IoT-Driven Soil Nutrient Measurement Using LoRa and Broken-Stick Regression Techniques
Received: 21 June 2025 | Revised: 26 July 2025, 23 August 2025, and 4 September 2025 | Accepted: 9 September 2025 | Online: 9 February 2026
Corresponding author: C. V. Pallavi
Abstract
Soil nutrient measurement systems are crucial for optimizing crop growth and yield in agriculture. Nitrogen (N), Phosphorus (P), and Potassium (K) are vital for crop growth. Many measuring systems have been presented, but accurately measuring soil nutrients is a challenging issue for efficient agriculture and environmental management. Conventional methods are often time-consuming, expensive, and incorrect due to soil variability and other factors. To address this issue, this study introduces Broken-Stick Regressive LoRa Technology (BSRLRT), aimed at accurately measuring soil nutrients based on IoT technology through sensor node deployment and data collection from a farmland. Initially, the broken-stick regression method is used to analyze and identify the exact position of the sensor nodes for efficient data collection. In addition, Long Range (LoRa) technology is used to monitor and collect data. The proposed method was experimentally deployed on V.C. Farm, Mandya, India.
Keywords:
soil, nitrogen-phosphorus-potassium, nutrient management system, long range, broken-stick regressionDownloads
References
M. R. Islam, K. Oliullah, M. M. Kabir, M. Alom, and M. F. Mridha, "Machine learning enabled IoT system for soil nutrients monitoring and crop recommendation," Journal of Agriculture and Food Research, vol. 14, Dec. 2023, Art. no. 100880. DOI: https://doi.org/10.1016/j.jafr.2023.100880
N. Ananthi, J. Divya, M. Divya, and V. Janani, "IoT based smart soil monitoring system for agricultural production," in 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR), Chennai, India, Apr. 2017, pp. 209–214. DOI: https://doi.org/10.1109/TIAR.2017.8273717
A. Na, W. Isaac, S. Varshney, and E. Khan, "An IoT based system for remote monitoring of soil characteristics," in 2016 International Conference on Information Technology (InCITe) - The Next Generation IT Summit on the Theme - Internet of Things: Connect your Worlds, Noida, India, Oct. 2016, pp. 316–320. DOI: https://doi.org/10.1109/INCITE.2016.7857638
M. AshifuddinMondal and Z. Rehena, "IoT Based Intelligent Agriculture Field Monitoring System," in 2018 8th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, Jan. 2018, pp. 625–629. DOI: https://doi.org/10.1109/CONFLUENCE.2018.8442535
L. Wang, X. Qiao, M. Li, and Y. Zhang, "Application and Development of Intelligent Systems in Monitoring and Early Warning of Coastal Soft Soil Foundation," Procedia Computer Science, vol. 243, pp. 577–584, Jan. 2024. DOI: https://doi.org/10.1016/j.procs.2024.09.070
P. Tan, E. T. Gebremariam, M. S. Rahman, H. Salman, and H. Xu, "Design and Implementation of Soil Moisture Monitoring and Irrigation System based on ARM and IoT," Procedia Computer Science, vol. 208, pp. 486–493, Jan. 2022. DOI: https://doi.org/10.1016/j.procs.2022.10.067
S. A. Siddiqi and Y. Al-Mulla, "Wireless Sensor Network System for Precision Irrigation using Soil and Plant Based Near-Real Time Monitoring Sensors," Procedia Computer Science, vol. 203, pp. 407–412, Jan. 2022. DOI: https://doi.org/10.1016/j.procs.2022.07.053
Y. T. Ting and K. Y. Chan, "Optimising performances of LoRa based IoT enabled wireless sensor network for smart agriculture," Journal of Agriculture and Food Research, vol. 16, June 2024, Art. no. 101093. DOI: https://doi.org/10.1016/j.jafr.2024.101093
V. T. Truong, A. Nayyar, and S. A. Lone, "System Performance of Wireless Sensor Network Using LoRa–Zigbee Hybrid Communication," Computers, Materials and Continua, vol. 68, no. 2, pp. 1615–1635, Mar. 2021. DOI: https://doi.org/10.32604/cmc.2021.016922
R. Cotrim, F. Assis, A. dos Santos Brito, Y. S. Peixouto, and L. S. Peixouto, "Multi-Hop LoRa-based underground network for monitoring soil moisture in agriculture," Computers and Electronics in Agriculture, vol. 227, Dec. 2024, Art. no. 109592. DOI: https://doi.org/10.1016/j.compag.2024.109592
X. Li et al., "Soil Moisture Monitoring and Evaluation in Agricultural Fields Based on NDVI Long Time Series and CEEMDAN," Remote Sensing, vol. 15, no. 20, Oct. 2023. DOI: https://doi.org/10.3390/rs15205008
R. Nagalingam, V. Chintamaneni, K. Paramasivan, and M. Ponnusamy, "Smart Agriculture – Automatic Monitoring of Soil Moisture and Irrigation Control for Farming Land," Current Agriculture Research Journal, vol. 11, no. 3, pp. 1023–1029, Jan. 2024. DOI: https://doi.org/10.12944/CARJ.11.3.30
S. R. Laha, B. K. Pattanayak, S. Pattnaik, D. Mishra, D. S. Kumar Nayak, and B. B. Dash, "An IOT-Based Soil Moisture Management System for Precision Agriculture: Real-Time Monitoring and Automated Irrigation Control," in 2023 4th International Conference on Smart Electronics and Communication (ICOSEC), Trichy, India, Sept. 2023, pp. 451–455. DOI: https://doi.org/10.1109/ICOSEC58147.2023.10276266
K. Khanal, G. Ojha, S. Chataut, and U. K. Ghimire, "IoT-Based Real-Time Soil Health Monitoring System for Precision Agriculture," International Research Journal of Engineering and Technology, vol. 11, no. 07, 2024.
