¹Aldrin J. Soriano,²Cherry G. Pascion,³ Timothy M. Amado,⁴Edmon O. Fernandez,⁵Nilo M.Arago

^1,2,3,4,5Technological University of the Philippines, Manila, Philippines

The tomato fruiting quality prediction using hydroponics and Machine Learning (ML) focuses on improving tomato quality under a micro-climate setting with the use of various sensors to monitor and analyze the parameters that affect the growth of tomato. This study employed various algorithms such as k-nearest neighbor (KNN), support vector machine (SVM), decision tree, linear regression, and random forest (RF) to find the most appropriate supervised ML algorithm in predicting the tomato fruiting quality. The Random Forest algorithm performs better than the other four ML algorithms at predicting the quality of tomato fruit in the microclimate setup. The RMSE of the Decision Tree is 0.089, the absolute error is 0.040, and the squared correlation is 0.675.

Cherry Tomato; Fruiting Quality Prediction; Hydroponics; Machine Learning; Photoperiod

1) A. L. G. Gorme, Z. C. Gonzaga, O. B. Capuno, R. J. C, S. McDougall, A. D. Goldwater and G. S. Rogers, "Growth And Yield of Tomato (Solanum Lycopercicum L.) as influenced by different soil organic amendments and types of cultivation," Annals of Tropical Research 39 (Supplement B), pp. 116 - 128, 2017.

2) J. Ellamar, "Off-season tomato production: A new technology in tarlac province of Philippines," Acta Horticulturae, vol. 1086, pp. 261-267, 2015.

3) WebMD, "Health Benefits of Cherry Tomatoes," 2021.

4) D. S. Mapa, "Major Vegetables and Rootcrops Quarterly Bulletin," Philippine Statistics Authority, vol. 14, no. 4, p. 10, 2020.

5) P. Sambo, C. Nicoletto, A. Giro, Y. Pii, F. Valentinuzzi, T. Mimmo, P. Lugli, G. Orzes, F. Mazzetto, S. Astolfi, R. Terzano and S. Cesco, "Hydroponic Solutions for Soilless Production Systems: Issues and Opportunities in a Smart Agriculture Perspective," Frontiers in Plant Science, vol. 10, no. 923, 2019.

6) A. C.S., S. S. Suhas G, R. K. Dingra and M. K. Dingra, "Modern Agriculture using Wireless Sensor Network," International Journal of Innovative Technology and Exploring Engineering, vol. 9, no. 4, 2020.

7) K. G. Liakos, P. Busato, D. Moshou and S. Pearson, "Machine Learning in Agriculture: A Review," Sensors, vol. 18, p. 2674, 2018.

8) KDnuggets, "Machine Learning in Agriculture: Applications and Techniques," 2019.

9) B. Alhnaity, S. Pearson, G. Leontidis and S. Kollias, "Using Deep Learning to Predict Plant Growth and Yield in Greenhouse Environments," arXiv, 2019.

10) Z. Hong, "A data-driven approach to soil moisture collection and prediction using a wireless sensor network and machine learning techniques".

11) A. Chlingaryan, S. Sukkarieh and B. Whelan, "Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review," Computers and Electronics in Agriculture, vol. 151, pp. 61-69, 2018.

12) M. Garanayak, G. Sahu, S. N. Mohanty and A. K. Jagadev, "Agricultural Recommendation System for Crops Using Different Machine Learning Regression Methods," International Journal of Agricultural and Environmental Information Systems (IJAEIS), p. 20, 2021.

13) B. Koerner, “Is it better for the environment to wear leather or pleather,” Slate Magazine, vol.3, no. 1, pp. 4–10, 04- Dec-2007. [Online]. Available: https://slate.com/technology/2007/12/is-it-better-for-the-environment-to-wear-leather- or-pleather.html. [Accessed: 04-Dec-2020].

14) "An Introductory Guide to Machine Learning," Apro-software.com, [Online]. Available: https://apro- software.com/machine-learning-agriculture/. [Accessed 6 January 2022].

15) Y. Zhao, L. Gong, Y. Huang and C. Liu, "A review of key techniques of vision-based control for harvesting robot," Computers and Electronics in Agriculture, vol. 127, pp. 311-323, 2016.

16) R. Shamshiri, J. W. Jones, K. R. Thorp, D. Ahmad, H. C. Man and S. Taheri, "Review of optimum temperature, humidity, and vapour pressure deficit Review of optimum temperature, humidity, and vapour pressure deficit," International Agrophysics, vol. 32, pp. 287-302, 2018.

17) T. Fertilizer, "MASTERBLEND Premium Fertilizers," Masterblend International-Tyler Enterprises, 2022. [Online]. Available: https://www.masterblend.com/4-18-38-tomato-formula/. [Accessed 5 May 2022].

VOLUME 06 ISSUE 07 JULY 2023

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