Application of a U-Net Neural Network to the Puccinia sorghi-Maize Pathosystem.

Computer vision approaches to analyze plant disease data can be both faster and more reliable than traditional, manual methods. However, the requirement of manually annotating training data for the majority of machine learning applications can present a challenge for pipeline development. Here, we describe a machine learning approach to quantify Puccinia sorghi incidence on maize leaves utilizing U-Net convolutional neural network models. We analyzed several U-Net models with increasing amounts of training image data, either randomly chosen from a large data pool or randomly chosen from a subset of disease time course data. As the training dataset size increases, the models perform better, but the rate of performance decreases. Additionally, the use of a diverse training dataset can improve model performance and reduce the amount of annotated training data required for satisfactory performance. Models with as few as 48 whole-leaf training images are able to replicate the ground truth results within our testing dataset. The final model utilizing our entire training dataset performs similarly to our ground truth data, with an intersection over union value of 0.5002 and an F1 score of 0.6669. This work illustrates the capacity of U-Nets to accurately answer real-world plant pathology questions related to quantification and estimation of plant disease symptoms. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Direct Agroinoculation of Maize Seedlings by Injection with Recombinant Foxtail Mosaic Virus and Sugarcane Mosaic Virus Infectious Clones.

Agrobacterium-based inoculation approaches are widely used for introducing viral vectors into plant tissues. This study details a protocol for the injection of maize seedlings near meristematic tissue with Agrobacterium carrying a viral vector. Recombinant foxtail mosaic virus (FoMV) clones engineered for gene silencing and gene expression were used to optimize this method, and its use was expanded to include a recombinant sugarcane mosaic virus (SCMV) engineered for gene expression. Gene fragments or coding sequences of interest are inserted into a modified, infectious viral genome that has been cloned into the binary T-DNA plasmid vector pCAMBIA1380. The resulting plasmid constructs are transformed into Agrobacterium tumefaciens strain GV3101. Maize seedlings as young as 4 days old can be injected near the coleoptilar node with bacteria resuspended in MgSO4 solution. During infection with Agrobacterium, the T-DNA carrying the viral genome is transferred to maize cells, allowing for the transcription of the viral RNA genome. As the recombinant virus replicates and systemically spreads throughout the plant, viral symptoms and phenotypic changes resulting from the silencing of the target genes lesion mimic 22 (les22) or phytoene desaturase (pds) can be observed on the leaves, or expression of green fluorescent protein (GFP) can be detected upon illumination with UV light or fluorescence microscopy. To detect the virus and assess the integrity of the insert simultaneously, RNA is extracted from the leaves of the injected plant and RT-PCR is conducted using primers flanking the multiple cloning site (MCS) carrying the inserted sequence. This protocol has been used effectively in several maize genotypes and can readily be expanded to other viral vectors, thereby offering an accessible tool for viral vector introduction in maize.