基于无人机低空遥感的水稻田间杂草分布图研究

朱圣; 邓继忠; 张亚莉; 杨畅; 严智威; 谢尧庆

doi:10.7671/j.issn.1001-411X.202006058

基于无人机低空遥感的水稻田间杂草分布图研究

Study on distribution map of weeds in rice field based on UAV remote sensing

摘要

摘要:
目的获取水稻田的低空遥感图像并分析得到杂草分布图，为田间杂草精准施药提供参考。

方法使用支持向量机(SVM)、K最近邻算法(KNN)和AdaBoost 3种机器学习算法，对经过颜色特征提取和主成分分析(PCA)降维后的无人机拍摄的水稻田杂草可见光图像进行分类比较；引入一种无需提取特征和降维、可自动获取图像特征的卷积神经网络(CNN)，对水稻田杂草图像进行分类以提升分类精度。

结果 SVM、KNN和AdaBoost对测试集的测试运行时间分别为0.500 4、2.209 2和0.411 1 s，分类精度分别达到89.75%、85.58%和90.25%，CNN对图像的分类精度达到92.41%，高于上述3种机器学习算法的分类精度。机器学习算法及CNN均能有效识别水稻和杂草，获取杂草的分布信息，生成水稻田间的杂草分布图。

结论 CNN对水稻田杂草的分类精度最高，生成的水稻田杂草分布图效果最好。

Abstract:
Objective To obtain and analyze the low altitude remote sensing image of rice field, acquire the weed distribution map, and provide a reference for the precious pesticide application of weeds in the field.

Method Three machine learning algorithms including support vector machine (SVM), K-nearest neighbor (KNN) and AdaBoost were used to classify and compare the weed visible light images in rice field captured by UAV after color feature extraction and principal component analysis (PCA) dimensionality reduction. A convolutional neural network (CNN) which can automatically obtain the image features without feature extraction and dimensionality reduction was introduced to classify the weed images and improve the classification accuracy.

Result The run time of test set based on SVM, KNN and AdaBoost were 0.500 4, 2.209 2 and 0.411 1 s, and the classification accuracies were 89.75%, 85.58% and 90.25% respectively; The classification accuracy of image based on CNN was 92.41%, which was higher than those of three machine learning algorithms. All machine learning algorithms and CNN could effectively recognize rice and weed, acquire weed distribution information, and generate distribution map of weed in rice field.

Conclusion The classification accuracy of weed in rice field based on CNN is the highest, and the weed distribution map generated by CNN is the best.

HTML全文

参考文献(36)

施引文献

资源附件(0)