Multispectral, Aerial Disease Detection for Myrtle Rust (Austropuccinia psidii) on a Lemon Myrtle Plantation

Disease management in agriculture often assumes that pathogens are spread homogeneously across crops. In practice, pathogens can manifest in patches. Currently, disease detection is predominantly carried out by human assessors, which can be slow and expensive. A remote sensing approach holds promise. Current satellite sensors are not suitable to spatially resolve individual plants or lack temporal resolution to monitor pathogenesis. Here, we used multispectral imaging and unmanned aerial systems (UAS) to explore whether myrtle rust (Austropuccinia psidii) could be detected on a lemon myrtle (Backhousia citriodora) plantation. Multispectral aerial imagery was collected from fungicide treated and untreated tree canopies, the fungicide being used to control myrtle rust. Spectral vegetation indices and single spectral bands were used to train a random forest classifier. Treated and untreated trees could be classified with high accuracy (95%). Important predictors for the classifier were the near-infrared (NIR) and red edge (RE) spectral band. Taking some limitations into account, that are discussedherein, our work suggests potential for mapping myrtle rust-related symptoms from aerial multispectral images. Similar studies could focus on pinpointing disease hotspots to adjust management strategies and to feed epidemiological models.

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TOPOGRAPHIC EFFECT ON SPECTRAL VEGETATION INDICES FROM LANDSAT TM DATA: IS TOPOGRAPHIC CORRECTION NECESSARY?

Boletim de Ciências Geodésicas ◽

10.1590/s1982-21702016000100006 ◽

2016 ◽

Vol 22 (1) ◽

pp. 95-107 ◽

Cited By ~ 9

Author(s):

Eder Paulo Moreira* ◽

Márcio de Morisson Valeriano ◽

Ieda Del Arco Sanches ◽

Antonio Roberto Formaggio

Keyword(s):

Near Infrared ◽

Incidence Angle ◽

Spectral Band ◽

Vegetation Indices ◽

Topographic Effect ◽

Topographic Correction ◽

Spectral Bands ◽

Spectral Vegetation Indices ◽

Current Availability ◽

Elevation Model

The full potentiality of spectral vegetation indices (VIs) can only be evaluated after removing topographic, atmospheric and soil background effects from radiometric data. Concerning the former effect, the topographic effect was barely investigated in the context of VIs, despite the current availability correction methods and Digital elevation Model (DEM). In this study, we performed topographic correction on Landsat 5 TM spectral bands and evaluated the topographic effect on four VIs: NDVI, RVI, EVI and SAVI. The evaluation was based on analyses of mean and standard deviation of VIs and TM band 4 (near-infrared), and on linear regression analyses between these variables and the cosine of the solar incidence angle on terrain surface (cos i). The results indicated that VIs are less sensitive to topographic effect than the uncorrected spectral band. Among VIs, NDVI and RVI were less sensitive to topographic effect than EVI and SAVI. All VIs showed to be fully independent of topographic effect only after correction. It can be concluded that the topographic correction is required for a consistent reduction of the topographic effect on the VIs from rugged terrain.

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New spectral vegetation indices based on the near-infrared shoulder wavelengths for remote detection of grassland phytomass

International Journal of Remote Sensing ◽

10.1080/01431161.2011.607195 ◽

2011 ◽

Vol 33 (7) ◽

pp. 2178-2195 ◽

Cited By ~ 39

Author(s):

Loris Vescovo ◽

Georg Wohlfahrt ◽

Manuela Balzarolo ◽

Sebastian Pilloni ◽

Matteo Sottocornola ◽

...

Keyword(s):

Near Infrared ◽

Vegetation Indices ◽

Remote Detection ◽

Spectral Vegetation Indices

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Monitoring and predicting crop growth and analysing agricultural ecosystems by remote sensing

Agricultural and Food Science ◽

10.23986/afsci.72741 ◽

1996 ◽

Vol 5 (3) ◽

pp. 367-376 ◽

Cited By ~ 1

Author(s):

Tsuyoshi Akiyama ◽

Y. Inoue ◽

M. Shibayama ◽

Y. Awaya ◽

N. Tanaka

Keyword(s):

