Hyperspectral measurements of yellow rust and fusarium head blight in cereal crops: Part 1: Laboratory study

Whetton, Rebecca; Hassall, Kirsty L.; Waine, Toby; Mouazen, Abdul Mounem

doi:10.1016/j.biosystemseng.2017.11.008

Cited by 43 publications

(38 citation statements)

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“…However, light intensity also influences leaf thickness [10], and then it often makes the output of the meter obscure [11]. In contrast, hyperspectral reflectance measurements may be used for detecting various responses from crops [12] or evaluating vegetation properties [13,14], and the use of reflectance for estimating chlorophyll content is being seriously considered. Some pigments, such as chlorophylls and carotenoids, absorb energy strongly in the ultraviolet, blue, and red regions, and then the reflectance and transmittance are weak [15].…”

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confidence: 99%

Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms

Sonobe

Hirono

2020

Plants

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Tea trees are kept in shaded locations to increase their chlorophyll content, which influences green tea quality. Therefore, monitoring change in chlorophyll content under low light conditions is important for managing tea trees and producing high-quality green tea. Hyperspectral remote sensing is one of the most frequently used methods for estimating chlorophyll content. Numerous studies based on data collected under relatively low-stress conditions and many hyperspectral indices and radiative transfer models show that shade-grown tea performs poorly. The performance of four machine learning algorithms-random forest, support vector machine, deep belief nets, and kernel-based extreme learning machine (KELM)-in evaluating data collected from tea leaves cultivated under different shade treatments was tested. KELM performed best with a root-mean-square error of 8.94 ± 3.05 µg cm −2 and performance to deviation values from 1.70 to 8.04 for the test data. These results suggest that a combination of hyperspectral reflectance and KELM has the potential to trace changes in the chlorophyll content of shaded tea leaves.Ultraviolet and visible light (UV-vis) spectroscopy and high-performance liquid chromatography (HPLC) techniques have previously been used to quantify chlorophyll content. These are destructive methods and may enforce a limited sample size because they are expensive and labour-intensive. It is also not always possible to apply these methods in situ. Alternately, some portable chlorophyll content meters, such as CL-01, SPAD-502, Dualex, and CCM-200, have been used to estimate chlorophyll content [9]. However, light intensity also influences leaf thickness [10], and then it often makes the output of the meter obscure [11]. In contrast, hyperspectral reflectance measurements may be used for detecting various responses from crops [12] or evaluating vegetation properties [13,14], and the use of reflectance for estimating chlorophyll content is being seriously considered. Some pigments, such as chlorophylls and carotenoids, absorb energy strongly in the ultraviolet, blue, and red regions, and then the reflectance and transmittance are weak [15]. The reflectance from vegetation is characterised by the low reflectance over the blue (400-500 nm) and red (650-690 nm) spectral regions due to strong absorption by chlorophylls plus carotenoids in the blue wavelengths and chlorophyll a in the red wavelengths, and the steep rise in reflectance between 680 and 750 nm is caused by the chlorophylls increasing to absorb at wavelengths beyond 700 nm [16]. Based on these features, some empirical approaches, such as vegetation indices, which are generally based on a few narrow or broad spectral bands, are convenient for use to estimate chlorophyll content. Most vegetation indices for chlorophyll content use wavelengths ranging from 400 to 860 nanometres [17][18][19] or the red edge (680 to 750 nm) [20,21]. However, some datasets, such as LOPEX, CALMIT, ANGERS, and HAWAII, that have been used for the development of vegetation indi...

