Study on distribution map of weeds in rice field based on UAV remote sensing
-
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:ObjectiveTo 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.
MethodThree 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.
ResultThe 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.
ConclusionThe classification accuracy of weed in rice field based on CNN is the highest, and the weed distribution map generated by CNN is the best.
-
-
![]()
图 2 无人机拍摄的原图和对应的标注图
Figure 2. Original image taken by UAV and corresponding annotated image
![]()
图 4 主成分分析算法计算流程图
Figure 4. Calculation process of principal component analysis algorithm
![]()
图 5 利用3种机器学习算法和CNN生成水稻田杂草分布图的技术路线图
Figure 5. Technical routes of using three kinds of machine learning algorithms and CNN to generate distribution map of paddy weeds
![]()
图 7 KNN算法k值与分类精度的关系
Figure 7. Relationship between k value and classification accuracy in KNN algorithm
![]()
图 8 卷积神经网络训练步数与Loss值之间的关系图
Figure 8. Relationship between train step and Loss value in convolutional neural network
![]()
图 9 基于4种算法生成的水稻田杂草分布图与原图、标注图的比较
Figure 9. Comparisons of distribution maps of paddy weeds generated based on four algorithms with original image and annotated image
表 1 3个模型的混淆矩阵精度、运行时间和综合识别精度
Table 1 Confusion matrix accuracy, run time and integrated recognition accuracy of three models
模型
Model项目
Item精度/% Accuracy 运行时间/s
Run time综合识别精度/%
Integrated recognition accuracy真实为杂草
True weed真实为水稻
True rice真实为其他
True other objectSVM 预测为杂草 Predicted weed 78.04 10.98 10.98 0.500 4 89.75 预测为水稻 Predicted rice 7.07 92.60 0.32 预测为其他
Predicted other object2.40 0.90 96.69 KNN 预测为杂草 Predicted weed 87.80 4.47 0.07 2.209 2 85.58 预测为水稻 Predicted rice 16.88 82.32 0.80 预测为其他
Predicted other object9.94 0 90.06 AdaBoost 预测为杂草 Predicted weed 82.93 11.29 5.28 0.411 1 90.25 预测为水稻 Predicted rice 6.91 92.77 0.32 预测为其他
Predicted other object7.23 1.81 90.96 
下载: 导出CSV
-
[1] 姚中统. 新型助剂对玉米田除草剂增效作用及增效机制的研究[D]. 哈尔滨: 东北农业大学, 2019. [2] LOPEZ-GRANADOS F, TORRES-SANCHEZ J, SERRANO-PEREZ A, et al. Early season weed mapping in sunflower using UAV technology: Variability of herbicide treatment maps against weed thresholds[J]. Precis Agric, 2016, 17(2): 183-199. doi: 10.1007/s11119-015-9415-8
[3] LAN Y, THOMSON S J, HUANG Y, et al. Current status and future directions of precision aerial application for site-specific crop management in the USA[J]. Comput Electron Agr, 2010, 74(1): 34-38. doi: 10.1016/j.compag.2010.07.001
[4] LAN Y, CHEN S. Current status and trends of plant protection UAV and its spraying technology in China[J]. Int J Precis Agric Aviat, 2018, 1(1): 1-9. doi: 10.33440/j.ijpaa.20180101.0002
[5] 刘斌. 基于无人机遥感影像的农作物分类研究[D]. 北京: 中国农业科学院, 2019. [6] 邓继忠, 任高生, 兰玉彬, 等. 基于可见光波段的无人机超低空遥感图像处理[J]. 华南农业大学学报, 2016, 37(6): 16-22. [7] PATHAK R, BARZIN R, BORA G C. Data-driven precision agricultural applications using field sensors and unmanned aerial vehicle (UAVs)[J]. Int J Precis Agric Aviat, 2018, 1(1): 19-23.
[8] BARRERO O, ROJAS D, GONZALEZ C, et al. Weed detection in rice fields using aerial images and neural networks[C]//ALTUVE M. Symposium of image, signal processing, and artificial vision. New York: IEEE, 2016.
[9] BARRERO O, PERDOMO S A. RGB and multispectral UAV image fusion for Gramineae weed detection in rice fields[J]. Precis Agric, 2018, 19(5): 809-822. doi: 10.1007/s11119-017-9558-x
[10] UTO K, SEKI H, SAITO G, et al. Characterization of rice paddies by a UAV-mounted miniature hyperspectral sensor system[J]. IEEE J-STARS, 2013, 6(2): 851-860.
