Technological Influences on Monitoring and Automation of the Hydroponics System

Comparative analysis of data using machine learning algorithms: A hydroponics system use case

“…Figs. 10,11,12,13 show the predicted Onion bulb diameter versus the original Onion bulb diameter for the AER, AG, Floating & NFT hydroponic systems respectively, it can be inferred that the four hydroponic systems performed very well with regards to the predictions of the Onion bulb diameters, the AER, AG, floating hydroponic system had more dataset points which are not a match to the original onion bulb diameter in their system but the NFT hydroponic system produced an optimal prediction performance as can be seen in Fig. 13 where only two dataset points of the predicted Onion bulb diameter are not a match to the original Onion bulb diameter, therefore the NFT hydroponic systems are the preferred system for the Onion bulb diameter predictions using the XGBoost model.…”

“…Their approach has not been tested on crops grown using rain or recycled water to ascertain its performance. The authors in [11] have discussed that hydroponics farming has improved the growth of plants in a controlled system, their work has discussed the total leaf length to stem Diameter ratio where multiple input variables are considered for a hybrid set of machine learning and neural networks algorithm. However, a comparative performance of technology impact on hydroponic and soil cultivated crops has not been conducted.…”