An IoT-based soil analysis system using optical sensors and multivariate regression
Abstract
Food is the primary requirement for the survival of any living being on this planet. The rapid increment in the population is a major concern for adequate food production due to the depletion of agricultural land, which has turned into housing societies. However, agriculture is India's main business and primary income source for the farmers. The agricultural crop yield mainly depends upon the physical parameters of the soil, such as micronutrients and pH values. The main constraint in monitoring these parameters is the location of land at the far remote places and it takes enough time to test these parameters following the lab test process. The real-time analysis of all the parameters remained a big challenge for the farm owner, so the soil fertility level could not be sustained at the optimum level during most of the crop production cycle. This ultimately results in the average level of crop production and becomes a matter of chance since the soil fertility and other parameters barely suit the crop type under cultivation. This paper mainly focuses on developing an Internet of Things (IoT) based digital method to measure the availability of soil macronutrients and their pH using a color optical sensor TCS3200 and transmit those parameters to a long distance in case of unavailability of any telecommunication network. The paper also describes the deployment of Long Range (LoRa) units interfaced with ESP8266 for long-distance communication and uploading the entire information over the cloud platform, which will be displayed over the mobile using an API. The average accuracy of the proposed method in determining the soil macronutrients was 0.969 for phosphorus, 0.953 for nitrogen, 0.961 for potassium, and 0.921 for Soil pH.
References
Ahmed, U., Lin, J.C.W., Srivastava, G., & Djenouri, Y. (2021). A nutrient recommendation system for soil fertilization based on evolutionary computation. Comput. Electron. Agric., 189, 106407. https://doi.org/10.1016/j.compag.2021.106407
Ahmed, E., Yaqoob, I., Hashem, I.A.T., Khan, I., Ahmed, A.I.A., Imran, M., & Vasilakos, (2017) A.V. The role of big data analytics in Internet of Things. Comput. Netw., 129, 459–471. https://doi.org/10.1016/j.comnet2017.06.013
Atreya, K., Sharma, S., Bajracharya, R. M., & Rajbhandaric, N. P. (2008). Developing a sustainable agro-system for central Nepal using reduced tillage and straw mulching. Journal of Environmental Management, 88(3), 547-555. https://doi.org/10.1016/j.jenvman.2007.03.017
Akhter, R., & Sofi, S.A. (2022). Precision agriculture using IoT data analytics and machine learning. J. King Saud Univ. Comput. Inf. Sci., 34(8), 5602-5618. https://doi.org/10.1016/j.jksuci.2021.05.013
ATSDR. (2013). Case Studies in Environmental medicine: nitrate/nitrite toxicity. Agency for Toxic Substances and Disease Registry Atlanta.
Bouma, J., & Finke, P.A. (1993). Origin and nature of soil resource variability. In
Proceedings of the Soil Specific Crop Management, Minneapolis, U.S.A., pp. 3-13. https://doi.org/10.2134/1993.soilspecificcrop.c1
Dagar, R., Som, S., & Khatri, S.K. (2018). Smart farming–IoT in agriculture. In Proceedings of the International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India. https://doi.org /10.1109/ICIRCA.2018.8597264
Faber, B. A., Downer, A. J., Holstege, D., & Mochizuki, M. J. (2007). Accuracy Varies for Commercially Available Soil Test Kits Analyzing Nitrate–Nitrogen, Phosphorus, Potassium, and pH. Hort. Technology, 17(3), 358-362. https://doi.org/10.21273/HORTTECH.17.3.358
Fan, W., Kam, K.A., Zhao, H., Culligan, P.J., & Kymissis, I. (2022). An Optical Soil Sensor for NPK Nutrient Detection in Smart Cities. In Proceedings of the 18th International Conference on Intelligent Environments (IE), Biarritz, France, pp. 1–4. https://doi.org/10.1109/IE54923.2022.9826759
Gliessman, S. R. (1985). Multiple cropping systems: A basis for developing an alternative agriculture. In Innovative biological technologies for lesser developed countries, Proceedings of workshop. OTA, Washington, DC, U.S.A., pp. 69-83.
Hue, N.V., Uchida, R., & Ho, M.C. (2000). Sampling and Analysis of Soils and Plant Tissues How to Take Representative Samples, How the Samples are Tested. Plant Nutrient Management in Hawaii’s Soils: Approaches for Tropical and Subtropical Agriculture. Honolulu, University of Hawaii, pp. 23-30.
Kim, Y.J., Kim, H.J., Ryu, K.H., & Rhee, J.Y. (2008). Fertiliser application performance of a variable-rate pneumatic granular applicator for rice production. Biosystems Engineering, 100(4), 498–510. https://doi.org/10.1016/j.biosystemseng.2008.05.007
Kashyap, B., & Kumar, R. (2021). Sensing Methodologies in Agriculture for Soil Moisture and Nutrient Monitoring. IEEE Access, 9, 14095–14121. https://doi.org/10.1109/ACCESS.2021.3052478
Lu, Y., Liu, M., Li, C., Liu, X., Cao, C., & Li, X. (2022). Kan, Z. Precision Fertilization and Irrigation: Progress and Applications. Agri. Engineering, 4, 626–655. https://doi.org/10.3390/agriengineering4030041
Lee, M., Hwang, J., & Yoe, H. (2013). Agricultural Production System based on IoT. IEEE 16th International Conference on Computational Science and Engineering, Sydney, NSW. pp. 833-837. https://doi.org/10.1109/CSE.2013.126
Laboski, C. A. M., Peters, J. B., & Bundy, L. G. (2006). Nutrient application guidelines for field, vegetable, and fruit crops in Wisconsin. Division of Cooperative Extension of the University of Wisconsin-Extension, Madison, Wisconsin, U.S.A.
