摘要
【目的】结合传统与现代农业病虫害监测的优缺点,探索通过无人机高光谱遥感技术检测出患病的柑橘植株、通过人工田间调查方式判断其患病种类及患病程度的病虫害监测方法。【方法】使用无人机获取原始高光谱图像,经过光谱预处理和特征工程后,采用连续投影算法提取对柑橘患病植株分类贡献值最大的特征波长组合,基于全波段使用BP神经网络和XgBoost算法、基于特征波段使用逻辑回归和支持向量机算法,建立分类模型。【结果】基于全波段的BP神经网络和XgBoost算法的ROC曲线下面积(Area under curve,AUC)分别为0.8830和0.9120,分类准确率均超过95%;提取出698和762 nm的特征波长组合,基于特征波长使用逻辑回归和支持向量机算法建立的分类模型召回率分别达到了93.00%和96.00%。【结论】基于特征波长建模在患病样本分类中表现出很高的准确率,证明了特征波长组合的有效性。本研究结果可为柑橘种植园的病虫害监测提供一定的数据和理论支撑。
【Objective】Combined with the advantages and disadvantages of traditional and modern agricultural pest monitoring,the method of monitoring pest and disease were discussed,which detected the diseased citrus plants by UAV hyperspectral remote sensing technology and judged the disease species and disease degree by artificial field investigation.【Method】The original hyperspectral images were obtained by UAV.After spectral preprocessing and feature engineering,continuous projection algorithm was used to extract the feature wavelength combination which contributed the most to the classification of citrus diseased plants.Finally,the BP neural network and XgBoost algorithm were used based on the full band,and the logistic regression and support vector machine algorithm were used to establish the classification model based on the characteristic band.【Result】The AUC scores of BP neural network and XgBoost were 0.8830 and 0.9120 respectively,and the accuracy rates of both methods were over 95%.The feature wavelength combination of 698 and 762 nm was extracted.Based on this characteristic band,the recall rates of logistic regression and support vector machine algorithm were 93.00%and 96.00%respectively.【Conclusion】The model based on characteristic band shows high accuracy in the classification of disease samples,which proves the effectiveness of characteristic wavelength combination.This result can provide some data and theoretical support for monitoring diseases and pests in citrus plantations.
作者
邓小玲
曾国亮
朱梓豪
黄梓效
杨佳诚
童泽京
殷献博
王天伟
兰玉彬
DENG Xiaoling;ZENG Guoliang;ZHU Zihao;HUANG Zixiao;YANG Jiacheng;TONG Zejing;YIN Xianbo;WANG Tianwei;LAN Yubin(College of Electronic Engineering/College of Artificial Intelligence,South China Agricultural University,Guangzhou 510642,China;National Center for International Collaboration Research on Precision Agricultural Aviation Pesticides Spraying Technology,Guangzhou 510642,China)
出处
《华南农业大学学报》
CAS
CSCD
北大核心
2020年第6期100-108,共9页
Journal of South China Agricultural University
基金
国家自然科学基金(61675003)
广东省重点领域研发计划(2019B020214003)
广东高校重点领域(人工智能)专项(2019KZDZX1012)
广东大学生科技创新培育专项(pdjh2019b007)。
关键词
柑橘病虫害
无人机
高光谱遥感
XgBoost
连续投影算法
citrus disease and pest
UAV
hyperspectral remote sensing
XgBoost
continuous projection algorithm
