Abstract
A comparison of leafy green spinach species growth rates in two different hydroponic systems was performed in a controlled environment. The integration of several sensors to monitor the parameters of plant growth has been deployed using the Internet of things (IoT) technology. Intelligent models to predict the plant growth in the hydroponic system are necessary for better decision making in controlling the parameter during plant growth. This research compares the plant growth dynamics in deep water culture (DWC) and nutrient film technique (NFT) systems. The results demonstrate efficient plant growth in the NFT system compared to DWC in terms of height and number of leaves. The study also discusses the observations during the growth time to analyze the most suitable hydroponic structure for spinach growth. The growth prediction is implemented using an ensemble classifier model, which gives an accuracy rate above 79% on DWC and 64% on the NFT dataset based on binary classification.
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References
Puno, J.C.V., Haban, J.J.I., Alejandrino, J.D., Bandala, A.A., Dadios, E.P.: Design of a nutrient film technique hydroponics system with fuzzy logic control. In: 2020 IEEE Region 10 Conference (TENCON), pp. 403–408. IEEE (2020)
Al-Tawaha, A.R., Al-Karaki, G., Al-Tawaha, A.R., Sirajuddin, S.N., Makhadmeh, I., Wahab, P.E.M., Youssef, R.A., Al Sultan, W., Massadeh, A.: Effect of water flow rate on quantity and quality of lettuce (Lactuca sativa L.) in nutrient film technique (NFT) under hydroponics conditions. Bul. J. Agric. Sci. 24(5), 791–798 (2018)
Lakhiar, I.A., Jianmin, G., Syed, T.N., Chandio, F.A., Buttar, N.A., Qureshi, W.A.: Monitoring and control systems in agriculture using intelligent sensor techniques: a review of the aeroponic system. J. Sens. 2018 (2018)
Chowdhury, M.E.H., Khandakar, A., Ahmed, S., Al-Khuzaei, F., Hamdalla, J., Haque, F., Reaz, M.B.I., Al Shafei, A., Al-Emadi, N.: Design, construction and testing of Iot based automated indoor vertical hydroponics farming test-bed in Qatar. Sensors 20(19), 5637 (2020)
Nguyen, T.P.D., Tran, T.T.H., Nguyen, Q.T.: Effects of light intensity on the growth, photosynthesis and leaf microstructure of hydroponic cultivated spinach (Spinacia oleracea L.) under a combination of red and blue LEDs in house. Int. J. Agric. Tech. 15(1), 75–90 (2019)
Maneejantra, N., Tsukagoshi, S., Lu, N., Supoaibulwatana, K., Takagaki, M., Yamori, W.: A quantitative analysis of nutrient requirements for hydroponic spinach (Spinacia oleracea L.) production under artificial light in a plant factory. J. Fertilizers Pesticides 7(12), 2–5 (2016)
Doty, S., Dickson, R.W., Evans, M.: Evaluation of a novel shallow aggregate Ebb-and-flood culture system and transplant size effects on hydroponic basil yield. HortTechnology 1(aop), 1–8 (2020)
Lennard, W., Ward, J.: A comparison of plant growth rates between an NFT hydroponic system and an NFT aquaponic system. Horticulturae 5(2), 27 (2019)
Helmy, H., Janah, D.A.M., Nursyahid, A., Mara, M.N., Setyawan, T.A., Nugroho, A.S.: Nutrient solution acidity control system on NFT-based hydroponic plants using multiple linear regression method. In: 2020 7th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), pp. 272–276. IEEE (2020)
Mashumah, S., Rivai, M., Irfansyah, A.N.: Nutrient film technique based hydroponic system using fuzzy logic control. In: 2018 International Seminar on Intelligent Technology and Its Applications (ISITIA), pp. 387–390. IEEE (2018)
Mehta, M., Chawla, P., Jot, G.: Farming of spinach and lettuce in hydroponics system with IoT. Int. J. Adv. Sci. Technol. 29(10s), 1743–1762 (2020)
Yue, S.J., Hairu, C., Hanafi, M., Shafie, S.M., Salim, N.A.: IoT based automatic water level and electrical conductivity monitoring system. In: 2020 IEEE 8th Conference on Systems, Process and Control (ICSPC), Melaka, Malaysia, pp. 95–100 (2020), http://doi.org/10.1109/ICSPC50992.2020.9305768
Majid, M., Khan, J.N., Shah, Q.M.A., Masoodi, K.Z., Afroza, B., Parvaze., S.: Evaluation of hydroponic systems for the cultivation of lettuce (Lactuca sativa L., var. Longifolia) and comparison with protected soil-based cultivation. Agric. Water Manag. 245, 106572 (2021)
Yasrab, R., Zhang, J., Smyth, P., Pound, M.P.: Predicting plant growth from time-series data using deep learning. Remote Sens. 13(3), 331 (2021)
Khudoyberdiev, A., Ahmad, S., Ullah, I., Kim, D.H.: An optimization scheme based on fuzzy logic control for efficient energy consumption in hydroponics environment. Energies 13(2), 289 (2020)
Kim, N., Ha, K.-J., Park, N.-W., Cho, J., Hong, S., Lee, Y.-W.: A comparison between major artificial intelligence models for crop yield prediction: case study of the midwestern United States, 2006–2015. ISPRS Int. J. Geo Inf. 8(5), 240 (2019)
Gashgari, R., Alharbi, K., Mughrbil, K., Jan, A., Glolam, A.: Comparison between growing plants in hydroponic system and soil based system. In: Proceedings of the 4th World Congress on Mechanical, Chemical, and Material Engineering, pp. 1–7. ICMIE, Madrid, Spain (2018)
RodrÃguez-Pérez, R., Vogt, M., Bajorath, J.: Support vector machine classification and regression prioritize different structural features for binary compound activity and potency value prediction. ACS Omega 2(10), 6371–6379 (2017)
Ilyas, Q.M., Ahmad, M.: An enhanced ensemble diagnosis of cervical cancer: a pursuit of machine intelligence towards sustainable health. IEEE Access 9, 12374–12388 (2021). https://doi.org/10.1109/ACCESS.2021.3049165
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Srivani, P., Yamuna Devi, C.R., Manjula, S.H. (2022). Prediction and Comparative Analysis Using Ensemble Classifier Model on Leafy Vegetable Growth Rates in DWC and NFT Smart Hydroponic System. In: Senjyu, T., Mahalle, P., Perumal, T., Joshi, A. (eds) IOT with Smart Systems. Smart Innovation, Systems and Technologies, vol 251. Springer, Singapore. https://doi.org/10.1007/978-981-16-3945-6_78
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DOI: https://doi.org/10.1007/978-981-16-3945-6_78
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