Estimation of plant health in a sorghum field infected with anthracnose using a fixed-wing unmanned aerial system

Pugh, N. Ace; Han, Xiongzhe; Collins, Scott D.; Thomasson, J. Alex; Cope, Dale; Chang, Anjin; Jung, Jinha; Isakeit, Thomas; Prom, Louis K.; Carvalho, G. M. C.; Gates, Ian T.; Vree, Andrew; Bagnall, G. Cody; Rooney, William L.

doi:10.1080/15427528.2018.1535462

Cited by 5 publications

(11 citation statements)

References 32 publications

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“…This is consistent with reports by Pugh et al. (2018), who showed a strong linear relationship between NDVI and AUDPC scores. Huang et al.…”

Section: Discussionsupporting

confidence: 93%

“…Anthracnose severity was measured using the area under the disease progress curve (AUDPC).To create the AUDPC, anthracnose incidence and severity scores (0–9) were recorded as the proportion of plants in a given plot showing typical symptoms of anthracnose infection, i.e., grey diseased tissue and or necrotic lesions (Pugh et al., 2018). The utilized scale was adapted from the original to include level 0 for no symptoms.…”

Section: Methodsmentioning

confidence: 99%

“…This indirect selection is based on specific features (e.g., biomass via NDVI; chlorophyll content via a CIg) that compose agronomically relevant traits (Li & Shi, 2018). In sorghum [ Sorghum bicolor (L.) Moench], multiple indices have been shown to be correlated with overall plant health (Pugh et al., 2018; Stanton et al., 2017), GY (Pugh et al., 2018; Shafian et al., 2018; Stanton et al., 2017), leaf area index (Shafian et al., 2018), senescence (Potgieter et al., 2017), fractional vegetation cover (Shafian et al., 2018), biomass yield, and chlorophyll and nitrogen content (Li & Shi, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Grain yield is dependent upon various characteristics and is polygenic in nature (Boyles et al., 2017). As these traits are correlated (Pugh et al., 2018), an identification of the appropriate phenological stage(s) at which the signal of genotype effect is maximized for both would lead to significant improvement of HTP‐based selection. The relevant cost of phenotyping for both traits makes it a suitable subject for a proof‐of‐concept study using HTP, thus producing results of benefit to breeders in the decision‐making process.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of UAS‐based high‐throughput phenotyping to estimate plant health and grain yield in sorghum

Galli

Horne

Collins

et al. 2020

The Plant Phenome Journal

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High-throughput phenotyping (HTP) has enabled the acquisition of vast amounts of data. Therefore, finding the most informative phenological stage(s) and high-throughput traits could lead to significant optimization of HTP-assisted selection. An investigation as to when phenotypic data should be collected and how it should be processed from unmanned aerial system (UAS) imagery for the optimization and assessment of two primary traits in grain sorghum [Sorghum bicolor (L). Moench], namely, grain yield and plant health (based on anthracnose scores) was conducted. By evaluating multiple flight dates across the growing season via multispectral UAS-based imagery, a set of scenarios composed of combinations of flight dates and vegetation indices were constructed for analysis. In this sense, results showed no increase in predictive ability when combining multiple vegetation indices. Hence, using only an index with a higher predictive ability (e.g., normalized difference vegetation index (NDVI) or modified simple ratio (MSR) for plant health with 0.75; and any tested index but chlorophyll index (CIg) for grain yield with ∼0.55) is recommended. Likewise, the combining of multiple flights did not result in a significant increase in predictive ability for either primary trait. Thus, we observed that a single flight for each trait (e.g., 121 d after sowing with 0.81 for plant health; 104 d after sowing with 0.59 for grain yield) was optimal. Concerning, the predictive algorithms examined, partial least squares regression (PLSR) and neural network, results were similar, with PLSR generally outperforming. In addition, we discuss our findings from an application standpoint of a field-based breeding program and suggest additional optimization options. Abbreviations: AUDPC, area under the disease progress curve; CIg, chlorophyll index; CIre, red-edge chlorophyll index; DAS, days after sowing; GY, grain yield; HTP, high-throughput phenotyping; ML, machine learning; MSR, modified simple ratio; MSRre, red-edge modified simple ratio; NDVI, normalized difference vegetation index; NDVIre, red-edge normalized difference vegetation index; PLSR, partial least squares regression; UAS, unmanned aerial system. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…This is consistent with reports by Pugh et al. (2018), who showed a strong linear relationship between NDVI and AUDPC scores. Huang et al.…”

