Innovations in Soil Health Monitoring: Role of Advanced Sensor Technologies and Remote Sensing

Authors

DOI:

https://doi.org/10.18006/2024.12(5).653.667

Keywords:

Soil health, Advanced sensor technologies, Remote sensing, IoT, Sustainable agriculture

Abstract

Soil health monitoring is essential for sustainable agricultural practices and effective environmental management. Recent sensor technologies and remote sensing innovations have transformed how we assess soil health, providing real-time and precise data that enhance decision-making processes. This review focuses on integrating advanced sensor technologies, like Internet of Things (IoT) devices, alongside remote sensing techniques, including drones and satellite imagery, in soil science. These technologies enable continuous monitoring of critical soil parameters, such as moisture levels and nutrient content, significantly improving the accuracy and efficiency of soil health evaluations. Additionally, remote sensing provides a comprehensive overview of soil conditions across large areas, allowing for the identification of spatial patterns and temporal changes that traditional methods may overlook. Various case studies from agricultural and environmental projects demonstrate the practical benefits and the challenges of implementing these innovations. The article also discusses future trends and potential obstacles, highlighting the need for further research and development to exploit these technologies' capabilities fully. Ultimately, advanced sensors and remote sensing promise to improve soil health monitoring, contributing to more sustainable and productive agricultural systems.

Author Biographies

Jorge Luis Huere-Peña, Universidad Nacional de Huancavelica, Huancavelica, Perú

Universidad Nacional de Huancavelica, Huancavelica, Perú

Manuel Castrejon-Valdez, Universidad Nacional de Huancavelica, Huancavelica, Perú

Universidad Nacional de Huancavelica, Huancavelica, Perú

Cesar Castañeda-Campos, Universidad Nacional de Huancavelica, Huancavelica, Perú

Universidad Nacional de Huancavelica, Huancavelica, Perú

Rodolfo Leon-Gomez, Universidad Nacional de Huancavelica, Huancavelica, Perú

Universidad Nacional de Huancavelica, Huancavelica, Perú

Walter Augusto Mateu-Mateo, Universidad Nacional de San Cristóbal de Huamanga, Ayacucho, Perú

Universidad Nacional de San Cristóbal de Huamanga, Ayacucho, Perú

Rolando Bautista-Gómez, Universidad Nacional de San Cristóbal de Huamanga, Ayacucho, Perú

Universidad Nacional de San Cristóbal de Huamanga, Ayacucho, Perú

Edward Arostegui-León, Universidad Nacional José María Arguedas, Andahuaylas, Perú

Universidad Nacional José María Arguedas, Andahuaylas, Perú

Carlos Dueñas-Jurado, Universidad Nacional de Huancavelica, Huancavelica, Perú

Universidad Nacional de Huancavelica, Huancavelica, Perú

Edwin Javier Ceenti-Chancha, Universidad Nacional de Huancavelica, Huancavelica, Perú

Universidad Nacional de Huancavelica, Huancavelica, Perú

Edwin Rojas-Felipe, Universidad Nacional de Huancavelica, Huancavelica, Perú

Universidad Nacional de Huancavelica, Huancavelica, Perú

Russbelt Yaulilahua-Huacho, Universidad Nacional de Huancavelica, Huancavelica, Perú

Universidad Nacional de Huancavelica, Huancavelica, Perú

References

Adamchuk, V. I., Hummel, J. W., Morgan, M. T., & Upadhyaya, S. K. (2004). On-the-go soil sensors for precision agriculture. Computers and Electronics in Agriculture, 44(1), 71-91.

Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393-422.

Basso, B., Reynolds, M., & D'Urso, G. (2021). Use of electrical conductivity sensors for irrigation management in arid and semi-arid regions. Field Crops Research, 258, 107914. https://doi.org/10.1016/j.fcr.2020.107914

Borrelli, P., Robinson, D. A., Panagos, P., Lugato, E., Yang, J. E., et al. (2017). An assessment of the global impact of 21st-century land use change on soil erosion. Nature Communications, 8, 2013. https://doi.org/10.1038/s41467-017-02142-7

Brady, N. C., & Weil, R. R. (2008). The nature and properties of soils (Vol. 13, pp. 662-710). Upper Saddle River, NJ: Prentice Hall.

Chen, L., Zhang, H., & Liu, Y. (2020a). IoT and weather data integration for flood risk management in rice paddies. Field Crops Research, 249, 107781. https://doi.org/10.1016/j.fcr.2020.107781

Chen, X., Wu, L., & Zhang, L. (2020b). Precision agriculture: Opportunities and challenges for the future. Journal of Agricultural Science, 12(3), 55-68.

