Hydroponic using Wick System for Herbal Cultivation using Random Forest Algorithm Comparing with Sift Algorithm

This study implements machine learning algorithms, including SIFT and Random Forest techniques, to monitor pH levels and fertilizers for optimal growth of hydroponic plants. The Random Forest Algorithm and the SIFT Algorithm function in conjunction. A collection of resources from Kaggle is used in this research. The Wick System enables the cultivation of cabbage in water, and the collection comprises instances of crops cultivated in various hydroponic settings and methods. In this study, two groups employed different methods to verify the authenticity of the images. Both Group 1 and Group 2 applied SIFT to a total of forty samples, with twenty samples in each group. The research utilized clinical data from clincalc.com and an 80% G-power value. A significance level of 0.05 was chosen, along with a type II error rate of 0.2. The Random Forest algorithm achieved an accuracy of 90.15%, while SIFT exhibited errors 62.2% of the time. A two-tailed significance test yielded a p-value of 0.001 (p < 0.05). A one-step power analysis was conducted to determine the optimal group size for the task. The algorithms were correct 95% of the time, making them an effective means to assess the efficacy of the automated procedures. Implementing the SIFT and Random Forest algorithms on the Wick System for hydroponic plant development reveals that pH and environmental conditions are critical for growth. This research study discusses the Wick System's environmental considerations, model comparison, real-time data resource economy, and the application of the SIFT and Random Forest algorithms.