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Ascomycota

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Modeling risk of Sclerotinia sclerotiorum-induced disease development on canola and dry bean using machine learning algorithms.

Scientific reports
Diseases caused by the fungus Sclerotinia sclerotiorum are managed mainly through fungicide applications in canola and dry bean. Accurate estimation of the risk of disease development on these crops could help farmers make spraying decisions. Five ma...

Multi-batch single-cell comparative atlas construction by deep learning disentanglement.

Nature communications
Cell state atlases constructed through single-cell RNA-seq and ATAC-seq analysis are powerful tools for analyzing the effects of genetic and drug treatment-induced perturbations on complex cell systems. Comparative analysis of such atlases can yield ...

From Nontargeted to Targeted Analysis: Feature Selection in the Differentiation of Truffle Species ( spp.) Using H NMR Spectroscopy and Support Vector Machine.

Journal of agricultural and food chemistry
The price of different truffle types varies according to their culinary value, sometimes by more than a factor of 10. Nonprofessionals can hardly distinguish visually the species within the white or black truffles, making the possibility of food frau...

Field pea leaf disease classification using a deep learning approach.

PloS one
Field peas are grown by smallholder farmers in Ethiopia for food, fodder, income, and soil fertility. However, leaf diseases such as ascochyta blight, powdery mildew, and leaf spots affect the quantity and quality of this crop as well as crop growth....

Integrated Assays of Genome-Wide Association Study, Multi-Omics Co-Localization, and Machine Learning Associated Calcium Signaling Genes with Oilseed Rape Resistance to .

International journal of molecular sciences
(Ss) is one of the most devastating fungal pathogens, causing huge yield loss in multiple economically important crops including oilseed rape. Plant resistance to Ss pertains to quantitative disease resistance (QDR) controlled by multiple minor gene...

Deep Learning-Based Barley Disease Quantification for Sustainable Crop Production.

Phytopathology
Net blotch disease caused by is a major fungal disease that affects barley () plants and can result in significant crop losses. In this study, we developed a deep learning model to quantify net blotch disease symptoms on different days postinfection...

Machine learning-based identification of general transcriptional predictors for plant disease.

The New phytologist
This study investigated the generalizability of Arabidopsis thaliana immune responses across diverse pathogens, including Botrytis cinerea, Sclerotinia sclerotiorum, and Pseudomonas syringae, using a data-driven, machine learning approach. Machine le...

Multiple, Single Trait GWAS and Supervised Machine Learning Reveal the Genetic Architecture of Fraxinus excelsior Tolerance to Ash Dieback in Europe.

Plant, cell & environment
Common ash (Fraxinus excelsior) is under intensive attack from the invasive alien pathogenic fungus Hymenoscyphus fraxineus, causing ash dieback at epidemic levels throughout Europe. Previous studies have found significant genetic variation among gen...

Identification of potent phytochemicals against Magnaporthe oryzae through machine learning aided-virtual screening and molecular dynamics simulation approach.

Computers in biology and medicine
Magnaporthe oryzae stands as a notorious fungal pathogen responsible for causing devastating blast disease in cereals, leading to substantial reductions in grain production. Despite the usage of chemical fungicides to combat the pathogen, their effec...

Ultrafast on-site adulteration detection and quantification in Asian black truffle using smartphone-based computer vision.

Talanta
Asian black truffle Tuber sinense (BT) is a premium edible fungus with medicinal value, but it is often prone to adulteration. This study aims to develop a fast, non-destructive, automatic, and intelligent method for identifying BT. A novel lightweig...