M. Afzal, I. Ahmed Saeed, M. Noman Sohail, M. Hanif Md Saad, and M. R. Sarker, "IoT-Enabled Adaptive Watering System With ARIMA-Based Soil Moisture Prediction for Smart Agriculture," IEEE Access, vol. 13, pp. 27714–27728, 2025. DOI: https://doi.org/10.1109/ACCESS.2025.3532447
Z. Y. Lim, H. K. Mun, J. H. Low, B. H. Kwan, and C. H. Tan, "A two-port microwave sensor for real-time liquid fertilizer concentration measurement system with Internet of Things," Computers and Electronics in Agriculture, vol. 226, Nov. 2024, Art. no. 109460. DOI: https://doi.org/10.1016/j.compag.2024.109460
N. H. Bindra, N. K. Khedkar, R. S. Kalyankar, V. Wagh, and K. M. Mahale, "A Survey For Soil Testing And Scheduling In Iot Enabled Farms Using ML Algorithm," International Journal of Creative Research Thoughts, vol. 11, no. 4, pp. b501–b506, 2023.
A. M. Joshua, "Exploring Machine Learning Models for Soil Nutrient Properties Prediction: A Systematic Review," presented at the Deep Learning Indaba 2023, Aug. 2023.
S. M. Cheema and I. M. Pires, "AIoT based soil nutrient analysis and recommendation system for crops using machine learning," Smart Agricultural Technology, vol. 11, Aug. 2025, Art. no. 100924. DOI: https://doi.org/10.1016/j.atech.2025.100924
S. Mohanty, S. K. Pani, N. Tripathy, R. Rout, M. Acharya, and P. K. Raut, "Prevention of soil erosion, prediction soil NPK and Moisture for protecting structural deformities in Mining area using fog assisted Smart agriculture system," Procedia Computer Science, vol. 235, pp. 2538–2547, Jan. 2024. DOI: https://doi.org/10.1016/j.procs.2024.04.239
F. Rossi et al., "Predicting soil nutrients with PRISMA hyperspectral data at the field scale: the Handan (south of Hebei Province) test cases," Geo-spatial Information Science, vol. 27, no. 3, pp. 870–891, May 2024. DOI: https://doi.org/10.1080/10095020.2024.2343021
T. B. Sheeba et al., "Machine Learning Algorithm for Soil Analysis and Classification of Micronutrients in IoT-Enabled Automated Farms," Journal of Nanomaterials, vol. 2022, no. 1, 2022, Art. no. 5343965. DOI: https://doi.org/10.1155/2022/5343965
R. D. C. Pecho, K. S. Vijaya, N. Sharma, H. Pal, and B. K. Jose, "An approach for crop yield prediction using hybrid XGBoost, SVM and C4.5 classifier algorithms," Engineering and Applied Science Research, vol. 51, no. 3, pp. 300–312, Apr. 2024.
T. Abekoon, H. Sajindra, N. Rathnayake, I. U. Ekanayake, A. Jayakody, and U. Rathnayake, "A novel application with explainable machine learning (SHAP and LIME) to predict soil N, P, and K nutrient content in cabbage cultivation," Smart Agricultural Technology, vol. 11, Aug. 2025, Art. no. 100879. DOI: https://doi.org/10.1016/j.atech.2025.100879
W. Karamti, S. El Khediri, and T. Moulahi, "Sustainable Wastewater Management in Agriculture: A Deep Learning-based Olive Classification for Resource Efficiency in Water-Scarce Regions," Engineering, Technology & Applied Science Research, vol. 15, no. 3, pp. 24061–24069, June 2025. DOI: https://doi.org/10.48084/etasr.10667
Y. Wu, Z. Yang, Y. Liu, Y. Wu, Z. Yang, and Y. Liu, "Internet-of-Things-Based Multiple-Sensor Monitoring System for Soil Information Diagnosis Using a Smartphone," Micromachines, vol. 14, no. 7, July 2023. DOI: https://doi.org/10.3390/mi14071395
N. Kakhani et al., "SSL-SoilNet: A Hybrid Transformer-Based Framework With Self-Supervised Learning for Large-Scale Soil Organic Carbon Prediction," IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1–15, 2024. DOI: https://doi.org/10.1109/TGRS.2024.3446042
N. N. C. Othaman, M. N. Isa, and R. Hussin, "IoT Based Soil Nutrient Sensing System for Agriculture Application," International Journal of Nanoelectronics and Materials, vol. 14, pp. 279–288, 2021.
