Aerial multispectral imagery for plant disease detection: radiometric calibration necessity assessment

Pourazar, Hossein; Samadzadegan, Farhad; Javan, Farzaneh Dadrass

doi:10.1080/22797254.2019.1642143

Cited by 40 publications

(15 citation statements)

References 38 publications

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“…Iordache et al [28] applied RF classifier on the classification of Pinus pinaster canopy types (infected, suspicious, and healthy) affected by pine wild and obtained an overall accuracy of 95%. Pourazar et al [27], obtain as overall accuracy 95.58% using five spectral bands and five indices to detect dead and diseased trees.…”

Section: Discussionmentioning

confidence: 99%

“…In order to classify tree canopies into two different classes, healthy and dead trees, supervised machine-learning (ML) classification was used. Among the different object-oriented ML classifiers, the RF algorithm was applied as its performance is one of the most accurate ML algorithms when supervised classification for GEOBIA is conducted [27,28,68,73]. Presented by Breiman [74], RF is an automatic ensemble method based on decision trees where each tree depends on a collection of random variables [75].…”

Section: Object-based Analysis and Classificationmentioning

confidence: 99%

“…In recent years the use of unmanned aerial vehicle (UAV) platforms has become widely employed for pests and disease detection and monitoring [12,[22][23][24][25][26][27][28]. The main attributes are very high resolution, suitability for multitemporal analysis, lower operational costs compared with airplanes and satellites, independence of cloud cover, and the ability to operate in specific phenological phases of plants or pest/disease outbreaks [12].…”

Section: Introductionmentioning

confidence: 99%

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Detection of Longhorned Borer Attack and Assessment in Eucalyptus Plantations Using UAV Imagery

Duarte¹,

Acevedo-Muñoz²,

Gonçalves³

et al. 2020

Remote Sensing

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Eucalyptus Longhorned Borers (ELB) are some of the most destructive pests in regions with Mediterranean climate. Low rainfall and extended dry summers cause stress in eucalyptus trees and facilitate ELB infestation. Due to the difficulty of monitoring the stands by traditional methods, remote sensing arises as an invaluable tool. The main goal of this study was to demonstrate the accuracy of unmanned aerial vehicle (UAV) multispectral imagery for detection and quantification of ELB damages in eucalyptus stands. To detect spatial damage, Otsu thresholding analysis was conducted with five imagery-derived vegetation indices (VIs) and classification accuracy was assessed. Treetops were calculated using the local maxima filter of a sliding window algorithm. Subsequently, large-scale mean-shift segmentation was performed to extract the crowns, and these were classified with random forest (RF). Forest density maps were produced with data obtained from RF classification. The normalized difference vegetation index (NDVI) presented the highest overall accuracy at 98.2% and 0.96 Kappa value. Random forest classification resulted in 98.5% accuracy and 0.94 Kappa value. The Otsu thresholding and random forest classification can be used by forest managers to assess the infestation. The aggregation of data offered by forest density maps can be a simple tool for supporting pest management.

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Section: Discussionmentioning

confidence: 99%

Section: Object-based Analysis and Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detection of Longhorned Borer Attack and Assessment in Eucalyptus Plantations Using UAV Imagery

Duarte¹,

Acevedo-Muñoz²,

Gonçalves³

et al. 2020

Remote Sensing

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“…In general, radiometric calibration complicates the process of HLB detection. However, the study showed insignificant effects of radiometric calibration on the discrimination of HLB-infected and healthy trees when the data were collected consistently with similar illumination and atmospheric condition (Pourazar et al 2019). Disease scouting contributes to the control of disease.…”

Section: Detection Of Diseased Fruit Treesmentioning

confidence: 80%

Orchard management with small unmanned aerial vehicles: a survey of sensing and analysis approaches

et al. 2021

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Advances in sensor miniaturization are increasing the global popularity of unmanned aerial vehicle (UAV)-based remote sensing applications in many domains of agriculture. Fruit orchards (the source of the fruit industry chain) require site-specific or even individual-tree-specific management throughout the growing season—from flowering, fruitlet development, ripening, and harvest—to tree dormancy. The recent increase in research on deploying UAV in orchard management has yielded new insights but challenges relating to determining the optimal approach (e.g., image-processing methods) are hampering widespread adoption, largely because there is no standard workflow for the application of UAVs in orchard management. This paper provides a comprehensive literature review focused on UAV-based orchard management: the survey includes achievements to date and shortcomings to be addressed. Sensing system architecture focusing on UAVs and sensors is summarized. Then up-to-date applications supported by UAVs in orchard management are described, focusing on the diversity of data-processing techniques, including monitoring efficiency and accuracy. With the goal of identifying the gaps and examining the opportunities for UAV-based orchard management, this study also discusses the performance of emerging technologies and compare similar research providing technical and comprehensive support for the further exploitation of UAVs and a revolution in orchard management.