Remote Sensing ◽

Near Infrared ◽

Vegetation Indices ◽

Nutrient Deficiency ◽

The Arctic ◽

Ground Vegetation ◽

Spectral Vegetation Indices ◽

Project Monitoring ◽

Crop Types ◽

Crop Discrimination

LANDSAT/TM data, which are characterized by high spectral/spatial resolutions, are able to contribute to practical agricultural management. In the first part of the paper, the authors review some recent applications of satellite remote sensing in agriculture. Techniques for crop discrimination and mapping have made such rapid progress that we can classify crop types with more than 80% accuracy. The estimation of crop biomass using satellite data, including leaf area, dry and fresh weights, and the prediction of grain yield, has been attempted using various spectral vegetation indices. Plant stresses caused by nutrient deficiency and water deficit have also been analysed successfully. Such information may be useful for farm management. In the latter half of the paper, we introduce the Arctic Science Project, which was carried out under the Science and Technology Agency of Japan collaborating with Finnish scientists. In this project, monitoring of the boreal forest was carried out using LANDSAT data. Changes in the phenology of subarctic ground vegetation, based on spectral properties, were measured by a boom-mounted, four-band spectroradiometer. The turning point dates of the seasonal near-infrared (NIR) and red (R) reflectance factors might indicate the end of growth and the beginning of autumnal tints, respectively.

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Generalization of Vegetation Indices for Monitoring the Terrestrial Biosphere

10.5194/egusphere-egu21-14263 ◽

2021 ◽

Author(s):

Gustau Camps-Valls ◽

Manuel Campos-Taberner ◽

Alvaro Moreno-Martinez ◽

Sophia Walther ◽

Grégory Duveiller ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Multispectral Imaging ◽

Spectral Band ◽

Vegetation Indices ◽

Observation Data ◽

Terrestrial Biosphere ◽

Time Resolved ◽

Area Index ◽

Nonlinear Generalization

Vegetation indices are the most widely used tool in remote sensing and multispectral imaging applications. This paper introduces a nonlinear generalization of the broad family of vegetation indices based on spectral band differences and ratios. The presented indices exploit all higher-order relations of the involved spectral channels, are easy to derive and use, and give some insight on problem complexity. The framework is illustrated to generalize the widely adopted Normalized Difference Vegetation Index (NDVI). Its nonlinear generalization named, kernel NDVI (kNDVI), largely improves performance over NDVI and the recent NIRv in monitoring key vegetation parameters, showing much higher correlation with independent products, such as the MODIS leaf area index (LAI), flux tower gross primary productivity (GPP), and GOME-2 sun-induced fluorescence. The family of indices constitutes a valuable choice for many applications that require spatially explicit and time-resolved analysis of Earth observation data. Reference: "A Unified Vegetation Index for Quantifying the Terrestrial Biosphere", Gustau Camps-Valls, Manuel Campos-Taberner, &#193;lvaro Moreno-Mart&#305;&#769;nez, Sophia Walther, Gr&#233;gory Duveiller, Alessandro Cescatti, Miguel Mahecha, Jordi Mu&#241;oz-Mar&#305;&#769;, Francisco Javier Garc&#305;&#769;a-Haro, Luis Guanter, John Gamon, Martin Jung, Markus Reichstein, Steven W. Running. Science Advances, in press, 2021

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Optimal Spatial Resolution for the Detection and Discrimination of Archaeological Proxies in Areas with Spectral Heterogeneity

Remote Sensing ◽

10.3390/rs12010136 ◽

2020 ◽

Vol 12 (1) ◽

pp. 136 ◽

Cited By ~ 3

Author(s):

Athos Agapiou

Keyword(s):

Spatial Resolution ◽

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Spectral Band ◽

Vegetation Indices ◽

Archaeological Site ◽

Area Of Interest ◽

First Case

Subsurface targets can be detected from space-borne sensors via archaeological proxies, known in the literature as cropmarks. A topic that has been limited in its investigation in the past is the identification of the optimal spatial resolution of satellite sensors, which can better support image extraction of archaeological proxies, especially in areas with spectral heterogeneity. In this study, we investigated the optimal spatial resolution (OSR) for two different cases studies. OSR refers to the pixel size in which the local variance, of a given area of interest (e.g., archaeological proxy), is minimized, without losing key details necessary for adequate interpretation of the cropmarks. The first case study comprises of a simulated spectral dataset that aims to model a shallow buried archaeological target cultivated on top with barley crops, while the second case study considers an existing site in Cyprus, namely the archaeological site of “Nea Paphos”. The overall methodology adopted in the study is composed of five steps: firstly, we defined the area of interest (Step 1), then we selected the local mean-variance value as the optimization criterion of the OSR (Step 2), while in the next step (Step 3), we spatially aggregated (upscale) the initial spectral datasets for both case studies. In our investigation, the spectral range was limited to the visible and near-infrared part of the spectrum. Based on these findings, we determined the OSR (Step 4), and finally, we verified the results (Step 5). The OSR was estimated for each spectral band, namely the blue, green, red, and near-infrared bands, while the study was expanded to also include vegetation indices, such as the Simple Ratio (SR), the Atmospheric Resistance Vegetation Index (ARVI), and the Normalized Difference Vegetation Index (NDVI). The outcomes indicated that the OSR could minimize the local spectral variance, thus minimizing the spectral noise, and, consequently, better support image processing for the extraction of archaeological proxies in areas with high spectral heterogeneity.

