IoT-driven environmental optimization for hydroponic lettuce: A data-centric approach to smart agriculture
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Abstract
An IoT sensor network enables real-time monitoring of key environmental parameters, including temperature, humidity, nutrient solution pH, and concentration. This system employs a rule-based expert system with dynamic threshold adjustments for automated control. Over a 35-day growth cycle (Days After Transplanting - DAT), the experiment revealed statistically significant improvement in lettuce growth within the smart indoor farming system compared to outdoor farming. Average increases were observed: 17.1% in plant weight, 16.9% in plant height, and 20.0% in the number of leaves. The IoT-based control system robustly maintained environmental parameters within optimal ranges, creating a stable and conducive growth environment. This approach highlights the transformative potential of integrating IoT and intelligent control logic for optimizing indoor hydroponic crop production, paving the way for more efficient and sustainable agriculture. The findings offer insights for future smart farming developments by demonstrating how IoT and intelligent control improve hydroponic lettuce yield.
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References
Bilal, M., Tayyab, M., Hamza, A., Shahzadi, K., & Rubab, F. (2023). The internet of things for smart farming: Measuring productivity and effectiveness. 58(1), 106. https://doi.org/10.3390/ecsa-10-16012
Chowdhury, H., & Asiabanpour, B. (2024). Influencing factors for the plant growth patterns in hydroponic and aquaponic environments: A subgroup analysis for sustainable agricultural production. Green Technologies and Sustainability, 2(2), 100084–100084. https://doi.org/10.1016/j.grets.2024.100084
Deswina, P., & Priadi, D. (2020). Development of Arrowroot (Maranta arundinacea L.) as functional food based of local resource. IOP Conference Series Earth and Environmental Science, 439(1), 12041–12041. https://doi.org/10.1088/1755-1315/439/1/012041
Dhal, S. B., Jungbluth, K., Lin, R. T. P., Sabahi, S. P., Bagavathiannan, M., Braga-Neto, U., & Kalafatis, S. (2022). A machine-learning-based IoT system for optimizing nutrient supply in commercial aquaponic operations. Sensors, 22(9), 3510–3510. https://doi.org/10.3390/s22093510
Dhal, S. B., Mahanta, S., Gumero, J., O’Sullivan, N., Soetan, M., Louis, J., Gadepally, K. C., Mahanta, S., Lusher, J., & Kalafatis, S. (2023). An IoT-based data-driven real-time monitoring system for control of heavy metals to ensure optimal lettuce growth in hydroponic Set-Ups. Sensors, 23(1). https://doi.org/10.3390/s23010451
Dutta, M., Gupta, D., Sahu, S., Limkar, S., Singh, P., Mishra, A., Kumar, M., & Mutlu, R. (2023). Evaluation of growth responses of lettuce and energy efficiency of the substrate and smart hydroponics cropping system. Sensors, 23(4), 1875–1875. https://doi.org/10.3390/s23041875
Fidelis, I. S., & Idim, I. A. (2020). Design and implementation of solar powered automatic irrigation system. American Journal of Electrical and Computer Engineering, 4(1), 1–9. https://doi.org/10.11648/j.ajece.20200401.11
Hebert, D., Boonekamp, J., Parrish, C. H., Ramasamy, K., Makarov, N. S., Castañeda, C., Schuddebeurs, L., McDaniel, H., & Bergren, M. R. (2022). Luminescent quantum dot films improve light use efficiency and crop quality in greenhouse horticulture. Frontiers in Chemistry, 10. https://doi.org/10.3389/fchem.2022.988227
Huang, Y., Ni, Z., Chang, Y. H., & Wang, L. (2024). Hydroponic lettuce in-situ water circulation evaluation via nondestructive mass measurement in controlled environment. Frontiers in Plant Science, 15. https://doi.org/10.3389/fpls.2024.1385191
Islam, M. R., Chowdhury, M. A. H., Saha, B. K., & Hasan, M. M. (2014). Integrated nutrient management on soil fertility, growth and yield of tomato. Journal of the Bangladesh Agricultural University, 11(1), 33–40. https://doi.org/10.3329/jbau.v11i1.18204
Lakhiar, I. A., Gao, J., Syed, T. N., Chandio, F. A., Buttar, N. A., & Qureshi, W. (2018, December). Monitoring and control systems in agriculture using intelligent sensor techniques: A review of the aeroponic system. Journal of Sensors, 2018, 1-18. https://doi.org/10.1155/2018/8672769