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“…Previous studies used QuickBird satellite images to estimate plant traits such as biochemical and biophysical parameters in crops. These include for instance estimating chlorophyll content [ 21 ], the estimation of grain yield [ 22 , 23 ], measuring leaf area index [ 24 , 25 ], the identification of nitrogen status [ 26 ], estimating above-ground biomass [ 10 , 27 ], the detection of crop disease [ 28 , 29 ], estimating crop evapotranspiration [ 30 ] and estimating dry matter [ 31 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

Integration of Radiometric Ground-Based Data and High-Resolution QuickBird Imagery with Multivariate Modeling to Estimate Maize Traits in the Nile Delta of Egypt

Elmetwalli

Tyler

Moghanm

et al. 2021

Sensors

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In site-specific management, rapid and accurate identification of crop stress at a large scale is critical. Radiometric ground-based data and satellite imaging with advanced spatial and spectral resolution allow for a deeper understanding of crop stress and the level of stress in a given area. This research aimed to assess the potential of radiometric ground-based data and high-resolution QuickBird satellite imagery to determine the leaf area index (LAI), biomass fresh weight (BFW) and chlorophyll meter (Chlm) of maize across well-irrigated, water stress and salinity stress areas in the Nile Delta of Egypt. Partial least squares regression (PLSR) and multiple linear regression (MLR) were evaluated to estimate the three measured traits based on vegetation spectral indices (vegetation-SRIs) derived from these methods and their combination. Maize field visits were conducted during the summer seasons from 28 to 30 July 2007 to collect ground reference data concurrent with the acquisition of radiometric ground-based measurements and QuickBird satellite imagery. The results showed that the majority of vegetation-SRIs extracted from radiometric ground-based data and high-resolution satellite images were more effective in estimating LAI, BFW, and Chlm. In general, the vegetation-SRIs of radiometric ground-based data showed higher R2 with measured traits compared to the vegetation-SRIs extracted from high-resolution satellite imagery. The coefficient of determination (R2) of the significant relationships between vegetation-SRIs of both methods and three measured traits varied from 0.64 to 0.89. For example, with QuickBird high-resolution satellite images, the relationships of the green normalized difference vegetation index (GNDVI) with LAI and BFW showed the highest R2 of 0.80 and 0.84, respectively. Overall, the ground-based vegetation-SRIs and the satellite-based indices were found to be in good agreement to assess the measured traits of maize. Both the calibration (Cal.) and validation (Val.) models of PLSR and MLR showed the highest performance in predicting the three measured traits based on the combination of vegetation-SRIs from radiometric ground-based data and high-resolution QuickBird satellite imagery. For example, validation (Val.) models of PLSR and MLR showed the highest performance in predicting the measured traits based on the combination of vegetation-SRIs from radiometric ground-based data and high-resolution QuickBird satellite imagery with R2 (0.91) of both methods for LAI, R2 (0.91–0.93) for BFW respectively, and R2 (0.82) of both methods for Chlm. The models of PLSR and MLR showed approximately the same performance in predicting the three measured traits and no clear difference was found between them and their combinations. In conclusion, the results obtained from this study showed that radiometric ground-based measurements and high spectral resolution remote-sensing imagery have the potential to offer necessary crop monitoring information across well-irrigated, water stress and salinity stress in regions suffering lack of freshwater resources.

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Aerial multispectral imagery for plant disease detection: radiometric calibration necessity assessment

Cited by 40 publications

References 38 publications

Detection of Longhorned Borer Attack and Assessment in Eucalyptus Plantations Using UAV Imagery

Detection of Longhorned Borer Attack and Assessment in Eucalyptus Plantations Using UAV Imagery

Orchard management with small unmanned aerial vehicles: a survey of sensing and analysis approaches

Integration of Radiometric Ground-Based Data and High-Resolution QuickBird Imagery with Multivariate Modeling to Estimate Maize Traits in the Nile Delta of Egypt

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