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Illumination Geometry and Flying Height Influence Surface Reflectance and NDVI Derived from Multispectral UAS Imagery

Drones ◽

10.3390/drones3030055 ◽

2019 ◽

Vol 3 (3) ◽

pp. 55 ◽

Cited By ~ 10

Author(s):

Daniel Stow ◽

Caroline J. Nichol ◽

Tom Wade ◽

Jakob J. Assmann ◽

Gillian Simpson ◽

...

Keyword(s):

Near Infrared ◽

Vegetation Indices ◽

Flying Height ◽

Unmanned Aerial Systems ◽

Radiometric Calibration ◽

Very High Spatial Resolution ◽

Vegetation Surveys ◽

Solar Noon ◽

Aerial Systems ◽

Reflectance Measurements

Small unmanned aerial systems (UAS) have allowed the mapping of vegetation at very high spatial resolution, but a lack of standardisation has led to uncertainties regarding data quality. For reflectance measurements and vegetation indices (Vis) to be comparable between sites and over time, careful flight planning and robust radiometric calibration procedures are required. Two sources of uncertainty that have received little attention until recently are illumination geometry and the effect of flying height. This study developed methods to quantify and visualise these effects in imagery from the Parrot Sequoia, a UAV-mounted multispectral sensor. Change in illumination geometry over one day (14 May 2018) had visible effects on both individual images and orthomosaics. Average near-infrared (NIR) reflectance and NDVI in regions of interest were slightly lower around solar noon, and the contrast between shadowed and well-illuminated areas increased over the day in all multispectral bands. Per-pixel differences in NDVI maps were spatially variable, and much larger than average differences in some areas. Results relating to flying height were inconclusive, though small increases in NIR reflectance with height were observed over a black sailcloth tarp. These results underline the need to consider illumination geometry when carrying out UAS vegetation surveys.

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Peach Flower Monitoring Using Aerial Multispectral Imaging

10.20944/preprints201611.0034.v1 ◽

2016 ◽

Author(s):

Ryan Horton ◽

Esteban Cano ◽

Duke Bulanon ◽

Esmaeil Fallahi

Keyword(s):

Image Processing ◽

Crop Production ◽

Near Infrared ◽

Multispectral Imaging ◽

Vegetation Indices ◽

Fruit Trees ◽

Training System ◽

Processing Algorithm ◽

Image Processing Algorithm ◽

Individual Tree

One of the tools for optimal crop production is regular monitoring and assessment of crops. During the growing season of fruit trees, the bloom period has increased photosynthetic rates that correlate with the fruiting process. This paper presents the development of an image processing algorithm to detect peach blossoms on trees. Images of an experimental peach orchard were acquired from the Parma Research and Extension Center of the University of Idaho using an off-the-shelf unmanned aerial system (UAS), equipped with a multispectral camera (Near-infrared, Green, Blue). The orchard has different stone fruit varieties and different plant training system. Individual tree images (high-resolution) and arrays of trees images (low-resolution) were acquired to evaluate the detection capability. The image processing algorithm was based on different vegetation indices. Initial results showed that the image processing algorithm could detect peach blossoms and demonstrate good potential as a monitoring tool for orchard management.

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UAS-based multi-angular remote sensing of the effects of soil management strategies on grapevine

OENO One ◽

10.20870/oeno-one.2015.49.2.91 ◽

2015 ◽

Vol 49 (2) ◽

pp. 85 ◽

Cited By ~ 3

Author(s):

Rebecca Retzlaff ◽

Daniel Molitor ◽

Marc Behr ◽

Christian Bossung ◽

Gilles Rock ◽

...

Keyword(s):

Near Infrared ◽

Management Strategies ◽

Vegetation Indices ◽

Soil Management ◽

Flexible Tool ◽

Plant Vigour ◽

Linear Discriminant ◽

Chlorophyll Contents ◽

Physiological Variables ◽

Box Plots

Aims: The present investigation in a Luxembourgish vineyard aimed at evaluating the potential of multispectral, multi-angular UAS (unmanned aerial system) imagery to separate four soil management strategies, to predict physiological variables (chlorophyll, nitrogen, yield etc.) and to follow seasonal changes in grapevine physiology in relation to soil management.Methods and results: Multi-angular (nadir and 45° off-nadir) multispectral imageries (530-900 nm) were taken in the years 2011 and 2012. Image grey values and reflectance-derived vegetation indices were computed and canopy and vigour properties were monitored in the field. All four soil management strategies could be significantly discriminated (box-plots, linear discriminant analysis) and vegetation properties estimated (linear regression) in 2011. For 2012, global models predicted chlorophyll contents and nitrogen balance index values with a R²cv of 0.65 and 0.76, respectively.Conclusions: Soil management strategies strongly affect plant vigour and reflectance. Differences were best detectable by oblique visible/near-infrared (Vis/nIR) UAS data of illuminated canopies.Significance and impact of the study: UAS imaging is a flexible tool for applications in precision viticulture.

