[1]贺佳,郭燕,王来刚,等.基于作物生长监测诊断仪的玉米南方锈病监测模型[J].江苏农业学报,2025,(08):1553-1558.[doi:doi:10.3969/j.issn.1000-4440.2025.08.011]
　HE Jia,GUO Yan,WANG Laigang,et al.Monitoring model for southern corn rust based on crop growth monitoring and diagnosis 402 (CGMD402)[J].,2025,(08):1553-1558.[doi:doi:10.3969/j.issn.1000-4440.2025.08.011]
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基于作物生长监测诊断仪的玉米南方锈病监测模型()

江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2025年08期
页码:
1553-1558
栏目:
农业信息工程
出版日期:
2025-08-31

文章信息/Info

Title:
Monitoring model for southern corn rust based on crop growth monitoring and diagnosis 402 (CGMD402)
作者:
贺佳; 郭燕; 王来刚; 张彦; 位盼盼; 曾凯
(河南省农业科学院农业信息技术研究所/农业农村部黄淮海智慧农业技术重点实验室/河南省农作物种植监测与预警工程研究中心,河南郑州450002)
Author(s):
HE Jia; GUO Yan; WANG Laigang; ZHANG Yan; WEI Panpan; ZENG Kai
(Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences/Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs/Henan Engineering Laboratory of Crop Planting Monitoring and Warning, Zhengzhou 450002, China)
关键词:
作物生长监测诊断仪; 玉米南方锈病; 病害监测模型
Keywords:
crop growth monitoring and diagnosis apparatus; southern corn rust; disease monitoring model
分类号:
S127；S51
DOI:
doi:10.3969/j.issn.1000-4440.2025.08.011
文献标志码:
A
摘要:
针对玉米南方锈病（SCR）的发生情况，本研究通过连续2年田间病害调查试验,利用402型作物生长监测诊断仪(Crop growth monitoring & diagnosis 402,CGMD402)和ASD FR-2500型地物光谱仪(Analytical spectral devices field spec,ASD2500)获取玉米冠层归一化差值植被指数(NDVI)、比值植被指数(RVI),通过分析光谱指数与玉米南方锈病病情指数(DISCR)的定量关系,并利用CGMD402采集的NDVI、RVI建立DISCR监测模型,以田间实测病情指数对模型精度进行验证评价。结果表明,2种传感器获取的NDVI、RVI拟合决定系数(R 2)分别为0.965、0.960,说明2种设备获取的冠层光谱指数具有较高的一致性；基于CGMD402采集的NDVI、RVI建立DISCR监测模型的决定系数(R 2)分别为0.824、0.778,标准差(SE)分别为0.047、0.051；以田间玉米南方锈病病情指数实测值对基于CGMD402采集的NDVI、RVI玉米南方锈病监测模型预测值进行验证,结果显示两者间R 2分别为0.978、0.975,均方根误差(RMSE)分别为0.267、0.301,相对误差(RE)分别为7.387%、9.734%,说明基于CGMD402的玉米南方锈病监测模型能较好监测田间DISCR。与目前利用便携式地物光谱仪相比,CGMD402能较好地获取作物病害信息,在病害田间监测上具有一定的精度与应用价值。
Abstract:
Regarding the occurrence of southern corn rust (SCR), this study conducted field disease investigation experiments over two consecutive years. Utilizing the crop growth monitoring and diagnosis 402 (CGMD402) and the analytical spectral devices FieldSpec FR-2500 (ASD2500) hyperspectrometer, the canopy normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) of corn were acquired. By analyzing the quantitative relationship between spectral indices and the disease index of southern corn rust (DISCR), monitoring models for DISCR were established using NDVI and RVI collected by the CGMD402. The accuracy of these models was validated and evaluated against the field-measured disease index. The results indicated that the coefficients of determination (R2) for NDVI and RVI obtained by the two sensors were 0.965 and 0.960, respectively, demonstrating a high consistency between the canopy spectral indices acquired by the two devices. The coefficients of determination (R2) for the DISCR monitoring models established based on NDVI and RVI collected by the CGMD402 were 0.824 and 0.778, respectively, with standard errors (SE) of 0.047 and 0.051. The validation of the predicted values from the southern corn rust monitoring models (based on NDVI and RVI collected by the CGMD402) against field-measured DISCR values showed that the R2 values between them were 0.978 and 0.975, the root mean square errors (RMSE) were 0.267 and 0.301, and the relative errors (RE) were 7.387% and 9.734%, respectively. These results demonstrated that the CGMD402-based monitoring models for southern corn rust can effectively track the field-measured DISCR. Compared to portable field spectrometers, the CGMD402 demonstrates a superior capability in acquiring crop disease information, offering considerable accuracy and practical value for field-based disease monitoring.

