Plant Disease Sensing: Studying Plant-Pathogen Interactions at Scale

Gold, Kaitlin M.

doi:10.1128/msystems.01228-21

Cited by 16 publications

(8 citation statements)

References 69 publications

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“…Potato plants live in highly inconsistent environments, and continuous exposure to minor stress levels interrupts plant homeostasis and results in constant energy loss due to resource diversion toward the activation of defense mechanisms (Fahad et al, 2017; Gold, 2021; Hipsch et al, 2021a; Zhu, 2016). Various technologies have been developed for the early detection of plant pathogens, such as nucleic acid-based methods and multispectral imaging (Baldeck & Asner, 2014; Gold et al, 2020; Khan et al, 2017; Lees et al, 2012; Zhao et al, 2016).…”

Section: Resultsmentioning

confidence: 99%

Early detection of late blight in potato by whole-plant redox imaging

Hipsch

Yaron

Lampl

et al. 2022

Preprint

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Late blight caused by the oomycete Phytophthora infestans is a most devastating disease of potatoes (Solanum tuberosum). Its early detection is crucial for suppressing disease spread. Necrotic lesions are normally seen in leaves at 4 dpi (days post inoculation) when colonized cells are dead, but early detection of the initial biotrophic growth stage, when the pathogen feeds on living cells, is challenging. Here, the biotrophic growth phase of P. infestans was detected by whole-plant redox imaging of potato plants expressing chloroplast-targeted reduction-oxidation sensitive green fluorescent protein (chl-roGFP2). Clear spots on potato leaves with a lower chl-roGFP2 oxidation state were detected as early as 2 dpi, before any visual symptoms were recorded. These spots were particularly evident during light-to-dark transitions and reflected mislocalization of chl-roGFP2 outside the chloroplasts, demonstrating perturbation of the chloroplast import system by the pathogen. Image analysis based on machine learning enabled systematic identification and quantification of spots and unbiased classification of infected and uninfected leaves in inoculated plants. Comparing redox to chlorophyll fluorescence imaging showed that infected leaf areas which exhibit mislocalized chl-roGFP2 also showed reduced non-photochemical quenching (NPQ) and enhanced quantum PSII yield (ΦPSII) compared to the surrounding leaf areas. The data suggest that mislocalization of chloroplast-targeted proteins is an efficient marker of late blight infection and demonstrate how it can be utilized for nondestructive monitoring of the disease biotrophic stage using whole-plant redox imaging.

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Section: Resultsmentioning

confidence: 99%

Early detection of late blight in potato by whole-plant redox imaging

Hipsch

Yaron

Lampl

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Potato plants live in highly inconsistent environments, and continuous exposure to minor stress levels interrupts plant homeostasis and results in constant energy loss due to resource diversion toward the activation of defense mechanisms (Fahad et al., 2017; Gold, 2021; Hipsch et al., 2021; Zhu, 2016). Various technologies have been developed for the early detection of plant pathogens, such as nucleic acid‐based methods and multispectral imaging (Baldeck & Asner, 2014; Gold, 2021; Khan et al., 2017; Lees et al., 2012; Zhao et al., 2016). This work is a critical step toward developing new quantitative tools for the detection of late blight disease in crop plants, and controlling and limiting disease spread by utilizing genetically encoded biosensors.…”

Section: Discussionmentioning

confidence: 99%

Early detection of late blight in potato by whole‐plant redox imaging

et al. 2023

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Late blight caused by the oomycete Phytophthora infestans is a most devastating disease of potatoes (Solanum tuberosum). Its early detection is crucial for suppressing disease spread. Necrotic lesions are normally seen in leaves at 4 days post-inoculation (dpi) when colonized cells are dead, but early detection of the initial biotrophic growth stage, when the pathogen feeds on living cells, is challenging. Here, the biotrophic growth phase of P. infestans was detected by whole-plant redox imaging of potato plants expressing chloroplasttargeted reduction-oxidation sensitive green fluorescent protein (chl-roGFP2). Clear spots on potato leaves with a lower chl-roGFP2 oxidation state were detected as early as 2 dpi, before any visual symptoms were recorded. These spots were particularly evident during light-to-dark transitions, and reflected the mislocalization of chl-roGFP2 outside the chloroplasts. Image analysis based on machine learning enabled systematic identification and quantification of spots, and unbiased classification of infected and uninfected leaves in inoculated plants. Comparing redox with chlorophyll fluorescence imaging showed that infected leaf areas that exhibit mislocalized chl-roGFP2 also showed reduced non-photochemical quenching and enhanced quantum PSII yield (ΦPSII) compared with the surrounding leaf areas. The data suggest that mislocalization of chloroplast-targeted proteins is an efficient marker of late blight infection, and demonstrate how it can be utilized for non-destructive monitoring of the disease biotrophic stage using whole-plant redox imaging.

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“…These approaches could also detect other in-field anomalies, such as nutrient deficiencies or weeds. However, few studies have used AI-EO to detect anomalies over large areas, partly due to limited access to high-resolution satellite datasets and ground-truth labels needed to train and evaluate AI models [17]. Current AI techniques for crop disease surveillance mostly focus on in-situ plant disease diagnosis using cell phone images or ground-based robots [18].…”

Section: Pest Disease and Anomaly Detectionmentioning

confidence: 99%

Considerations for AI-EO for agriculture in Sub-Saharan Africa

Nakalembe

Kerner

2023

Environ. Res. Lett.

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With the ever-growing urgency of food insecurity and the threat of climate change, there is increasing interest in using artificial intelligence for Earth observations (AI-EO) for agriculture, particularly in Sub-Saharan Africa (SSA). This paper provides an overview of the primary research areas within AI-EO for agriculture in SSA. %(cropland and crop type classification, field boundary delineation, yield estimation, and pest/disease monitoring). We discuss examples and limitations of current research and opportunities for future work. In addition, we identify ten key considerations for future efforts involving AI-EO for agriculture in SSA.

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Plant Disease Sensing: Studying Plant-Pathogen Interactions at Scale

Abstract: Plant disease threatens the environmental and financial sustainability of crop production, causing $220 billion in annual losses. The dire threat disease poses to modern agriculture demands tools for better detection and monitoring to prevent crop loss and input waste.

Cited by 16 publications

References 69 publications

Early detection of late blight in potato by whole-plant redox imaging

Early detection of late blight in potato by whole-plant redox imaging

Early detection of late blight in potato by whole‐plant redox imaging

Considerations for AI-EO for agriculture in Sub-Saharan Africa

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