Growth Stages Classification of Potato Crop Based on Analysis of Spectral Response and Variables Optimization
Growth Stages Classification of Potato Crop Based on Analysis of Spectral Response and Variables Optimization
复制标题
基于光谱响应分析和变量优化的马铃薯作物生长阶段分类
DOI：
10.3390/s20143995
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发表时间：
2020-07-01
期刊：
影响因子：
3.9
通讯作者：
Wang, Xinbing
中科院分区：
文献类型：
--
作者：
Liu, Ning;Zhao, Ruomei;Wang, Xinbing
马铃薯是世界第四大粮食作物，仅次于水稻、小麦和玉米。与其他作物不同，它是一种典型的块根作物，具有特殊的生长周期模式和地下块茎，这使得跟踪马铃薯的进展和提供自动化作物管理变得更加困难。马铃薯生育期的划分对适时管理具有重要意义。本文旨在研究如何利用光谱技术对马铃薯作物的生育期进行准确的分类。为建立马铃薯作物生育期分级模型，分别在分蘖期（S1）、块茎形成期（S2）、块茎膨大期（S3）和块茎成熟期（S4）进行田间试验。对光谱数据进行预处理后，分析了生长过程中叶绿素含量的动态变化和光谱响应。然后，使用支持向量机（SVM）算法的基础上的光谱波段和从反射光谱的连续小波变换（CWT）获得的小波系数的分类模型。为了提高模型的分类性能，采用三种选择算法对光谱变量（包括敏感光谱波段和特征小波系数）进行了优化。选择算法包括相关分析（CA），连续投影算法（SPA），和随机青蛙（RF）算法。模型结果被用来比较各种方法的性能。CWT-SPA-SVM模型具有良好的性能。在训练集A（train）和测试集A（test）上的分类准确率分别为100%和97.37%，表明该模型具有良好的分类能力。交叉验证的A（train）与准确度（A（cv））之差为1%，表明该模型具有良好的稳定性。因此，CWT-SPA-SVM模型可以用于马铃薯作物生育期的准确分类。该研究为马铃薯田间生育期的划分提供了重要的支持方法。
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 (A(train)) and the test set (A(test)) were respectively 100% and 97.37%, demonstrating the good classification capability of the model. The difference between theA(train)and accuracy of cross-validation (A(cv)) 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.