参考文献/References:

[1]MENG R, LV Z G, YAN J B, et al. Development of spectral disease indices for southern corn rust detection and severity classification[J]. Remote Sensing,2020,12(19):3233.
[2]ZHANG J C, HUANG Y B, PU R L, et al. Monitoring plant diseases and pests through remote sensing technology:a review[J]. Computers and Electronics in Agriculture,2019,165:104943.
[3]KUNDU N, RANI G, DHAKA V S, et al. Disease detection,severity prediction,and crop loss estimation in maize crop using deep learning[J]. Artificial Intelligence in Agriculture,2022,6:276-291.
[4]韩烨,侯睿峥,陈霄. 基于循环一致对抗网络的玉米灰斑病图像迁移方法研究[J]. 中国农机化学报,2023,44(2):163-171.
[5]PREY L, SCHMIDHALTER U. Sensitivity of vegetation indices for estimating vegetative N status in winter wheat[J]. Sensors,2019,19(17):3712.
[6]ZHANG L Y, ZHANG H H, NIU Y X, et al. Mapping maize water stress based on UAV multispectral remote sensing[J]. Remote Sensing,2019,11(6):605.
[7]MENG R, GAO R J, ZHAO F, et al. Landsat-based monitoring of southern pine beetle infestation severity and severity change in a temperate mixed forest[J]. Remote Sensing of Environment,2022,269:112847.
[8]LV Z G, MENG R, CHEN G S, et al. Combining multiple spectral enhancement features for improving spectroscopic asymptomatic detection and symptomatic severity classification of southern corn leaf blight[J]. Precision Agriculture,2023,24(4):1593-1618.
[9]DU R Q, CHEN J Y, XIANG Y Z, et al. Incremental learning for crop growth parameters estimation and nitrogen diagnosis from hyperspectral data[J]. Computers and Electronics in Agriculture,2023,215:108356.
[10]王利民,刘佳,邵杰,等. 基于高光谱的春玉米大斑病害遥感监测指数选择[J]. 农业工程学报,2017,33(5):170-177.
[11]沈艳艳,赵玉涛,陈庚申,等. 玉米典型叶部病害高光谱识别及其烈度分类[J]. 智慧农业,2024,6(2):28-39.
[12]王凡,王超,冯美臣,等. 基于高光谱的玉米大斑病害监测[J]. 山西农业科学,2019,47(6):1065-1068.
[13]刘佳,王利民,杨福刚,等. 基于高光谱微分指数监测春玉米大斑病的研究[J]. 中国农学通报,2019,35(6):143-150.
[14]李浩,徐航煌,郑恒宇,等. 基于无人机遥感图像的松材线虫病监测技术研究[J]. 中国农机化学报,2020,41(9):170-175.
[15]贾箫. 基于无人机多光谱和热红外成像的玉米叶斑病遥感监测[D]. 郑州:河南农业大学,2023.
[16]杨建宁,张井超,朱艳,等. 便携式作物生长监测诊断仪性能试验[J]. 农业机械学报,2013,44(4):208-212,201.
[17]倪军,姚霞,田永超,等. 便携式作物生长监测诊断仪的设计与试验[J]. 农业工程学报,2013,29(6):150-156.
[18]林维潘. CGMD303作物生长监测诊断仪的测试与评价[D]. 南京:南京农业大学,2020.
[19]贺佳,郭燕,王利军,等. 基于作物生长监测诊断仪的玉米LAI监测模型研究[J]. 农业机械学报,2019,50(12):187-194.
[20]李艳大,曹中盛,舒时富,等. 基于作物生长监测诊断仪的双季稻叶干重监测模型[J]. 作物学报,2021,47(10):2028-2035.
[21]李艳大,叶春,曹中盛,等. 基于作物生长监测诊断仪的双季稻叶片氮含量和氮积累量监测[J]. 应用生态学报,2020,31(9):3040-3050.
[22]李艳大,孙滨峰,曹中盛,等. 基于作物生长监测诊断仪的双季稻叶面积指数监测模型[J]. 农业工程学报,2020,36(10):141-149.
[23]李艳大,曹中盛,孙滨峰,等. 江西双季稻氮素监测诊断模型的建立与应用[J]. 应用生态学报,2020,31(2):433-440.
[24]陈青春,黎学记,刘鹏飞,等. 应用CGMD-302光谱仪监测马铃薯病害[J]. 中国马铃薯,2016,30(5):302-307.
[25]刘梦冉,张海艳,齐双丽,等. 基于高光谱遥感的小麦黄花叶病害等级监测研究 [J]. 麦类作物学报,2022,42(7):872-882.
[26]REN K H, DONG Y Y, HUANG W J, et al. Monitoring of winter wheat stripe rust by collaborating canopy SIF with wavelet energy coefficients[J]. Computers and Electronics in Agriculture,2023,215:108366.
[27]贺佳,刘冰峰,黎世民,等. 不同生育时期冬小麦籽粒蛋白质含量的高光谱遥感监测模型[J]. 中国生态农业学报,2017,25(6):865-875.
[28]TIAN L, XUE B W, WANG Z Y, et al. Spectroscopic detection of rice leaf blast infection from asymptomatic to mild stages with integrated machine learning and feature selection[J]. Remote Sensing of Environment,2021,257:112350.

备注/Memo

备注/Memo:
收稿日期：2025-01-16基金项目：国家重点研发计划项目(2022YFD2001105)；河南省重点研发与推广专项(232102110027、251111112500)；河南省农业科学院自主创新项目(2024ZC071、2025ZC81)作者简介：贺佳(1985-),男,河南三门峡人,博士,副研究员,主要从事农业遥感研究应用。(E-mail)hejia2011@163.com通讯作者：曾凯,(E-mail)zkzy6798@163.com
更新日期/Last Update: 2025-09-23