Mapping the condition of macadamia tree crops using multi-spectral UAV and WorldView-3 imagery

Johansen, Kasper; Duan, Qibin; Tu, Yu-Hsuan; Searle, Chris; Wu, Dan; Robson, Andrew; McCabe, Matthew F.

doi:10.1016/j.isprsjprs.2020.04.017

“…This result is an important finding in terms of the use of UAS, particularly the NIR band for mapping variability in tree health. High NIR reflectance is synonymous with high tree vigour [30,31], and as such, using these image layers where high NIR reflectance is the result of a BRDF effect as opposed to actual tree variability may lead to incorrect assessment of crop condition and management decisions. For the banana datasets, we found that the empirical correction provided moderate consistency rates in the green and red bands even though the data was acquired at 50 m AGL.…”

Section: Brdf Correction Consistency Assessmentmentioning

confidence: 99%

Assessing Radiometric Correction Approaches for Multi-Spectral UAS Imagery for Horticultural Applications

Tu

¹

,

Phinn

²

,

Johansen

³

et al. 2018

Self Cite

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Multi-spectral imagery captured from unmanned aerial systems (UAS) is becoming increasingly popular for the improved monitoring and managing of various horticultural crops. However, for UAS-based data to be used as an industry standard for assessing tree structure and condition as well as production parameters, it is imperative that the appropriate data collection and pre-processing protocols are established to enable multi-temporal comparison. There are several UAS-based radiometric correction methods commonly used for precision agricultural purposes. However, their relative accuracies have not been assessed for data acquired in complex horticultural environments. This study assessed the variations in estimated surface reflectance values of different radiometric corrections applied to multi-spectral UAS imagery acquired in both avocado and banana orchards. We found that inaccurate calibration panel measurements, inaccurate signal-to-reflectance conversion, and high variation in geometry between illumination, surface, and sensor viewing produced significant radiometric variations in at-surface reflectance estimates. Potential solutions to address these limitations included appropriate panel deployment, site-specific sensor calibration, and appropriate bidirectional reflectance distribution function (BRDF) correction. Future UAS-based horticultural crop monitoring can benefit from the proposed solutions to radiometric corrections to ensure they are using comparable image-based maps of multi-temporal biophysical properties.

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“…As such, measurements of these parameters can provide growers with a strong indication of plant health or vigour, photosynthetic capacity and yield potential [13,14]. In most Australian horticultural industries, such assessment is usually conducted by on-ground visual evaluation, which is time-consuming, labour-intensive, subjective and often inconsistent [7,[15][16][17]. Therefore, there is a demand for more efficient, accurate and quantitative alternatives for such assessments.…”

Section: Introductionmentioning

confidence: 99%

“…This assessment is based on tree canopy structure, and hence should be quantifiable from the aforementioned structural attributes that derived from UAS imagery. Johansen, Duan, Tu, Searle, Wu, Phinn and Robson [15] used near-infrared (NIR) brightness as the input feature of a random forest classifier to predict the condition of macadamia trees. Different growing stages and levels of stress for avocado trees produce changes in overall canopy form [33], and the amount of leaf, stem and trunk biomass, these resulted in different NIR reflectance levels [34][35][36] which were detected by the classifier in the study.…”

Section: Introductionmentioning

confidence: 99%

Measuring Canopy Structure and Condition Using Multi-Spectral UAS Imagery in a Horticultural Environment

Tu

¹

,

Johansen

²

,

Phinn

³

et al. 2019

Self Cite

View full text Add to dashboard Cite

Tree condition, pruning and orchard management practices within intensive horticultural tree crop systems can be determined via measurements of tree structure. Multi-spectral imagery acquired from an unmanned aerial system (UAS) has been demonstrated as an accurate and efficient platform for measuring various tree structural attributes, but research in complex horticultural environments has been limited. This research established a methodology for accurately estimating tree crown height, extent, plant projective cover (PPC) and condition of avocado tree crops, from a UAS platform. Individual tree crowns were delineated using object-based image analysis. In comparison to field measured canopy heights, an image-derived canopy height model provided a coefficient of determination (R2) of 0.65 and relative root mean squared error of 6%. Tree crown length perpendicular to the hedgerow was accurately mapped. PPC was measured using spectral and textural image information and produced an R2 value of 0.62 against field data. A random forest classifier was applied to assign tree condition into four categories in accordance with industry standards, producing out-of-bag accuracies >96%. Our results demonstrate the potential of UAS-based mapping for the provision of information to support the horticulture industry and facilitate orchard-based assessment and management.

show abstract

“…As such, measurements of these parameters can provide growers with a strong indication of plant health or vigour, photosynthetic capacity and yield potential [13,14]. In most Australian horticultural industries, such assessment is usually conducted by on-ground visual evaluation, which is timeconsuming, labour-intensive, subjective and often inconsistent [7,[15][16][17]. Therefore, there is a demand for more efficient, accurate and quantitative alternatives for such assessments.…”

Section: Introductionmentioning

confidence: 99%

“…This assessment is based on tree canopy structure, and hence should be quantifiable from the aforementioned structural attributes that derived from UAS imagery. Johansen, Duan, Tu, Searle, Wu, Phinn and Robson [15] used NIR brightness as the input feature of a random forest classifier to predict the condition of macadamia trees. Different growing stages and levels of stress for avocado trees produce changes in overall canopy form [33], and the amount of leaf, stem and trunk biomass, these resulted in different NIR reflectance levels [34][35][36] which were detected by the classifier in the study.…”

Section: Introductionmentioning

confidence: 99%

Measuring Canopy Structure and Condition Using Multi-Spectral UAS Imagery in a Horticultural Environment

Tu¹,

Johansen²,

Phinn³

et al. 2018

Preprint

Self Cite

0

View full text Add to dashboard Cite

Tree condition, pruning and orchard management practices within intensive horticultural tree crop systems can be determined via measurements of tree structure. Multi-spectral imagery acquired from an unmanned aerial system (UAS) has been demonstrated as an accurate and efficient platform for measuring various tree structural attributes, but research in complex horticultural environments has been limited. This research established a methodology for accurately estimating tree crown height, extent, plant projective cover (PPC) and condition of avocado tree crops, from a UAS platform. Individual tree crowns were delineated using object-based image analysis. In comparison to field measured canopy heights, an image-derived canopy height model provided a coefficient of determination (R2) of 0.65 and relative root mean squared error of 6%. Tree crown length perpendicular to the hedgerow was accurately mapped. PPC was measured using spectral and textural image information and produced an R2 value of 0.62 against field data. A random forest classifier was applied to assign tree condition into four categories in accordance with industry standards, producing out-of-bag accuracies >96%. Our results demonstrate the potential of UAS-based mapping for the provision of information to support the horticulture industry and facilitate orchard-based assessment and management.

show abstract

Mapping the condition of macadamia tree crops using multi-spectral UAV and WorldView-3 imagery

Cited by 50 publications

References 60 publications

Assessing Radiometric Correction Approaches for Multi-Spectral UAS Imagery for Horticultural Applications

Assessing Radiometric Correction Approaches for Multi-Spectral UAS Imagery for Horticultural Applications

Measuring Canopy Structure and Condition Using Multi-Spectral UAS Imagery in a Horticultural Environment

Measuring Canopy Structure and Condition Using Multi-Spectral UAS Imagery in a Horticultural Environment

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