[11] 马明洋. 基于无人机低空遥感的东北粳稻叶绿素含量估测方法研究[D]. 沈阳: 沈阳农业大学, 2018. [12] 洪雪. 基于水稻高光谱遥感数据的植被指数产量模型研究[D]. 沈阳: 沈阳农业大学, 2017. [13] MA X, DENG X, QI L, et al. Fully convolutional network for rice seedling and weed image segmentation at the seedling stage in paddy fields[J]. PLoS One, 2019, 14(4): e0215676. doi: 10.1371/journal.pone.0215676
[14] 邓向武, 齐龙, 马旭, 等. 基于多特征融合和深度置信网络的稻田苗期杂草识别[J]. 农业工程学报, 2018, 34(14): 165-172. [15] 陈晓红. 数据降维的广义相关分析研究[D]. 南京: 南京航空航天大学, 2011. [16] KARL PEARSON F R S. LIII: On lines and planes of closest fit to systems of points in space[J]. Philosophical Magazine Series 1, 1901, 11(2): 559-572.
[17] HOTELLING H. Analysis of a complex of statistical variables into principal components[J]. J Educ Psychol, 1933, 24(6): 417-441. doi: 10.1037/h0071325
[18] 王小龙, 邓继忠, 黄华盛, 等. 基于高光谱数据的棉田虫害鉴别研究[J]. 华南农业大学学报, 2019, 40(3): 97-103. [19] 曾伟辉. 面向农作物叶片病害鲁棒性识别的深度卷积神经网络研究[D]. 合肥: 中国科学技术大学, 2018. [20] SUN J, YANG J, ZHANG C, et al. Automatic remotely sensed image classification in a grid environment based on the maximum likelihood method[J]. Math Comput Model, 2013, 58(3/4): 573-581.
[21] MILLER D M, KAMINSKY E J, RANA S. Neural network classification of remote-sensing data[J]. Comput Geosci, 1995, 21(3): 377-386. doi: 10.1016/0098-3004(94)00082-6
[22] ZENG J, GUO H F, HU Y M. Artificial neural network model for identifying taxi gross emitter from remote sensing data of vehicle emission[J]. J Environ Sci, 2007, 19(4): 427-431. doi: 10.1016/S1001-0742(07)60071-0
[23] BROWN M, LEWIS H G, GUNN S. Linear spectral mixture models and support vector machines for remote sensing[J]. IEEE T Geosci Remote, 2000, 38(5): 2346-2360. doi: 10.1109/36.868891
[24] HUANG C, DAVIS L S, TOWNSHEND J R G. An assessment of support vector machines for land cover classification[J]. Int J Remote Sens, 2002, 23(4): 725-749. doi: 10.1080/01431160110040323
[25] IRVIN B J, VENTURA S J, SLATER B K. Fuzzy and isodata classification of landform elements from digital terrain data in Pleasant Valley, Wisconsin[J]. Geoderma, 1997, 77(2/3/4): 137-154.
[26] PAL S K, GHOSH A, SHANKAR B U. Segmentation of remotely sensed images with fuzzy thresholding, and quantitative evaluation[J]. Int J Remote Sens, 2000, 21(11): 2269-2300. doi: 10.1080/01431160050029567
[27] 王璨, 李志伟. 利用融合高度与单目图像特征的支持向量机模型识别杂草[J]. 农业工程学报, 2016, 32(15): 165-174. [28] 唐美丽, 张劲松, 李璐, 等. 基于GPU的SVM参数优化并行算法[J]. 江苏大学学报(自然科学版), 2017, 38(5): 576-581. [29] CORTES C, VAPNIK V N. Support-vector networks[J]. 1995. doi: 10.1023/A: 1022627411411.
[30] 李春雨, 葛啸, 金燕婷, 等. 基于近红外光谱技术的蔬菜农药残留种类检测[J]. 农业工程, 2019, 9(6): 33-39. [31] 黄欣, 莫海淼, 赵志刚. 基于自适应烟花算法和k近邻算法的特征选择算法[J]. 计算机应用与软件, 2020, 37(5): 268-274. [32] FREUND Y, SCHAPIRE R E. A decision-theoretic generalization of on-line learning and an application to boosting[J]. J Comput Syst Sci, 1997, 55(1): 119-139. doi: 10.1006/jcss.1997.1504
[33] 杨国欣. 基于Adaboost算法和视觉显著性的羊只目标检测与计数方法研究[D]. 杨凌: 西北农林科技大学, 2019. [34] 付忠良. 关于AdaBoost有效性的分析[J]. 计算机研究与发展, 2008, 45(10): 1747-1755. [35] LIANG X, XU C, SHEN X, et al. Human parsing with contextualized convolutional neural network[J]. IEEE T Pattern Anal, 2017, 39(1): 115-127. doi: 10.1109/TPAMI.2016.2537339
[36] 卢伟, 胡海阳, 王家鹏, 等. 基于卷积神经网络面部图像识别的拖拉机驾驶员疲劳检测[J]. 农业工程学报, 2018, 34(7): 192-199.
