Machine Learning Approach to Remove Ion Interference Effect in Agricultural Nutrient Solutions

Abstract

High concentration agricultural facilities such as vertical farms or plant factories consider hydroponic techniques as optimal solutions. Although closed-system dramatically reduces water consumption and pollution issues, it has ion-ratio related problem. As the root absorbs individual ions with different rate, ion rate in a nutrient solution should be adjusted periodically. But traditional method only considers pH and electrical conductivity to adjust the nutrient solution, leading to ion imbalance and accumulation of excessive salts. To avoid those problems, some researchers have proposed ion-balancing methods which measure and control each ion concentration. However, those approaches do not overcome the innate limitations of ISEs, especially ion interference effect. An anion sensor is affected by other anions, and the error grows larger in higher concentration solution. A machine learning approach to modify ISE data distorted by ion interference effect is proposed in this paper. As measurement of TDS value is relatively robust than any other signals, we applied TDS as key parameter to build a readjustment function to remove the artifact. Once a readjustment model is established, application on ISE data can be done in real time. Readjusted data with proposed model showed about 91.6 ~ 98.3% accuracies. This method will enable the fields to apply recent methods in feasible status.

Pan-artifact Removing with Deep Learning, on ISEs

“…Our previous work with quadratic regression model [11] was applied on the experiment data for comparison. It failed to remove artifacts and even increased error dramatically.…”

“…Liberti et al provided readjustment method with Gran's plots to remove ion interference effect but it also require prior knowledge of ions. [10] We suggested a machine learning approach to remove the ion interference effect performing 91.5~97.8% accuracy on test cases [11], but failed to remove the artifact from movement of solution. A distorted signal caused by drop of water made the regression fail.…”

Nutrient Solution Management System for Smart Farms and Plant Factory

“…We applied multiple chemicals at same solvent for two reason. At first, we can directly apply machine learning algorithms for removal of ion interference effect [11,12] on the mixture of solutes. Secondly, calibration on the mixture of solutes can reduce the magnitude of artifacts because the ion interference effect is not a random value.…”

“…The magnitude of this artifact was measured as 9~41%. [4] This artifact make ISEbased nutrient management system not feasible for farmers. Kim Hak-Jin et al suggested an ISE-based automated nutrient management system in 2013.…”

“…We suggested a machine-learning based algorithm for removal of ion interference effect in 2019. [11] This algorithm can readjust the ISE signals to restore its accuracy up to 98%. It also works in O(1) time after trained.…”