Study of the Effect of Physical Parameters on Commercial Hydroponics Based on Internet of Things (IoT): A Case Study of Bok Coy Plants (Brassica rapa) and Water Spinach (Ipomoea Aquatica)
DOI:
https://doi.org/10.5614/j.math.fund.sci.2022.54.2.5Keywords:
bok coy, hydroponics, IoT, machine learning, water spinachAbstract
Population growth causes the demand for food to increase. One solution that can be applied is agriculture with hydroponic technology. To increase production efficiency, one must know the physical parameters that most influence the production process. This research used an IoT system to gather accurate and precise measurement data of physical parameters to be used as a dataset for machine learning. The dataset consisted of light intensity, humidity, air temperature, and total dissolved solids (TDS). Plant growth was measured by leaf area of the plant, number of leaves, and plant stem length every 3 to 4 days. The models used in the machine learning process were linear regression, polynomial regression, and random forest regression. The machine learning results showed that the best model for predicting plant growth was random forest regression with an MAE of 8.3% and an R2 of 0.93, for both bok coy and water spinach. The variables that influence growth the most are TDS and light intensity. According to the relationship between TDS gradient and plant growth gradient, the most optimal growth can be achieved by raising the TDS gradient or by maintaining a high TDS, which can be achieved by adding nutrient solution to the tank regularly.
References
Bandung Regency in Figures 2021, Badan Pusat Statistik Kabupaten Bandung. https://apindojabar.or.id/, (2021).
S. Eshkabilov et al. Hyperspectral imaging techniques for rapid detection of nutrient content of hydroponically grown lettuce cultivars. Comput. Electron. Agric. 181. 2021.
Jones Jr, J. Benton, Hydroponics: a practical guide for the soilless grower, Second edition, CRC Press, 2014.
Zhang, H., Asutosh, A. & Hu, W., Implementing vertical farming at university scale to promote sustainable communities: a feasibility analysis. Sustainability, 10(12), pp. 4429, 2018.
Srinidhi, H. K., Shreenidhi, H. S., & Vishnu, G. S, Smart Hydroponics system integrating with IoT and Machine learning algorithm. In 2020 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), pp. 261-264. IEEE, 2020.
Crisnapati, P. N., Wardana, I. N. K., Aryanto, I. K. A. A., & Hermawan, A, Hommons: Hydroponic management and monitoring system for an IOT based NFT farm using web technology. In 2017 5th International Conference on Cyber and IT Service Management (CITSM), pp. 1-6. IEEE, 2017.
Gertphol, S., Chulaka, P., & Changmai, T., Predictive models for lettuce quality from internet of things-based hydroponic farm. In 2018 22nd International Computer Science and Engineering Conference (ICSEC), pp. 1-5. IEEE, 2018.
Sambo, P., Nicoletto, C., Giro, A., Pii, Y., Valentinuzzi, F., Mimmo, T., ... & Cesco, S., Hydroponic solutions for soilless production systems: Issues and opportunities in a smart agriculture perspective. Frontiers in Plant Science, 10, pp. 923, 2019.
Mohmed, G., Grundy, S., Lotfi, A., & Lu, C., Using AI Approaches for Predicting Tomato Growth in Hydroponic Systems. In UK Workshop on Computational Intelligence, pp. 277-287, 2021.
Sarala, S. M., Robust Smart Irrigation System using Hydroponic Farming based on Data Science and IoT. In 2020 IEEE Bangalore Humanitarian Technology Conference (B-HTC), pp. 1-4. IEEE, 2020.
Bahga, A., & Madisetti, V., Internet of Things: A hands-on approach. Vpt, 2015.
How to Set Up a Nutrient Film Technique (NFT) Hydroponic System?, https://hydroponicway.com/how-to-nft-hydroponics, (25 December 2022).
What Does Pak Choi Taste Like?, Thrive Cuisine, (13 December 2022).
Growing kangkong in water, curiousgardener.com, (13 December 2022).
Kangkungking.com, (13 December 2022).
Jeannie, C. & Berlyn, G., Light, Moisture, and Nutrient Use by Plants, The Silvicultural Basis For Agroforestry Systems, mountainhightree.com, (13 December 2022).
Dht11 dht22 sensors temperature and humidity tutorial using Arduino, https://howtomechatronics.com/tutorials/arduino, (13 December 2022).
Chooruang, K. and Meekul, K., Design of an IoT Energy Monitoring System, 16th Int. Conf. on ICT and Knowledge Engineering (ICT&KE), pp. 1-4, 2018.
Albi?a, A., Seli?teanu, D. & M?muleanu, M., Using MQTT Protocol for Remote Monitoring of Low and Medium Power Electrical Network, 2021 16th International Conference on Engineering of Modern Electric Systems (EMES), pp. 1-4, 2021.
Chicco, D., Warrens, M. J. & Jurman, G, The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, 2021.
Blankenship, R. E. Molecular Mechanisms of Photosynthesis, Blackwell Publishing: Williston, VT, USA, 2021.
Luckyardi, S., Mega, R. U., Rafdhi, A. A., Nandiyanto, A. B. D., Hirawan, D. &Soegoto,E. S., Smart Monitoring of Nutrient Content, pH Condition and Temperature in Vegetable Leaf Grown through Deep Flow Technique. Moroccan Journal of Chemistry, 9(4), pp. 842-855, 2021.
Patil, N., Patil, S., Uttekar, A. & Suryawanshi, A. R., Monitoring of Hydroponics System using IoT Technology. International Research Journal of Engineering and Technology (IRJET). 07(06) pp. 1455-1458. 2020.
Eridani, D., Wardhani, O. & Widianto, E. D., Designing and implementing the arduino-based nutrition feeding automation system of a prototype scaled nutrient film technique (NFT) hydroponics using total dissolved solids (TDS) sensor, 2017 4th International Conference on Information Technology Computer and Electrical Engineering (ICITACEE), pp. 170-175, 2017.