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Evaluating the suitability of hyper- and multispectral imaging to detect foliar symptoms of the grapevine trunk disease Esca in vineyards

Plant Methods ◽

10.1186/s13007-020-00685-3 ◽

2020 ◽

Vol 16 (1) ◽

Author(s):

Nele Bendel ◽

Anna Kicherer ◽

Andreas Backhaus ◽

Hans-Christian Klück ◽

Udo Seiffert ◽

...

Keyword(s):

Data Acquisition ◽

Near Infrared ◽

Multispectral Imaging ◽

Original Data ◽

Economic Losses ◽

Disease Detection ◽

Field Detection ◽

Multispectral Data ◽

True Incidence ◽

Grapevine Trunk Diseases

Abstract Background Grapevine trunk diseases (GTDs) such as Esca are among the most devastating threats to viticulture. Due to the lack of efficient preventive and curative treatments, Esca causes severe economic losses worldwide. Since symptoms do not develop consecutively, the true incidence of the disease in a vineyard is difficult to assess. Therefore, an annual monitoring is required. In this context, automatic detection of symptoms could be a great relief for winegrowers. Spectral sensors have proven to be successful in disease detection, allowing a non-destructive, objective, and fast data acquisition. The aim of this study is to evaluate the feasibility of the in-field detection of foliar Esca symptoms over three consecutive years using ground-based hyperspectral and airborne multispectral imaging. Results Hyperspectral disease detection models have been successfully developed using either original field data or manually annotated data. In a next step, these models were applied on plant scale. While the model using annotated data performed better during development, the model using original data showed higher classification accuracies when applied in practical work. Moreover, the transferability of disease detection models to unknown data was tested. Although the visible and near-infrared (VNIR) range showed promising results, the transfer of such models is challenging. Initial results indicate that external symptoms could be detected pre-symptomatically, but this needs further evaluation. Furthermore, an application specific multispectral approach was simulated by identifying the most important wavelengths for the differentiation tasks, which was then compared to real multispectral data. Even though the ground-based multispectral disease detection was successful, airborne detection remains difficult. Conclusions In this study, ground-based hyperspectral and airborne multispectral approaches for the detection of foliar Esca symptoms are presented. Both sensor systems seem to be suitable for the in-field detection of the disease, even though airborne data acquisition has to be further optimized. Our disease detection approaches could facilitate monitoring plant phenotypes in a vineyard.

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Characterisation of the Spectral-Temporal Pattern of the Crambe Crop Using Hyperspectral Sensors

Journal of Agricultural Science ◽

10.5539/jas.v9n11p220 ◽

2017 ◽

Vol 9 (11) ◽

pp. 220 ◽

Cited By ~ 1

Author(s):

Octavio Henrique Viana ◽

Erivelto Mercante ◽

Henrique Felipetto ◽

Douglas Kusminski ◽

Helmuth Guilherme Bleil Jr

Keyword(s):

Vegetative Growth ◽

Temporal Pattern ◽

Near Infrared ◽

Spectral Band ◽

Vegetation Indices ◽

Phenological Stages ◽

Maturation Stages ◽

Oil Characteristics ◽

Cellular Water ◽

Temporal Profiles

Crambe is an oleaginous plant mainly cultivated in Brazil due to its oil characteristics that provide stability to oxidation, qualifying it for the use in a variety of products. Understanding the spectral-temporal pattern of the crambe crop is important to identify and quantify already cultivated areas via remote sensing. This study spectrally characterised the plant, seeking to relate the spectral pattern to the phenological stages of the crop throughout its development. The spectral information was obtained by passive terrestrial sensors in two harvests, thus generating a spectral-temporal pattern and the crambe temporal profile through the vegetation indices NDVI and SAVI. During the phenological stages of the seedling and the beginning of the vegetative growth, the red spectral band showed higher values of reflectance; this occurred because the crop had not yet completely covered the soil. Stages at the end of the vegetative growth and the beginning of the flowering, there was a higher reflectance in the near infrared and a lower reflectance in the mid-infrared. For the granulation and maturation stages, the reflectance in the mean and near infrared reduced due to leaf senescence and loss of cellular water content. The NDVI and SAVI temporal profiles demonstrate linear growth up to the vegetative peak, which occurs between the end of the phenological stage of the vegetative growth and the beginning of the flowering and highest amount of green biomass. At the beginning of grain formation and filling, yellowing of leaves and senescence, granulation and maturation stages, the values reduced.

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