Capacitive coupled contactless conductivity detection(C⁴D) plays an important role in agricultural soil nutrient ion detection. Effective identification of characteristic ion peaks in C⁴D signals is conducive to subsequent qualitative and quantitative analysis of characteristic ion peaks, which provides a basis for improving agricultural soil nutrient management. However, the detection of characteristic peaks in C⁴D signals still has shortcomings, such as the inability of automatic and accurate identification, complicated manual operation, and low efficiency.

In this study, an automatic spectral peak identification algorithm based on continuous wavelet transform combined with particle swarm optimization(PSO) and maximum interclass variance method(Otsu) was proposed to achieve accurate, efficient and automated identification of C⁴D signal peaks. Capillary electrophoresis(CE) combined with a C⁴D device(CE-C⁴D) was used to detect the standard ions and soil sample solutions to obtain the C⁴D ion signal spectra, which were simulated according to the characteristics of the real C⁴D signal spectra to obtain the C⁴D simulated signals containing single Gaussian peaks and multi-Gaussian peaks. The continuous wavelet transform was performed on the C⁴D spectrogram signal to obtain the wavelet transform coefficient matrix. The local maxima and local minima of the continuous wavelet transform coefficient matrix were searched by the staircase scanning method, and the local maxima and local minima were connected to form ridges and valleys. The wavelet coefficient matrix was converted to a grayscale image by logistic mapping to visualize the data. The number of particle populations in PSO was set to 15, the gray scale threshold of 15 particles was set to a random integer within the gray scale level of 0～255, and the initial velocity of the particles was set to 5. The combination of PSO and Otsu calculated the fitness(variance value) of each particle, updated the individual best position and the global best position, further updated the velocities and positions of the particles to find the global best position(the maximum interclass variance), and defined the maximum interclass variance was defined as the optimal threshold value, used the optimal threshold value for background and target segmentation of the gray-scale image and extracted the ridges within the peak region segmented from the gray-scale image by the PSO-Otsu algorithm. A threshold was set according to the length of the ridge line; the ridge lines larger than the threshold were extracted; the valley lines on both sides of the ridge line were found according to the filtered ridge line; and the start and end points of the peak region were obtained from the valley lines. The filtered ridge lines were used to identify the peak location of the peak region, and the edge threshold was set to remove the false peaks due to continuous wavelet transform(CWT) located in the edge region of the C⁴D signal and to accurately identify the location of the true peak value.

The datasets containing 41, 61 and 102 peaks were tested, and the Receiver Operating Characteristic(ROC)curves and metric values were used as a guideline to evaluate the performance of the peak detection algorithms. Compared to the two methods, multi scale peak detection(MSPD) and CWT-based image segmentation(CWT-IS), the CWT combined with Particle Swarm Optimization based maximum spectral peaks automatic identification algorithm based on Continuous Wavelet Transform combined with Particle Swarm Optimization of Otsu to segment image(CWTSPSO) method of interclass variance segmentation(CWT-IS), the ROC curves of the three groups remained above 0.9. Testing the dataset containing 102 peaks, the ROC curves of MSPD and CWT-IS were also high only in the case of high false discovery rate. The highest metric values of CWTSPSO were 0.976, 0.915, and 0.969, respectively, and the highest metric values of 1 set of MSPD and CWT-IS were 0.754 and 0.505. The results showed that the ROC curves of CWTSPSO in the 3 sets of dataset were not high. Using ROC curves and metric values as a criterion comparison to evaluate the performance of peak detection algorithms, the characteristic peak recognition performance was outstanding, which could achieve a higher correct rate while maintaining a lower false discovery rate, effectively detected more weak and overlapping peaks while detecting fewer false peaks, which was conducive to the enhancement of the spectral peak recognition rate and accuracy of the C⁴D signals.

This study provided a fast and accurate method for the identification of characteristic peaks in the spectrograms of ion signals detected by contactless conductivity, CWTSPSO could accurately identify the weak and overlapping peaks in the spectrograms of ion signals detected by contactless conductivity. The automatic identification algorithm of the spectrogram peaks of CWTSPSO would provide powerful support for the characterization and quantification of the signals of nutrient ions detected by contactless conductivity in agricultural soils and would further strengthen the precision of soil fertilization and crop growth management fertilization and crop growth management.