Liu, Z., & Xu, Q. (2018). An automatic irrigation control system for soilless culture of lettuce. Water, 10(11), 1692–1692. https://doi.org/10.3390/w10111692
Martini, B., Helfer, G. A., Barbosa, J. L. V., Modolo, R. C. E., Silva, M. R. da, Figueiredo, R. M. de, Mendes, A. S., Silva, L. A., & Leithardt, V. R. Q. (2021). IndoorPlant: A model for intelligent services in indoor agriculture based on context histories. Sensors, 21(5), 1631–1631. https://doi.org/10.3390/s21051631
Miller, A., Langenhoven, P., & Nemali, K. (2020). Maximizing productivity of greenhouse-grown hydroponic lettuce during winter. HortScience, 55(12), 1963–1969. https://doi.org/10.21273/hortsci15351-20
Nikolov, N. V., Atanasov, A. Z., Evstatiev, B., Vlăduț, V., & Biriş, S. Ş. (2023). Design of a small-scale hydroponic system for indoor farming of leafy vegetables. Agriculture, 13(6), 1191–1191. https://doi.org/10.3390/agriculture13061191
Nikose, P. C., & Mehare, J. P. (2023). Monitoring and controlling hydroponic units using IoT. International Journal For Multidisciplinary Research, 5(3). https://doi.org/10.36948/ijfmr.2023.v05i03.4167
Ogbolumani, O., & Mabaso, B. (2023). An IoT-based hydroponic monitoring and control system for sustainable food production. Journal of Digital Food, Energy & Water Systems, 4(2). https://doi.org/10.36615/digital_food_energy_water_systems.v4i2.2873
Oudah, M., Al‐Naji, A., Al-Janabi, T. Y., Namaa, D. S., & Chahl, J. (2024). Automatic irrigation system based on computer vision and an artificial intelligence technique using Raspberry Pi. Automation, 5(2), 90–105. https://doi.org/10.3390/automation5020007
Prakash, C., Singh, L. P., Gupta, A., & Lohan, S. K. (2023). Advancements in smart farming: A comprehensive review of IoT, wireless communication, sensors, and hardware for agricultural automation. Sensors and Actuators A Physical, 362, 114605–114605. https://doi.org/10.1016/j.sna.2023.114605
Qomariyah, L., Febriyanti, M. N., Wibowo, E. N., Risqiyani, A. K., Suluki, M. H., & Faridah, F. N. (2022). Realizing family food independence through the urban farming concept. Community Empowerment, 7(3), 474–482. https://doi.org/10.31603/ce.6059
Siregar, S., Sari, M. I., & Jauhari, R. (2016). Automation system hydroponic using smart solar power plant unit. Jurnal Teknologi, 78(5-7). https://doi.org/10.11113/jt.v78.8713
Soussi, A., Zero, E., Sacile, R., Trinchero, D., & Fossa, M. (2024, April). Smart sensors and smart data for precision agriculture: A review. Sensors, 24(8), 2647. https://doi.org/10.3390/s24082647
Suresh, V., Logasundari, T., Sravani, V., Ali, S. M., & Srinivasan, S. (2024). IOT based automated indoor hydroponic farming system. E3S Web of Conferences, 547, 02002. https://doi.org/10.1051/e3sconf/202454702002
Tatas, K., Al-Zoubi, A., Christofides, N., Zannettis, C., Chrysostomou, M., Panteli, S., & Antoniou, A. (2022). Reliable IoT-based monitoring and control of hydroponic systems. Technologies, 10(1), 26. https://doi.org/10.3390/technologies10010026
Tzounis, A., Katsoulas, Ν., Bartzanas, T., & Kittas, C. (2017). Internet of Things in agriculture, recent advances and future challenges. Biosystems Engineering, 164, 31–48. https://doi.org/10.1016/j.biosystemseng.2017.09.007
Gaikwad, D., Ravindranath, N., Panda, A., & Patnaik, S. (2024). Urban hydroponics: Growing fresh food in the city. In. M. Sairam, S. DT, D. J. Gaikwad & S. Maitra (Eds.), Advances in modern agricultural practices (pp. 129-144). New Delhi Publishers.
Widiastuti, D. P., Hatta, M., Aziz, H., Permana, D., Santari, P. T., Rohaeni, E. S., Ahmad, S. N., Bakrie, B., Tan, S. S., & Rakhmani, S. I. W. (2024). Peatlands management for sustainable use on the integration of maize and cattle in a circular agriculture system in West Kalimantan, Indonesia. Heliyon, 10(10), e31259. https://doi.org/10.1016/j.heliyon.2024.e31259
Zhao, X., Peng, J., Zhang, L., Yang, X., Qiu, Y., Cai, C., Hu, J., Huang, T., Liang, Y., Li, Z., Tian, M., Liu, F., & Wang, Z. (2024). Optimizing the quality of horticultural crop: Insights into pre-harvest practices in controlled environment agriculture. Frontiers in Plant Science, 15. https://doi.org/10.3389/fpls.2024.1427471
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