M. K. Senapaty, A. Ray, N. Padhy, M. K. Senapaty, A. Ray, and N. Padhy, "IoT-Enabled Soil Nutrient Analysis and Crop Recommendation Model for Precision Agriculture," Computers, vol. 12, no. 3, Mar. 2023. DOI: https://doi.org/10.3390/computers12030061
K. Avhad, I. Kharat, D. Mahajan, S. Jadhav, and M. Chattopadhyay, "Soil Nutrients Analysis Techniques and Crop/ Fertilizers Prediction- A Review," International Journal of Engineering Research, vol. 11, no. 12, pp. 39–48, 2022.
M. A. Ahmed et al., "LoRa Based IoT Platform for Remote Monitoring of Large-Scale Agriculture Farms in Chile," Sensors, vol. 22, no. 8, Apr. 2022. DOI: https://doi.org/10.3390/s22082824
M. I. Hossain et al., "Development of electrochemical sensors for quick detection of environmental (soil and water) NPK ions," RSC Advances, vol. 14, no. 13, pp. 9137–9158, 2024. DOI: https://doi.org/10.1039/D4RA00034J
A. Pal, S. K. Dubey, S. Goel, and P. K. Kalita, "Portable sensors in precision agriculture: Assessing advances and challenges in soil nutrient determination," TrAC Trends in Analytical Chemistry, vol. 180, Nov. 2024, Art. no. 117981. DOI: https://doi.org/10.1016/j.trac.2024.117981
D. G. Kharlukhi, K. Upadhyaya, and U. K. Sahoo, "Influence of integrated nutrient management on soil health, growth and yield of paddy in ‘jhum lands’ of north-eastern Himalayas," Discover Agriculture, vol. 2, no. 1, Nov. 2024, Art. no. 107. DOI: https://doi.org/10.1007/s44279-024-00128-w
H. N. Kim and J. H. Park, "Monitoring of soil EC for the prediction of soil nutrient regime under different soil water and organic matter contents," Applied Biological Chemistry, vol. 67, no. 1, Jan. 2024, Art. no. 1. DOI: https://doi.org/10.1186/s13765-023-00849-4
C. Tang et al., "An Electrochemical Microfluidic System for on-Site Continuous Monitoring of Soil Phosphate," IEEE Sensors Journal, vol. 24, no. 5, pp. 6754–6764, Mar. 2024. DOI: https://doi.org/10.1109/JSEN.2023.3348807
H. E. Ali, N. K. Asmel, A. A. Ganiyu, and H. Tijani, "Effect of sodium compounds additives on the strength of cement-stabilized soils," Engineering and Applied Science Research, vol. 47, no. 3, pp. 287–296, Sept. 2020.
L. Burton, K. Jayachandran, and S. Bhansali, "Review—The ‘Real-Time’ Revolution for In situ Soil Nutrient Sensing," Journal of The Electrochemical Society, vol. 167, no. 3, Oct. 2020, Art. no. 037569. DOI: https://doi.org/10.1149/1945-7111/ab6f5d
R. P. Potdar, M. M. Shirolkar, A. J. Verma, P. S. More, and A. Kulkarni, "Determination of soil nutrients (NPK) using optical methods: a mini review," Journal of Plant Nutrition, vol. 44, no. 12, pp. 1826–1839, July 2021. DOI: https://doi.org/10.1080/01904167.2021.1884702
S. Postolache et al., "IoT-Based Systems for Soil Nutrients Assessment in Horticulture," Sensors, vol. 23, no. 1, Dec. 2022. DOI: https://doi.org/10.3390/s23010403
H. Pratama, A. Yunan, and R. A. Candra, "Design and Build a Soil Nutrient Measurement Tool for Citrus Plants Using NPK Soil Sensors Based on the Internet of Things," Brilliance: Research of Artificial Intelligence, vol. 1, no. 2, pp. 67–74, Dec. 2021. DOI: https://doi.org/10.47709/brilliance.v1i2.1300
M. A. Al-Shareeda, A. M. Ali, M. A. Hammoud, Z. H. M. Kazem, and M. A. Hussein, "Secure IoT-Based Real-Time Water Level Monitoring System Using ESP32 for Critical Infrastructure," Journal of Cyber Security and Risk Auditing, vol. 2025, no. 2, pp. 43–52, Apr. 2025. DOI: https://doi.org/10.63180/jcsra.thestap.2025.2.4
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