Circuit Digest. (2023). IoT-based smart agriculture monitoring system | Smart farming project using NodeMCU ESP8266. Retrieved from https://circuitdigest.com

Corwin, D. L., & Lesch, S. M. (2005). Apparent soil electrical conductivity measurements in agriculture. Computers and Electronics in Agriculture, 46(1-3), 11-43.

Feng, L., Zhang, Y., & Liu, Y. (2022). Drone-based multispectral remote sensing for soil moisture and crop stress assessment in agricultural fields. Remote Sensing, 14(12), 2931. https://doi.org/10.3390/rs14122931

Fritton, D. D., & Olson, G. W. (1972). Temperature measurement in soil. Agronomy Journal, 64(5), 629-634.

Gao, J., Zhang, X., & Wang, Z. (2023). Optimization of irrigation schedules using electrical conductivity sensors in irrigated agricultural fields. Agricultural Water Management, 280, 107925. https://doi.org/10.1016/j.agwat.2022.107925

Hassan, A., Bhat, S., & Singh, S. (2021). Integration of IoT-based sensors for optimizing fertilization and pH management in wheat farming. Agricultural Systems, 187, 103039. https://doi.org/10.1016/j.agsy.2020.103039

Hassan, R., Khan, S., & Ahmed, M. (2022). Application of nutrient sensors for monitoring nutrient runoff in agricultural watersheds. Environmental Monitoring and Assessment, 194(6), 310. https://doi.org/10.1007/s10661-022-10223-3

Havlin, J. L., Tisdale, S. L., Nelson, W. L., & Beaton, J. D. (2013). Soil fertility and fertilizers: An introduction to nutrient management (8th ed.). Pearson.

He, Z. L., Zhang, H., Calvert, D. V., Stoffella, P. J., Yang, X. E., & Yu, S. (2007). Assessment of soil property variability using a global positioning system and geostatistics. Communications in Soil Science and Plant Analysis, 38(5-6), 689-711.

Hernández, R., García, J., & Martínez, M. (2021). Assessment of soil nutrient levels using hyperspectral remote sensing. Remote Sensing of Environment, 255, 112257. https://doi.org/10.1016/j.rse.2021.112257

Huang, Y., Liu, L., & Zhang, C. (2022). Field-scale assessment of soil moisture sensing for vineyard irrigation management. Agricultural Systems, 195, 103283. https://doi.org/10.1016/j.agsy.2021.103283

Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295-309. https://doi.org/10.1016/0034-4257(88)90106-X

India CSR. (2024). The potential of drone technology in agriculture in India. India CSR.

Jagarlapudi, A. (2016). Real-time smart irrigation system. International Journal of Intelligent Technologies and Applied Statistics, 9(1), 81-95. https://doi.org/10.6148/IJITAS.201603_9(1).0006

Jensen, J. R. (2009). Remote sensing of the environment: An earth resource perspective 2/e. Pearson Education India.

Jensen, T., Scharf, P., Hong, S. Y., & Dobermann, A. (2007). Spatial analysis of field-scale nitrogen management experiments using aerial photography and site-specific management zones. Agronomy Journal, 99(3), 1090-1103. https://doi.org/10.2134/agronj2006.0270

Jones, D. A., & Brown, R. B. (2019). Remote sensing in soil science: Past, present, and future applications. Soil Science Society of America Journal, 83(4), 941-954.

Jones, S. B., Sheng, W., & Weihermüller, L. (2020). Soil moisture measurement methods and applications. In J. H. Dane & G. C. Topp (Eds.), Methods of soil analysis: Part 4 physical methods (pp. 578-596). Soil Science Society of America.

Kang, Z., Wang, J., & Liu, F. (2023). Mapping soil salinity using satellite imagery and machine learning algorithms. Environmental Monitoring and Assessment, 195(2), 38. https://doi.org/10.1007/s10661-023-10792-6

Khatami, R., Mirzavand, M., & Tan, W. (2021). Utilizing drones with thermal and multispectral sensors for precision agriculture in vineyards. Field Crops Research, 263, 108119. https://doi.org/10.1016/j.fcr.2021.108119

Kim, Y., Evans, R. G., & Iversen, W. M. (2017). Remote sensing and control of an irrigation system using a distributed wireless sensor network. IEEE Transactions on Instrumentation and Measurement, 57(7), 1379-1387.

