Effect of Initial Inoculum on the Temporal and Spatial Dynamics of Wheat Blast Under Field Conditions in Bolivia.

Epidemiological studies to better understand wheat blast (WB) spatial and temporal patterns were conducted in three field environments in Bolivia between 2019 and 2020. The temporal dynamics of wheat leaf blast (WB) and spike blast (WB) were best described by the logistic model compared with the Gompertz and exponential models. The nonlinear logistic infection rates were higher under defined inoculation in experiments two and three than under undefined inoculation in experiment one, and they were also higher for WB than for WB.

Monitoring tar spot disease in corn at different canopy and temporal levels using aerial multispectral imaging and machine learning.

Introduction: Tar spot is a high-profile disease, causing various degrees of yield losses on corn ( L.) in several countries throughout the Americas. Disease symptoms usually appear at the lower canopy in corn fields with a history of tar spot infection, making it difficult to monitor the disease with unmanned aircraft systems (UAS) because of occlusion.

Contour-Based Detection and Quantification of Tar Spot Stromata Using Red-Green-Blue (RGB) Imagery.

Quantifying symptoms of tar spot of corn has been conducted through visual-based estimations of the proportion of leaf area covered by the pathogenic structures generated by (stromata). However, this traditional approach is costly in terms of time and labor, as well as prone to human subjectivity. An objective and accurate method, which is also time and labor-efficient, is of an urgent need for tar spot surveillance and high-throughput disease phenotyping.

Wheat Spike Blast Image Classification Using Deep Convolutional Neural Networks.

Wheat blast is a threat to global wheat production, and limited blast-resistant cultivars are available. The current estimations of wheat spike blast severity rely on human assessments, but this technique could have limitations. Reliable visual disease estimations paired with Red Green Blue (RGB) images of wheat spike blast can be used to train deep convolutional neural networks (CNN) for disease severity (DS) classification.