Detecting the Type and Severity of Mineral Nutrient Deficiency in Rice Plants Based on an Intelligent microRNA Biosensing Platform

Abstract

The early determination of the type and severity of stresses caused by nutrient deficiency is necessary for taking timely measures and preventing a remarkable yield reduction. This study is an effort to investigate the performance of a machine learning-based model that identifies the type and severity of nitrogen, phosphorus, potassium, and sulfur in rice plants by using the plant microRNA data as model inputs. The concentration of 14 microRNA compounds in plants exposed to nutrient deficiency was measured using an electrochemical biosensor based on the peak currents produced during the probe-target microRNA hybridization. Subsequently, several machine learning models were utilized to predict the type and severity of stress. According to the results, the biosensor used in this work exerted promising analytical performance, including linear range (10^-19 to 10^-11 M), limit of detection (3 × 10^-21 M), and reproducibility during microRNA measurement in total RNA extracted from rice plant samples. Among the microRNAs studied, miRNA167, miRNA162, miRNA169, and miRNA395 exerted the largest contribution in predicting the nutrient deficiency levels based on feature selection methods. Using these four microRNAs as model inputs, the random forest with hyperparameters optimized by the genetic algorithm was capable of detecting the type of nutrient deficiency with an average accuracy, precision, and recall of 0.86, 0.94, and 0.87, respectively, seven days after the application of the nutrient treatment. Within this period, the optimized machine was able to detect the level of deficiency with average MSE and R² of 0.010 and 0.92, respectively. Combining the findings of this study and the results we reported earlier on determining the occurrence of salinity, drought, and heat in rice plants using microRNA biosensors can be useful to develop smart biosensing platforms for efficient plant health monitoring systems.

Keywords: biosensor; feature selection; microRNA compounds; random forest.

Figure 1

The proposed method for predicting stresses in rice plants by using microRNA concentrations and machine learning.

Figure 2

The cyclic voltammograms obtained by the biosensor at various concentrations of the studied microRNAs, (a) 0, (b) 10⁻²⁰, (c) 10⁻¹⁹, (d) 10⁻¹⁸, (e) 10⁻¹⁷, (f) 10⁻¹⁶, (g) 10⁻¹⁵, (h) 10⁻¹⁴, (i) 10⁻¹³, (j) 10⁻¹², (k) 10⁻¹¹, and (l) 10⁻¹⁰ M. The cyclic voltammogram corresponding to each concentration shows a voltammogram with the closest peak to the mean peak value obtained for all studied microRNAs.

Figure 3

The calibration plot of the biosensor. The light blue area shows the dispersion of peak current data of studied microRNAs from the mean peak current. The darker blue line shows the linear range.

Figure 4

Times of changes in the studied microRNAs towards various nutrient deficiencies. Pink color shows overexpression, while the blue color shows suppression.

Figure 5

The scores of microRNAs in predicting the severity of nutrient deficiency.

Figure 6

Taylor’s diagrams for the prediction of the severity of nutrient deficiency. Red-colored dots are the observed values, while the other dots belong to the predicted values using the RF (green), ANN (blue), and SVM (black). (a) N deficiency, (b) P deficiency, (c) K deficiency, and (d) S deficiency.