The content of nitrogen (N) and potassium (K) in the soil directly affects crop yield, making it a crucial indicator in agricultural production processes. Insufficient levels of the two nutrients can impede crop growth and reduce yield, while excessive levels can result in environmental pollution. Rapidly quantifying the N and K content in soil is of great importance for agricultural production and environmental protection.

A rapid and quantitative method was proposed for detecting N and K nutrient ions in soil based on polydimethylsiloxane (PDMS) microfluidic chip electrophoresis and capacitively coupled contactless conductivity detection (C⁴D). Microfluidic chip electrophoresis enables rapid separation of multiple ions in soil. The electrophoresis microfluidic chips have a cross-shaped channel layout and were fabricated using soft lithography technology. The sample was introduced into the microfluidic chip by applying the appropriate injection voltage at both ends of the injection channel. This simple and efficient procedure ensured an accurate sample introduction. Subsequently, an electrophoretic voltage was applied at both ends of the separation channel, creating a capillary zone electrophoresis that enables the rapid separation of different ions. This process offered high separation efficiency, required a short processing time, and had a small sample volume requirement. This enabled the rapid processing and analysis of many samples. C⁴D enabled precise measurement of changes in conductivity. The sensing electrodes were separated from the microfluidic chips and printed onto a printed circuit board (PCB) using an immersion gold process. The ions separated under the action of an electric field and sequentially reach the sensing electrodes. The detection circuit, connected to the sensing electrodes, received and regulated the conductivity signal to reflect the variance in conductivity between the sample and the buffer solution. The sensing electrodes were isolated from the sample solution to prevent interference from the high-voltage electric field used for electrophoresis.

The voltage used for electrophoresis, as well as the operating frequency and excitation voltage of the excitation signal in the detection system, had a significant effect on separation and detection performance. Based on the response characteristics of the system output, the optimal operating frequency of 1 000 kHz, excitation voltage of 50 V, and electrophoresis voltage of 1.5 kV were determined. A peak overshoot was observed in the electrophoresis spectrum, which was associated with the operating frequency of the system. The total noise level of the system was approximately 0.091 mV. The detection limit (S/N = 3) for soil nutrient ions was determined by analyzing a series of standard sample solutions with varying concentrations. The detection limited for potassium (K⁺), ammonium (NH₄⁺), and nitrate (NO₃^-) standard solutions were 0.5, 0.1 and 0.4 mg/L, respectively. For the quantitative determination of soil nutrient ion concentration, the linear relationship between peak area and corresponding concentration was investigated under optimal experimental conditions. K⁺, NH₄⁺, and NO₃^- exhibit a strong linear relationship in the range of 0.5～40 mg/L, with linear correlation coefficients (R²) of 0.994, 0.997, and 0.990, respectively, indicating that this method could accurately quantify N and K ions in soil. At the same time, to evaluate the repeatability of the system, peak height, peak area, and peak time were used as evaluation indicators in repeatability experiments. The relative standard deviation (RSD) was less than 4.4%, indicating that the method shows good repeatability. In addition, to assess the ability of the C⁴D microfluidic system to detect actual soil samples, four collected soil samples were tested using MES/His and PVP/PTAE as running buffers. K⁺, NH₄⁺,Na⁺, Chloride (Cl^-), NO₃^-, and sulfate(SO₄^3-)were separated sequentially within 1 min. The detection efficiency was significantly improved. To evaluate the accuracy of this method, spiked recovery experiments were performed on four soil samples. The recovery rates ranged from 81.74% to 127.76%, indicating the good accuracy of the method.

This study provides a simple and effective method for the rapid detection of N and K nutrient ions in soil. The method is highly accurate and reliable, and it can quickly and efficiently detect the contents of N and K nutrient ions in soil. This contactless measurement method reduced costs and improved economic efficiency while extending the service life of the sensing electrodes and reducing the frequency of maintenance and replacement. It provided strong support for long-term, continuous conductivity monitoring.