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Westergaard, J. C.

Publications and source records attributed to Westergaard, J. C..

2 recordsLinked to original sources
bioRxiv2026

Comparative high-throughput phenotyping across two facilities reveals differential impact of defence mechanisms on plant growth and development.

Fitness costs of plant disease defence are often subtle and difficult to quantify. In this study, we therefore used comparative high-throughput phenotyping in two independent facilities to assess growth, morphology and physiology of potato (cv. Desiree) with high time-resolution monitoring different defence mechanisms under pathogen-free conditions. Plants were either treated weekly with the resistance inducers {beta}-aminobutyric acid (BABA; 10 mM) or potassium phosphite (KPhi; 36 mM) or comprised six transgenic lines expressing late blight resistance genes (single Rpi genes or a three-gene stack) or reduced jasmonate perception (StCOI1-RNAi). Over four weeks, image-derived traits revealed consistent cross-facility effects for plant height and colour: BABA treatment increased plant height but reduced canopy area and induced a paler greenness signature, whereas KPhi caused minimal and transient growth effects. Chlorophyll fluorescence at the NaPPI facility indicated reduced vitality (Rfd_Lss) in BABA-treated plants and increased Rfd_Lss following KPhi, while maximum PSII efficiency was largely unchanged. Several transgenic lines showed somewhat reduced above-ground biomass. Enzyme activity profiling produced distinct treatment and genotype signatures, but was strongly modulated by facility conditions that overrode these specificities. Overall, high-throughput phenotyping robustly detected subtle growth-defence trade-offs across platforms. HighlightHigh-throughput optical phenotyping validated across two independent research facilities reveals that stacked resistance genes and resistance inducers in potato trigger subtle growth trade-offs. Graphical abstracts O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/713143v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@89df47org.highwire.dtl.DTLVardef@1a1ce64org.highwire.dtl.DTLVardef@1f52f0dorg.highwire.dtl.DTLVardef@1e41c35_HPS_FORMAT_FIGEXP M_FIG C_FIG Experimental timeline for high-throughput plant phenotyping platforms. Created in BioRender. Poque, S. (2026) https://BioRender.com/nmkve7g

plant biology↗
bioRxiv2020

Automatic late blight lesion recognition and severity quantification based on field imagery of diverse potato genotypes by deep learning

The plant pathogen Phytophthora infestans causes the severe disease late blight in potato, which results in a huge loss for potato production. Automatic and accurate disease lesion segmentation enables fast evaluation of disease severity and assessment of disease progress for precision crop breeding. Deep learning has gained tremendous success in computer vision tasks for image classification, object detection and semantic segmentation. To test whether we could extract late blight lesions from unstructured field environments based on high-resolution visual field images and deep learning algorithms, we collected ~500 field RGB images in a set of diverse potato genotypes with different disease severity (0-70%), resulting in 2100 cropped images. 1600 of these cropped images were used as the dataset for training deep neural networks. Finally, the developed model was tested on the 250 cropped images. The results show that the intersection over union (IoU) values of background (leaf and soil) and disease lesion classes in the test dataset are 0.996 and 0.386, respectively. Furthermore, we established a linear relationship (R2 = 0.655) between manual visual scores of late blight and the number of lesions at the canopy level. We also learned that imbalance weights of lesion and background classes improved segmentation performance, and that fused masks based on the majority voting of the multiple masks enhanced the correlation with the visual scores. This study demonstrates the feasibility of using deep learning algorithms for disease lesion segmentation and severity evaluation based on proximal imagery for crop resistance breeding in field environments.

plant biology↗