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Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms

Sonobe

Hirono

2020

Plants

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“…Spectral sensors measure the light reflected from the crop canopy [1]. During pathogen attack and disease development on the crop leaf, diseases establish a spectral fingerprint in the reflected leaf signature [8][9][10]. These shifts of the signature can be detected using spectral sensors, particularly in the electromagnetic spectrum from 400-2500 nm [11].…”

Section: Of 20mentioning

confidence: 99%

In-Field Detection of Yellow Rust in Wheat on the Ground Canopy and UAV Scale

Bohnenkamp

Behmann

Mahlein³

2019

Remote Sensing

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The application of hyperspectral imaging technology for plant disease detection in the field is still challenging. Existing equipment and analysis algorithms are adapted to highly controlled environmental conditions in the laboratory. However, only real time information from the field scale is able to guide plant protection measures and to optimize the use of resources. At the field scale, many parameters such as the optimal measurement distance, informative feature sets, and suitable algorithms have not been investigated. In this study, the hyperspectral detection and quantification of yellow rust in wheat was evaluated using two measurement platforms: a ground-based vehicle and an unmanned aerial vehicle (UAV). Different disease development stages and disease severities were provided in a plot-based field experiment. Measurements were performed weekly during the vegetation period. Data analysis was performed by three prediction algorithms with a focus on the selection of optimal feature sets. In this context, the across-scale application of optimized feature sets, an approach of information transfer between scales, was also evaluated. Relevant aspects for an on-line disease assessment in the field integrating affordable sensor technology, sensor spatial resolution, compact analysis models, and fast evaluation have been outlined and reflected upon. For the first time, a hyperspectral imaging observation experiment of a plant disease was comparatively performed at two scales, ground canopy and UAV.

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“…The methodologies and models for application of the hyperspectral imager and crop disease detection was utilised from our previous (Whetton et al, 2017(Whetton et al, , 2018a(Whetton et al, and 2018b…”

Section: Disease and Canopy Data Collectionmentioning

confidence: 99%

“…The prediction results of these models are shown in Table 2. More detailed information about the calibration and validation of the FHB and yellow rust models can be found in Whetton et al (2018a and2018b). The same approach was used for the calibration and validation of models to predict soil properties (e.g., MC, OC, TN and CEC).…”

Section: Spectral Modelling Of Disease and Soil Propertiesmentioning

confidence: 99%

“…Site-specific management requires high resolution data on all relevant factors. High resolution data collection for specific diseases, crop canopy, and soil properties is possible with the use of online crop and soil sensors (Kuang et al, 2012;Kuang and Mouazen, 2013;Whetton et al, 2018a). Fusing these different layers of information allows for the creation of management zones (MZ).…”

Section: Introductionmentioning

confidence: 99%

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Evaluating management zone maps for variable rate fungicide application and selective harvest

Whetton

Waine

Mouazen

2018

Computers and Electronics in Agriculture

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Currently the majority of crop protection approaches are based on homogeneous rate fungicide application (HRFA) over the entire field area. With the increasing pressures on fungicide applications, associated with increased environmental impact and cost, an alternative approach based on variable rate fungicide application (VRFA) and selective harvest (SH) is needed. This study was undertaken to evaluate the economic viability of adopting VRSA and SH in winter wheat and the environmental benefit in terms of chemical reduction is also discussed. High resolution data of crop canopy properties, yellow rust, fusarium head blight (FHB), soil properties and yield were subjected to k-means cluster analysis to develop management zone (MZ) maps for one field in Bedfordshire, UK. Virtual cost-benefit analysis for VRFA was performed on three fungicide application timings, namely, T1 and T2 focused on yellow rust, and T3 focused on FHB. Cost-benefit analysis was also applied to SH, which assumed different selling prices between healthy and grain downgraded due to mycotoxin infection. Results showed that in this study VRFA allowed for fungicide reductions of 22.24% at T1 and T2 and 25.93% at T3 when compared to HRFA. SH reduced the risk of market rejection due to low quality and high mycotoxin content. Gross profit of combining SH and VRFA was £83.35 per hectare per year, divided into SH £48.04 ha-1 , and VRFA £8.8ha-1 for T1 and T2 and £17.7 ha-1 for T3. Total profit when considering soil and crop scanning costs would be £66.85 ha-1 per

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Hyperspectral measurements of yellow rust and fusarium head blight in cereal crops: Part 1: Laboratory study

Cited by 43 publications

References 43 publications

Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms

Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms

In-Field Detection of Yellow Rust in Wheat on the Ground Canopy and UAV Scale

Evaluating management zone maps for variable rate fungicide application and selective harvest

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