Growth Stages Classification of Potato Crop Based on Analysis of Spectral Response and Variables Optimization

Abstract

Potato is the world’s fourth-largest food crop, following rice, wheat, and maize. Unlike other crops, it is a typical root crop with a special growth cycle pattern and underground tubers, which makes it harder to track the progress of potatoes and to provide automated crop management. The classification of growth stages has great significance for right time management in the potato field. This paper aims to study how to classify the growth stage of potato crops accurately on the basis of spectroscopy technology. To develop a classification model that monitors the growth stage of potato crops, the field experiments were conducted at the tillering stage (S1), tuber formation stage (S2), tuber bulking stage (S3), and tuber maturation stage (S4), respectively. After spectral data pre-processing, the dynamic changes in chlorophyll content and spectral response during growth were analyzed. A classification model was then established using the support vector machine (SVM) algorithm based on spectral bands and the wavelet coefficients obtained from the continuous wavelet transform (CWT) of reflectance spectra. The spectral variables, which include sensitive spectral bands and feature wavelet coefficients, were optimized using three selection algorithms to improve the classification performance of the model. The selection algorithms include correlation analysis (CA), the successive projection algorithm (SPA), and the random frog (RF) algorithm. The model results were used to compare the performance of various methods. The CWT-SPA-SVM model exhibited excellent performance. The classification accuracies on the training set (Atrain) and the test set (Atest) were respectively 100% and 97.37%, demonstrating the good classification capability of the model. The difference between the Atrain and accuracy of cross-validation (Acv) was 1%, which showed that the model has good stability. Therefore, the CWT-SPA-SVM model can be used to classify the growth stages of potato crops accurately. This study provides an important support method for the classification of growth stages in the potato field.

Coupling Square Wave Anodic Stripping Voltammetry with Support Vector Regression to Detect the Concentration of Lead in Soil under the Interference of Copper Accurately

“…To study the stripping response law of Pb 2+ in the existence of different Cu 2+ concentrations, the concentration of Pb 2+ was set with 10 gradients, which were 1,5,10,15,20,25,30,35,40, and 45 µg/L. The Cu 2+ concentration was set with seven gradients, which were 0, 1, 5, 10, 15, 20, and 25 µg/L, because the peak current of Pb 2+ was extremely small and kept decreasing when the Cu 2+ concentration exceeded 25 µg/L.…”

“…The introduction of the kernel function achieves the purpose of increasing the dimension, and the added parameters are adjustable and optimizable to prevent overfitting questions. Many scholars have indicated that the radial basis function net is an efficient tool for the fitting of a nonlinear relationship [25,33]. Therefore, in this study, a radial basis function was selected as the SVR kernel function to discover and interpret the nonlinear relationship between the stripping peak currents of Pb 2+ and Cu 2+ and the Pb 2+ concentration.…”

“…SVR, as a learning algorithm for small sample size, is widely used to solve nonlinear regression questions [23,24]. Many scholars have indicated that the radial basis function (RBF) net is an efficient tool for the fitting of a nonlinear relationship [25] and can efficiently eliminate the overfitting problems that widely exist in traditional nonlinear regression models [26]. Therefore, in this study, the radial basis function was selected as the SVR kernel function to interpret the nonlinear relationship between the stripping peak currents of Pb 2+ and Cu 2+ and Pb 2+ concentration.…”

Estimation of nitrate nitrogen content in cotton petioles under drip irrigation based on spectral indices and wavelet neural network

“…The diagnosis of N nutrition in crops based on spectral data has made considerable progress [17]. The technique has been applied in several crops to rapidly obtain crop N nutrition status spectral indices derived from spectral [18][19][20]. Several researchers have developed crop N nutrition monitoring models based on spectral indices, and achieved high accuracy.…”

Analysis of Chlorophyll Concentration in Potato Crop by Coupling Continuous Wavelet Transform and Spectral Variable Optimization

“…Potato (Solanum tuberosum) is the world's fourth-largest food crop following rice, wheat, and maize [1,2]. Chlorophyll, as the essential photosynthetic pigment of potato leaves, reflects growth information about plant health [3] and photosynthetic rate [4], and its content is also significantly correlated with the concentration of nitrogen [5].…”