Kong, X., Li, Y., & Liu, L. (2022). Environmental monitoring of soil salinity using electrical conductivity sensors in reclaimed land. Environmental Monitoring and Assessment, 194(3), 183. https://doi.org/10.1007/s10661-022-10063-6

Kumar, P., & Lal, R. (2020). Advances in sensors for monitoring soil health in precision agriculture. Sensors and Actuators B: Chemical, 322, 128708. https://doi.org/10.1016/j.snb.2020.128708

Lal, R. (2001). Soil degradation by erosion. Land Degradation & Development, 12(6), 519-539. https://doi.org/10.1002/ldr.472

Martínez, M., Sánchez, A., & López, J. (2021). Precision fertilization with nutrient sensors in vineyards: A case study. Field Crops Research, 268, 108207. https://doi.org/10.1016/j.fcr.2021.108207

Mulla, D. J. (2013). Twenty-five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering, 114(4), 358-371. https://doi.org/10.1016/j.biosystemseng.2012.08.009

Mutyalamma, A. V., Yoshitha, G., Dakshyani, A., & Padmavathi, B. V. (2020). Smart agriculture to measure humidity, temperature, moisture pH, and nutrient values of the soil using IoT. International Journal of Engineering and Advanced Technology, 9(5), 394-398.

O'Shaughnessy, S. A., &Evett, S. R. (2010). Canopy temperature-based system effectively schedules and controls center pivot irrigation of cotton. Agricultural Water Management, 97(9), 1310-1316. https://doi.org/10.1016/j.agwat.2010.03.012

Papendick, R. I., & Campbell, G. S. (1981). Theory and measurement of water potential. In J. F. Parr, W. Gardner, R., & Elliott, L. F. (Eds.). (Year). Water potential relations in soil microbiology (pp. 1-22). SSSA Special Publication.

Patel, A., Mehta, R., & Verma, S. (2021). Optimization of irrigation systems using soil moisture sensors: A case study. Agricultural Water Management, 244, 106472. https://doi.org/10.1016/j.agwat.2020.106472

Paul, E. A. (2014). Soil Microbiology, Ecology, and Biochemistry (4th ed.). Academic Press.

Press Information Bureau. (2022). Use of drones in agriculture and improving farmers income. Ministry of Agriculture & Farmers Welfare.

Rai, D., Singh, R., & Gupta, S. (2020). Application of nutrient sensors for optimizing fertilizer use in corn farming. Field Crops Research, 253, 107795. https://doi.org/10.1016/j.fcr.2020.107795

Rhoades, J. D., Raats, P. A. C., & Prather, R. J. (1989). Effects of liquid-phase electrical conductivity, water content, and surface conductivity on bulk soil electrical conductivity. Soil Science Society of America Journal, 43(5), 807-813.

Rouse, J. W., Haas, R. H., Schell, J. A., Deering, D. W., & Harlan, J. C. (1974). Monitoring the vernal advancement and retrogradation (greenwave effect) of natural vegetation. NASA/GSFC Type III Final Report.

Saha, D., Mohanty, S., & Chatterjee, S. (2020). Monitoring soil salinity using electrical conductivity sensors in saline soil areas of India. Agricultural Water Management, 231, 105964. https://doi.org/10.1016/j.agwat.2019.105964

Scharf, P. C., & Lory, J. A. (2000). Calibration of remotely sensed corn color to predict nitrogen need. Agronomy Journal, 92(5), 875-883. https://doi.org/10.2134/agronj2000.925875x

Sehgal, A., Singh, G., Quintana, N., Kaur, G., Ebelhar, W., Nelson, K. A., & Dhillon, J. (2023). Long-term crop rotation affects crop yield and economic returns in humid subtropical climate. Field Crops Research, 298, 108952.

Shao, W., Zhang, W., & Xie, Y. (2019). Soil temperature and its impact on rice growth in paddy fields. Field Crops Research, 233, 68-74. https://doi.org/10.1016/j.fcr.2019.01.001

Sharma, R., Singh, A., & Gupta, R. (2021). Role of electrical conductivity sensors in precision irrigation systems for vineyards. Precision Agriculture, 22(6), 1358-1373. https://doi.org/10.1007/s11119-021-09809-3

Sharma, R., Singh, A., & Gupta, R. (2023). Integration of nutrient sensors for real-time monitoring of nitrogen levels in precision agriculture. Agricultural Systems, 189, 103024. https://doi.org/10.1016/j.agsy.2022.103024

Smith, J. R., Johnson, K., & Williams, M. A. (2020). Internet of Things (IoT) in agriculture: Implementing smart soil health monitoring systems. Agricultural Systems, 182, 102857.

