Assessing winter wheat foliage disease severity using aerial imagery acquired from small Unmanned Aerial Vehicle (UAV)

Bhandari, Mahendra; Ibrahim, Amir M. H.; Xue, Qingwu; Jung, Jinha; Chang, Anjin; Rudd, Jackie C.; Maeda, Murilo M.; Rajan, Nithya; Neely, Haly L.; Landivar, Juan

doi:10.1016/j.compag.2020.105665

Cited by 44 publications

(16 citation statements)

References 21 publications

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“…8 ); we believe this is likely due to features of predictions from relatedness, not a shared physiological cause. This study confirmed previous findings that UAS HTP can facilitate understanding of genotypic response to disease in a manner complementary to traditional disease phenotyping methods 59 , 60 , as well as to replace subjective visual assessment of senescence with quantitative, phenotyping-based screens of large breeding populations 20 . Preliminary findings in this study highlight the potential for grain filling period to serve as a predictor of yield.…”

Section: Discussionsupporting

confidence: 89%

Phenomic data-facilitated rust and senescence prediction in maize using machine learning algorithms

DeSalvio

Adak

Murray

et al. 2022

Sci Rep

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Current methods in measuring maize (Zea mays L.) southern rust (Puccinia polyspora Underw.) and subsequent crop senescence require expert observation and are resource-intensive and prone to subjectivity. In this study, unoccupied aerial system (UAS) field-based high-throughput phenotyping (HTP) was employed to collect high-resolution aerial imagery of elite maize hybrids planted in the 2020 and 2021 growing seasons, with 13 UAS flights obtained from 2020 and 17 from 2021. In total, 36 vegetation indices (VIs) were extracted from mosaicked aerial images that served as temporal phenomic predictors for southern rust scored in the field and senescence as scored using UAS-acquired mosaic images. Temporal best linear unbiased predictors (TBLUPs) were calculated using a nested model that treated hybrid performance as nested within flights in terms of rust and senescence. All eight machine learning regressions tested (ridge, lasso, elastic net, random forest, support vector machine with radial and linear kernels, partial least squares, and k-nearest neighbors) outperformed a general linear model with both higher prediction accuracies (92–98%) and lower root mean squared error (RMSE) for rust and senescence scores (linear model RMSE ranged from 65.8 to 2396.5 across all traits, machine learning regressions RMSE ranged from 0.3 to 17.0). UAS-acquired VIs enabled the discovery of novel early quantitative phenotypic indicators of maize senescence and southern rust before being detectable by expert annotation and revealed positive correlations between grain filling time and yield (0.22 and 0.44 in 2020 and 2021), with practical implications for precision agricultural practices.

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Section: Discussionsupporting

confidence: 89%

Phenomic data-facilitated rust and senescence prediction in maize using machine learning algorithms

DeSalvio

Adak

Murray

et al. 2022

Sci Rep

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“…tritici Eriks.) and powdery mildew of wheat, respectively [82,83]. The spectral location of these VIs is consistent with the dynamic of pathogenesis discussed above.…”

Section: Discussionsupporting

confidence: 85%

Early Detection of Wild Rocket Tracheofusariosis Using Hyperspectral Image-Based Machine Learning

et al. 2021

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Fusarium oxysporum f. sp. raphani is responsible for wilting wild rocket (Diplotaxis tenuifolia L. [D.C.]). A machine learning model based on hyperspectral data was constructed to monitor disease progression. Thus, pathogenesis after artificial inoculation was monitored over a 15-day period by symptom assessment, qPCR pathogen quantification, and hyperspectral imaging. The host colonization by a pathogen evolved accordingly with symptoms as confirmed by qPCR. Spectral data showed differences as early as 5-day post infection and 12 hypespectral vegetation indices were selected to follow disease development. The hyperspectral dataset was used to feed the XGBoost machine learning algorithm with the aim of developing a model that discriminates between healthy and infected plants during the time. The multiple cross-prediction strategy of the pixel-level models was able to detect hyperspectral disease profiles with an average accuracy of 0.8. For healthy pixel detection, the mean Precision value was 0.78, the Recall was 0.88, and the F1 Score was 0.82. For infected pixel detection, the average evaluation metrics were Precision: 0.73, Recall: 0.57, and F1 Score: 0.63. Machine learning paves the way for automatic early detection of infected plants, even a few days after infection.

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“…It was highly surprising that senescence, an end-of-life measure, had some predictability as early as 17 days after planting (Figure 8); we believe this is likely due to features of predictions from relatedness, not a shared physiological cause. This study con rmed previous ndings that UAS HTP can facilitate understanding of genotypic response to disease in a manner complementary to traditional disease phenotyping methods 52,53 , as well as to replace subjective visual assessment of senescence with quantitative, phenotyping-based screens of large breeding populations 20 . Preliminary ndings in this study highlight the potential for grain lling period to serve as a predictor of yield.…”

Section: High Throughput Phenotyping: Implications For Plant Breeders and Geneticistssupporting

confidence: 86%

Phenomic Data-Facilitated Rust and Senescence Prediction in Maize Using Machine Learning Algorithms

DeSalvio

Adak

Murray

et al. 2021

Preprint

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Current methods in measuring maize (Zea mays L.) southern rust (Puccinia polyspora Underw.) and subsequent crop senescence require expert observation which are resource-intensive and prone to subjectivity. In this study, unoccupied aerial system (UAS) field-based high-throughput phenotyping (HTP) was employed to collect high-resolution aerial imagery of elite maize hybrids planted in the 2020 and 2021 growing seasons, with 13 UAS flights obtained from 2020 and 17 from 2021. Vegetation indices (VIs) were extracted from mosaicked aerial images that served as temporal phenomic predictors for southern rust scored in the field and senescence as scored using UAS-acquired mosaic images. Temporal best linear unbiased predictors (TBLUPs) were calculated using a nested model that treated the pedigree performances as nested within flights in terms of rust and senescence. All eight machine learning regressions tested (ridge, lasso, elastic net, random forest, support vector machine with radial and linear kernels, partial least squares, and k-nearest neighbors) outperformed a general linear model with both higher prediction accuracies (92-98%) and lower root mean squared error (RMSE) for rust and senescence scores. UAS-acquired VIs enabled the discovery of novel early quantitative phenotypic indicators of maize senescence and southern rust before being detectable by expert annotation and revealed positive correlations between grain filling time and yield (0.22 and 0.44 in 2020 and 2021), with practical implications for precision agricultural practices.

show abstract

Assessing winter wheat foliage disease severity using aerial imagery acquired from small Unmanned Aerial Vehicle (UAV)

Cited by 44 publications

References 21 publications

Phenomic data-facilitated rust and senescence prediction in maize using machine learning algorithms

Phenomic data-facilitated rust and senescence prediction in maize using machine learning algorithms

Early Detection of Wild Rocket Tracheofusariosis Using Hyperspectral Image-Based Machine Learning

Phenomic Data-Facilitated Rust and Senescence Prediction in Maize Using Machine Learning Algorithms

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