Tomato Fruiting Quality Prediction Using Hydroponics and Machine Learning
Journal Title: International Journal of Multidisciplinary Research and Analysis - Year 2023, Vol 6, Issue 07
Abstract
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.
Authors and Affiliations
Aldrin J. Soriano, Cherry G. Pascion, Timothy M. Amado, Edmon O. Fernandez, Nilo M. Arago
Keywords
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- EP ID EP719214
- DOI 10.47191/ijmra/v6-i7-50
- Views 65
- Downloads 0
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