Smith, J., & Black, S. (2019). Advanced sensor technologies in precision agriculture: Challenges and opportunities. Journal of Agricultural Technology, 15(3), 155-167.

Smith, M., Jones, P., & Williams, D. (2021). Integration of IoT-based soil moisture sensors with weather forecasting systems for drought management in agriculture. Agricultural Water Management, 256, 106115. https://doi.org/10.1016/j.agwat.2021.106115

Sood, A., Sharma, S., & Yadav, S. (2020). IoT-based soil monitoring system for precision irrigation in vineyards. Sensors, 20(12), 3502. https://doi.org/10.3390/s20123502

Sparks, D. L. (2003). Environmental Soil Chemistry (2nd ed.). Academic Press.

Topp, G. C., Davis, J. L., & Annan, A. P. (1980). Electromagnetic determination of soil water content: Measurements in coaxial transmission lines. Water Resources Research, 16(3), 574-582.

Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127-150. https://doi.org/10.1016/0034-4257(79)90013-0

Van der Meer, F. D., Van der Werff, H. M. A., Van Ruitenbeek, F. J. A., Hecker, C. A., Bakker, W. H., et al. (2012). Multi- and hyperspectral geologic remote sensing: A review. International Journal of Applied Earth Observation and Geoinformation, 14(1), 112-128. https://doi.org/10.1016/j.jag.2011.08.002

Van der Meer, F., Fernández, D., & Rivas, J. (2023). Hyperspectral drone imaging for mapping soil properties and vegetation health. Remote Sensing of Environment, 259, 112382. https://doi.org/10.1016/j.rse.2023.112382

Vaughan, R., Robinson, J., & Price, D. (2022). Satellite remote sensing for large-scale soil moisture monitoring in agriculture. Agricultural Systems, 196, 103257. https://doi.org/10.1016/j.agsy.2022.103257

Wang, Y., & Li, H. (2022). Integrating sensor and remote sensing data for soil health assessment. Environmental Monitoring and Assessment, 194, 128.

Wang, Y., Li, J., & Zhang, L. (2020). Optimizing wheat sowing using soil temperature monitoring systems. Agricultural Systems, 180, 102760. https://doi.org/10.1016/j.agsy.2020.10276

Yilmaz, H., Bahar, S., & Akgül, M. (2021). Hyperspectral drone sensing for soil organic carbon mapping in agricultural fields. Remote Sensing of Environment, 257, 112302. https://doi.org/10.1016/j.rse.2021.112302

Zarco-Tejada, P. J., Hubbard, N., & Loudjani, P. (2014). Precision agriculture: An opportunity for EU farmers—Potential support with the CAP 2014-2020. Joint Research Centre, European Commission.

Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: A review. Precision Agriculture, 13(6), 693-712. https://doi.org/10.1007/s11119-012-9274-5

Zhang, H., Li, Y., & Wang, W. (2021). Use of nutrient sensors for potassium fertilization in wheat fields. Agricultural Systems, 189, 103074. https://doi.org/10.1016/j.agsy.2021.103074

Zhang, Q., Zhao, Y., & Liu, J. (2021). Soil variability mapping for precision agriculture: Techniques and applications. Computers and Electronics in Agriculture, 182, 105938.

Zhao, Y., Zhang, Y., & Li, X. (2022). Using satellite-based multispectral sensors for monitoring soil moisture and organic carbon in agricultural fields. Agricultural Systems, 189, 103254. https://doi.org/10.1016/j.agsy.2021

Zhou, S., Williams, A.P., Lintner, B.R., Berg, A.M., Zhang, Y., et al. (2021) Soil moisture-atmosphere feedbacks mitigate declining water availability in drylands. Nature Climate Change, 11 (1), 38-44, doi:10.1038/s41558-020-00945-z.

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2024-11-29

How to Cite

Huere-Peña, J. L., Castrejon-Valdez, M., Castañeda-Campos, C., Leon-Gomez, R., Mateu-Mateo, W. A., Bautista-Gómez, R., Arostegui-León, E., Dueñas-Jurado, C., Ceenti-Chancha, E. J., Rojas-Felipe, E., & Yaulilahua-Huacho, R. (2024). Innovations in Soil Health Monitoring: Role of Advanced Sensor Technologies and Remote Sensing. Journal of Experimental Biology and Agricultural Sciences, 12(5), 653–667. https://doi.org/10.18006/2024.12(5).653.667

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