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

• Main Authors: Ning Liu, Ruomei Zhao, Lang Qiao, Yao Zhang, Minzan Li, Hong Sun, Zizheng Xing, Xinbing Wang
• Format: Article
• Language: English
• Published: MDPI AG 2020-07-01
• Series: Sensors
• Subjects: precision agriculture; continuous wavelet transform (CWT); successive projection algorithm (SPA); random frog (RF); support vector machine (SVM)
• Online Access: https://www.mdpi.com/1424-8220/20/14/3995

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 (<i>A_train</i>) and the test set (<i>A_test</i>) were respectively 100% and 97.37%, demonstrating the good classification capability of the model. The difference between the <i>A_train</i> and accuracy of cross-validation (<i>A_cv</i>) 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.