS reflected its ability to integrate soil moisture, temperature, and humidity data more accurately, thereby offering a clearer understanding of real-time field conditions. This enhanced sensing capability directly contributed to the reduced ETo values observed across all months, indicating lower evaporative losses and more precise estimation of crop water requirements. In contrast, the higher SDF and ETo values in CIS highlighted the inefficiencies associated with conventional irrigation practices, which do not respond dynamically to changing environmental conditions. Collectively, these outcomes confirmed that the adoption of sensor data fusion significantly improved evapotranspiration estimation and strengthened the overall performance of smart drip irrigation systems. However, the study is not without limitations. Data were collected over a five-month period, which may not fully capture the seasonal fluctuations occurring across a complete agricultural cycle. The evaluation was also conducted under a single climatic condition and soil type, making it difficult to generalize the findings to broader agro-ecological contexts. Minor inaccuracies may have arisen from sensor calibration drift or environmental interference, which are common challenges in field-based sensor deployments. To enhance the reliability and impact of such systems, it is recommended that future implementations incorporate regular sensor calibration, improved sensor protection, and redundancy for critical measurements to ensure stable long-term operation. Expanding the monitoring period to cover full planting cycles and multiple crop types will also provide deeper insight into system performance across varying conditions. Looking ahead, future studies should investigate the integration of machine learning techniques for more accurate evapotranspiration prediction, explore the use of satellite or drone-derived data to complement ground sensors, and conduct large-scale field trials across diverse climatic regions. Comparative assessments involving different soil textures, irrigation regimes, and crop varieties will further strengthen the application of SDIS and broaden its relevance in precision agriculture. 5. ACKNOWLEDGMENT The authors wish to acknowledge the assistance and contributions of the staff of Department of Electrical and Electronic Engineering, Enugu State University of Science and Technology, Enugu and Michael Okpara University of Agriculture, Umudike toward the success of this work. 6. CONFLICT OF INTEREST There is no conflict of interest associated with this work. REFERENCES Derardja, B., Khadra, R., Abdelmoneim, A. A. A., El-Shirbeny, M. A., Valsamidis, T., De Pasquale, V., Deflorio, A. M., and Volden, E. (2024). Advancements in remote sensing for evapotranspiration estimation: A comprehensive review of temperature-based models. Remote Sensing, 16(11), pp. 1927 Faseth, T.R., Baumann, P., Gruber, S.T., Hasler, A., Kosch, T., Lehning, M., Mendez, R., Vilella, D., Li, J.Y. and Badia, P. (2010). Wireless sensor networks for environmental monitoring: The sensor scope experience, proceedings of the Institute of Electrical and Electronics Engineers, (IEEE), vol. 98, no. 11, pp. 1903-1917 638 K.O. Odo et al. / Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 631-638 Gennaro, S.F. D.; Cini, D.; Berton, A. and Matese, A. (2024). Development of a low-cost smart irrigation system for sustainable water management in the Mediterranean region. Smart Agricultural Technology, Volume 9, pp. 100629. https://doi.org/10.1016/j.atech.2024.100629 Gong, L., Yan, J.; Chen, Y.; An, J.; He, L.; Zheng, L.; and Zhuo, Z. (2022). An IoT-based intelligent irrigation system with data fusion and a self-powered wide-area network. Journal of Industrial Information Integration, Volume 29, pp. 100367. https://doi.org/10.1016/j.jii.2022.100367 Hussian, S.A., Singh, K.P., Suryadevara, N.K., and Mukhopadhyay, S.C. (2013). Wireless sensor networks in healthcare: A survey. International journal of computer applications, vol. 62, no. 1, pp. 37-43 Jain, R., Xie, F., Wu, J. and Chen, J. (2011). Wireless sensor networks for intelligent transportation systems. Institute of Electrical and Electronics Engineers,(IEEE) transactions on intelligent transportation systems, vol. 12, no. 4, pp.1667-1676. Kumar, V.; Sharma, K.V.; Kedam, N.; Patel, A.; Kate, T.R. and Rathnayake, U. (2024). A Comprehensive Review on Smart and Sustainable Agriculture Using IoT Technologies. Smart Agricultural Technology, Volume 8, pp.100487 Lakhiar, I. A.; Yan, H.; Zhang, C.; Wang, G.; he, B.; Hao, B.; han, Y.; Wang, B.; Bao, R.; Syed, T.N.; Chauhdary, J.N.; and Rakibuzzam, M (2024). A review of precision irrigation water-saving technology and smart irrigation scheduling algorithms. Agriculture, Multidisciplinary Digital Publishing Institute, (MDPI), 14(7), pp. 1141. https://www.mdpi.com/2077-0472/14/7/1141 Lee, T.A., Chung, J.W. and Hong, S.H. (2010). Smart home energy management system using wireless sensor networks. Institute of Electrical and Electronics Engineers, (IEEE) transactions on consumer electronics, vol. 56, no. 3, pp. 1403 – 1410. Matthew N. O. Sadiku, Kirtikumar K. Patel and Sarhan M. Musa (2019). Wireless Sensor Network in Agriculture: A Primer. International Journal of Trend in Research and Development, Volume 6(5), pp. 4-6. Misra, S., Woungang, I. and Misra, S.C. (2015). Industrial wireless sensor networks: Applications, protocols and standards. International journal of distributed sensor networks, vol. 11, no. 6, pp. 690583 Mohan, A., Subramanian, S., Manogaran, V., Venkatesh, G. and Kanniappan, G.K. (2020). Wireless sensor networks for agriculture: The state of the art in practice and future challenges. Computers and electronics in agriculture, vol. 174, pp. 105507 Musa, P.; Sugeru, H. and Wibowo, E.P. (2024). Wireless sensor networks for precision agriculture: a review of NPK sensor implementations. Sensors 2024, 24(1), pp. 51; https://doi.org/10.3390/s24010051 Odo, K.O., Abonyi, D.O. and Eke, J. (2024). Wireless sensor networks: A vital tool for improved agricultural practices in Nigeria. UNIZIK Journal of Engineering and Applied Sciences 3(5), December (2024), pp. 1324 - 1334 Ruipeng T.; Jianbu Y.; Jianrui T.; Aridas N.K.; and Talip, M.S.A. (2024). Design of agricultural wireless sensor network node optimization method based on improved data fusion algorithm. PLoS ONE 19(11), pp. 1-19. https://doi.org/10.1371/journal.pone.0308845.