Mandal, A., Dhaliwal, S.S., Mani, P.K., & Toor, A.S. (2021). Chapter 2—Conservation Agricultural Practices under Organic Farming. In Advances in Organic Farming; Woodhead Publishing, Sawston, UK. ISBN 978-0-12-822358-1. pp. 17–37. https://doi.org/10.1016/B978-0-12-822358-1.00014-6
Potdar, R.P., Shirolkar, M.M., Verma, A.J., More, P.S., & Kulkarni, A. (2021). Determination of soil nutrients (NPK) using optical methods: a mini review. Journal of Plant Nutrition, 44(12), 1826-1839. https://doi.org/10.1080/01904167.2021.1884702.
Reisenauer, H.M. (ed.). (1978). Soil and plant tissue-testing in California. Div. Agr. Sci., Univ. Calif. Bul., 1879.
Sowmya, S.L.K., Rana, M., Ahmed, S.T., & Anitha, K. (2021). Real-Time IoT Based Temperature and NPK Monitoring System Sugarcane-Crop Yield for Increasing. In Proceedings of the Innovations in Power and Advanced Computing Technologies (i-PACT), Kuala Lumpur, Malaysia, pp. 1–5. https://doi.org/10.1109/i-PACT52855.2021.9696564
Sivakumar, R., Prabadevi, B., Velvizhi, G., Muthuraja, S., Kathiravan, S., Biswajita, M., & Madhumathi, A. (2022). Internet of Things and Machine Learning Applications for Smart Precision Agriculture. In IoT Applications Computing, IntechOpen: London, UK, pp. 135. https://doi.org/10.5772/intechopen.97679
Sun, B., Zhou, S., & Zhao, Q. (2003). Evaluation of spatial and temporal changes of soil
quality based on geostatistical analysis in the hill region of subtropical China. Geoderma, 115(1-2), 85-99. https://doi.org/10.1016/S0016-7061(03)00078-8
Staben, M. L., Ellsworth, J.W., Sullivan, D.M., Horneck, D., Brown, B.D., & Stevens, R.G. (2003). Monitoring Soil Nutrients using a Management Unit Approach. OSU Extension Service, PNW 570-E, Oregon State University, Oregon, U.S.A.
Tung, P.G.A., Yusoff, M.K., Majid, N.M., Joo, G.K., & Huang, G.H. (2009). Effect of N and K fertilizers on nutrient leaching and groundwater quality under mature oil palm in Sabah during the monsoon period. American Journal of Applied Sciences, 6(10), 1788–1799. https://doi.org/10.3844/ajassp.2009.1788.1799
Tuli, A., Hasteer, N., Sharma, M., & Bansal, A., (2014). Framework to leverage cloud for the modernization of the Indian agriculture system. IEEE International Conference on Electro/Information Technology, Milwaukee, WI, pp. 109-115. https://doi.org/10.1109/EIT.2014.6871748
Wang, C., Bi, Z., & Da, Xu, L., (2014). IoT and cloud computing in automation of assembly modeling systems. IEEE Transactions on Industrial Informatics, 10(2,) 1426-1434. https://doi.org/10.1109/TII.2014.2300346
Wang, Z. M., Song, K. S., Zhang, B., Liu, D. W., Li, X. Y., Ren, C. Y., Zhang, S. M., Luo, L., & Zhang, C. H. (2009). Spatial variability and affecting factors of soil nutrients in croplands of Northeast China: A case study in Dehui County. Plant Soil and Environment, 55(3) 110-120. https://doi.org/10.17221/323-PSE
Wei, H., Wang, X., Li, Y., Yang, J., Wang, J., Lü, X., & Han, X. (2020). Simulated nitrogen deposition decreases soil microbial diversity in a semiarid grassland, with little mediation of this effect by mowing. Pedobiologia, 80, 150644. https://doi.org/10.1016/j.pedobi.2020.150644
Wittry, D.J., & Mallarino, A.P. (2004). Comparison of uniform and variable rate phosphorus fertilizer for corn-soybean rotation. Agronomy Journal, 96(1), 26–33. https://doi.org/10.2134/AGRONJ2004.2600
WHO. (2009). Potassium in drinking-water: background document for development of WHO guidelines for drinking-water quality. Geneva: World Health Organization, pp. 2–4. https://apps.who.int/iris/handle/10665/70171
Zhu, W., Rezaei, E.E., Nouri, H., Yang, T., Li, B., Gong, H., Lyu, Y., Peng, J., & Sun, Z. (2021). Quick Detection of Field-Scale Soil Comprehensive Attributes via the Integration of UAV and Sentinel-2B Remote Sensing Data. Remote. Sens., 13, 4716. https://doi.org/10.3390/rs13224716
Zamora-Izquierdo, M.A., Santa, J., Martínez, J.A., Martínez, V., & Skarmeta, A.F. (2019). Smart farming IoT platform based on edge and cloud computing. Biosyst. Eng., 177, 4–17. https://doi.org/10.1016/j.biosystemseng.2018.10.014
Zhang, X.Y., Sui, Y.Y., Zhang, X.D., Meng, K., & Herbert, S. J., (2007). Spatial variability of nutrient properties in black soil of Northeast China. Pedosphere., 17(1) 19-29. https://doi.org/10.1016/S1002-0160(07)60003-4
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