Diseases have adverse effects on crop production and yield loss. Various diseases such as leaf rust, stem rust, and strip rust can affect yield quality and quantity for a studied area. In addition, manual wheat disease identification and interpretation is time-consuming and cumbersome. Currently, decisions related to plants mainly rely on the level of expertise in the domain. To resolve these challenges and to identify wheat disease as early as possible, we implemented different deep learning models such as Inceptionv3, Resnet50, and VGG16/19. This research was conducted in collaboration with Bishoftu Agricultural Research Institute, Ethiopia. Our main objective was to automate plant-disease identification using advanced deep learning approaches and image data. For the experiment, RGB image data were collected from the Bishoftu area. From the experimental results, the VGG19 model classified wheat disease with 99.38% accuracy.

As Ethiopia pushes toward self-sufficiency in wheat production, it has escaped large outbreaks of Fusarium head blight (FHB), a disease that threatens wheat production globally. However, in 2022, FHB incidences in Ethiopia rose to 80%, with some areas experiencing 100% disease severity. Here we provide insights into the etiology of this disease outbreak and point toward future directions to mitigate the emerging threat of FHB on a global scale. Although most wheat samples from 2022 exhibited low trichothecene levels, 26% exceeded recommended thresholds and several contained multiple trichothecene variants. We obtained 64 isolates from the outbreak and identified diverse members of the Fusarium graminearum species complex (FGSC) and many Epicoccum species. The FGSC species contributing to the outbreak are rare on a global scale. Genomic analyses reveal that Fusarium aethiopicum has persisted in Ethiopia for decades and shares ancient ancestry with a newly emerged novel species in the FGSC that we formally described as Fusarium kistleri. Single-nucleotide polymorphism–based analyses suggest high clonal fraction among FGSC isolates in Ethiopia, raising questions about a recent population expansion. Our findings reveal that although Epicoccum alone causes minimal disease on wheat, its presence can have a small but synergistic impact on disease symptoms when F. graminearum has already infected. The unique diversity and species composition of the 2022 Ethiopian outbreak underscores the importance of addressing emerging threats in a globalized agricultural economy to secure food safety, food security, and global food equity.

Tef (Eragrostis tef) is the most important cereals in Ethiopia, is threatened by abiotic and biotic constraints. Among the biotic, tef leaf rust (Uromyces eragrostis) is one of the major constraints of tef production. Information on the occurrence and reaction of cultivars to tef leaf rust in Ethiopia was not well known. Therefore, the aim of this survey was to observe the performance of commercial cultivars and understand the distribution of major tef diseases in the country. The survey was made in 2016 main cropping seasons (in October) following the main roads and accessible routes in each survey district, and stops were made at every 5-10km intervals based on vehicles odometers as per tef fields presented. Five stops were made in each tef field by moving “X” fashion at each stop interval and data were collected from each. A total of 19 districts were covered during the survey. In each surveyed areas the incidence and severity of tef diseases were recorded. A total of 41 fields were observed in all assessed regions and all the observed fields were infected by tef leaf rust disease. The prevalent of tef leaf rust disease was 100% in all regions.