Section: Discussionsupporting

confidence: 93%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimization of UAS‐based high‐throughput phenotyping to estimate plant health and grain yield in sorghum

Galli

Horne

Collins

et al. 2020

The Plant Phenome Journal

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“…With UAS image data beyond the visible spectrum now routinely being collected in many breeding programs, there is potential to enhance counting sorghum panicles with data from other non-visible wavelengths. Multispectral imagery, particularly data from the red-edge and near-infrared bands, has been applied for different aspects of plant phenotyping including in characterizing senescence patterns sorghum breading lines [62], in assessing nitrogen and chlorophyll content [63] and in assessing plant stress and disease [9,64,65]. The ability of these non-visible bands to model such subtle changes in plant condition should contribute to better discrimination of panicles from objects that are spectrally similar in the visible spectrum such as ground and dried foliage.…”

Section: Discussionmentioning

confidence: 99%

A Deep Learning Semantic Segmentation-Based Approach for Field-Level Sorghum Panicle Counting

Malambo

Popescu

et al. 2019

Remote Sensing

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Small unmanned aerial systems (UAS) have emerged as high-throughput platforms for the collection of high-resolution image data over large crop fields to support precision agriculture and plant breeding research. At the same time, the improved efficiency in image capture is leading to massive datasets, which pose analysis challenges in providing needed phenotypic data. To complement these high-throughput platforms, there is an increasing need in crop improvement to develop robust image analysis methods to analyze large amount of image data. Analysis approaches based on deep learning models are currently the most promising and show unparalleled performance in analyzing large image datasets. This study developed and applied an image analysis approach based on a SegNet deep learning semantic segmentation model to estimate sorghum panicles counts, which are critical phenotypic data in sorghum crop improvement, from UAS images over selected sorghum experimental plots. The SegNet model was trained to semantically segment UAS images into sorghum panicles, foliage and the exposed ground using 462, 250 × 250 labeled images, which was then applied to field orthomosaic to generate a field-level semantic segmentation. Individual panicle locations were obtained after post-processing the segmentation output to remove small objects and split merged panicles. A comparison between model panicle count estimates and manually digitized panicle locations in 60 randomly selected plots showed an overall detection accuracy of 94%. A per-plot panicle count comparison also showed high agreement between estimated and reference panicle counts (Spearman correlation ρ = 0.88, mean bias = 0.65). Misclassifications of panicles during the semantic segmentation step and mosaicking errors in the field orthomosaic contributed mainly to panicle detection errors. Overall, the approach based on deep learning semantic segmentation showed good promise and with a larger labeled dataset and extensive hyper-parameter tuning, should provide even more robust and effective characterization of sorghum panicle counts.

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A novel way to validate UAS-based high-throughput phenotyping protocols using in silico experiments for plant breeding purposes

et al. 2020

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Estimation of plant health in a sorghum field infected with anthracnose using a fixed-wing unmanned aerial system

Cited by 5 publications

References 32 publications

Optimization of UAS‐based high‐throughput phenotyping to estimate plant health and grain yield in sorghum

Optimization of UAS‐based high‐throughput phenotyping to estimate plant health and grain yield in sorghum

A Deep Learning Semantic Segmentation-Based Approach for Field-Level Sorghum Panicle Counting

A novel way to validate UAS-based high-throughput phenotyping protocols using in silico experiments for plant breeding purposes

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