Remote Sensing and Precision Agriculture Technologies for Crop Disease Detection and Management with a Practical Application Example

Remote sensing technology has long been used to detect and map crop diseases.Airborne and satellite imagery acquired during growing seasons can be used not only for early detection and within-season management of some crop diseases,but also for the control of recurring diseases in future seasons.With variable rate technology in precision agriculture,site-specific fungicide application can be made to infested areas if the disease is stable,although traditional uniform application is more appropriate for diseases that can spread rapidly across the field.This article provides a brief overview of remote sensing and precision agriculture technologies that have been used for crop disease detection and management.Specifically,the article illustrates how airborne and satellite imagery and variable rate technology have been used for detecting and mapping cotton root rot,a destructive soilborne fungal disease,in cotton fields and how site-specific fungicide application has been implemented using prescription maps derived from the imagery for effective control of the disease.The overview and methodologies presented in this article should provide researchers,extension personnel,growers,crop consultants,and farm equipment and chemical dealers with practical guidelines for remote sensing detection and effective management of some crop diseases.

Improvement in estimation of soil water deficit by integrating airborne imagery data into a soil water balance model

In this study,an approach that integrates airborne imagery data as inputs was used to improve the estimation of soil water deficit(SWD)for maize and sunflower grown under full and deficit irrigation treatments.The proposed model was applied to optimize the maximum total available soil water(TAWr)by minimizing the difference between a water stress coefficient ks and crop water stress index(1-CWSI).The optimal value of maximum TAWr was then used to calibrate a soil water balance model which in turn updated the estimation of soil water deficit.The estimates of SWD in the soil profile of both irrigated maize and sunflower fields were evaluated with the crop root zone SWD derived from neutron probe measurements and the FAO-56 SWD procedure.The results indicated a good agreement between the estimated SWD from the proposed approach and measured SWD for both maize and sunflower.The statistical analyses indicated that the maximum TAWr estimated from CWSI significantly improved the estimates of SWD,which reduced the mean absolute error(MAE)and root mean square error(RMSE)by 40%and 44%for maize and 22%for sunflower,compared with the FAO-56 model.The proposed procedure works better for crops under deficit irrigation condition.With the availability of higher spatial and temporal resolution airborne imagery during the growing season,the optimization procedure can be further improved.

Relationship between spatial heterogeneity and wavelength in multisensor airborne images

Variogram has been utilized to exploring the spatial heterogeneity of remote sensing images,especially its association with spatial resolution.However,very few attentions have been drawn on evaluating the spatial heterogeneity of multisensor airborne imagery and its relationship with spectral wavelength.Therefore,an investigation was carried out on multisensor airborne images to determine the relation between spatial heterogeneity and spectral wavelength for woodland,grass,and urban landscapes by applying variogram modeling.The airborne thematic mapper(ATM),compact airborne spectrographic imager(CASI),and Specim AISA Eagle airborne images at Harwood Forest,Monks wood,Cambridge,and River Frome areas,UK,were utilized.Results revealed that(1)the red band contained greater spatial variability than near-infrared wavelengths and other visible wavebands;(2)there was a steep gradient at the red edge in reference to its spatial variability of multisensor airborne images;(3)only for natural landscape such as woodland and grass,near-infrared waveband contains greater within-scene variations than the blue and green bands;(4)compared with the discrepancy of spatial resolution introduced by multisensor images(ATM,CASI,and Eagle),the specific landscape and spectral bands were more important in determining heterogeneity by means of original visible,near-infrared bands,and normalized difference vegetation index(NDVI).These findings remained us to be caution when combining and interpreting spatial variability and spatial structures derived from airborne images with different spatial resolution and spectral wavelength.Additionally,the outcomes of this study also have considerable implications in terms of designing and choosing suitable images for different applications.

Assessing cotton defoliation, regrowth control and root rot infection using remote sensing technology

Cotton defoliation and post-harvest destruction are important cultural practices for cotton production.Cotton root rot is a serious and destructive disease that affects cotton yield and lint quality.This paper presents an overview and summary of the methodologies and results on the use of remote sensing technology for evaluating cotton defoliation and regrowth control methods and for assessing cotton root rot infection based on published studies.Ground reflectance spectra and airborne multispectral and hyperspectral imagery were used in these studies.Ground reflectance spectra effectively separated different levels of defoliation and airborne multispectral imagery permitted both visual and quantitative differentiations among defoliation treatments.Both ground reflectance and airborne imagery were able to differentiate cotton regrowth among different herbicide treatments for cotton stalk destruction.Airborne multispectral and hyperspectral imagery accurately identified root rot-infected areas within cotton fields.Results from these studies indicate that remote sensing can be a useful tool for evaluating the effectiveness of cotton defoliation and regrowth control strategies and for detecting and mapping root rot damage in cotton fields.Compared with traditional visual observations and ground measurements,remote sensing techniques have the potential for effective and accurate assessments of various cotton production operations and pest conditions.