Maize streak virus strain A (MSV-A), the causal agent of maize streak disease, is today one of the most serious biotic threats to African food security. Determining where MSV-A originated and how it spread transcontinentally could yield valuable insights into its historical emergence as a crop pathogen. Similarly, determining where the major extant MSV-A lineages arose could identify geographical hot spots of MSV evolution. Here, we use model-based phylogeographic analyses of 353 fully sequenced MSV-A isolates to reconstruct a plausible history of MSV-A movements over the past 150 years. We show that since the probable emergence of MSV-A in southern Africa around 1863, the virus spread transcontinentally at an average rate of 32.5 km/year (95% highest probability density interval, 15.6 to 51.6 km/year). Using distinctive patterns of nucleotide variation caused by 20 unique intra-MSV-A recombination events, we tentatively classified the MSV-A isolates into 24 easily discernible lineages. Despite many of these lineages displaying distinct geographical distributions, it is apparent that almost all have emerged within the past 4 decades from either southern or east-central Africa. Collectively, our results suggest that regular analysis of MSV-A genomes within these diversification hot spots could be used to monitor the emergence of future MSV-A lineages that could affect maize cultivation in Africa.

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The Food and Agriculture Organization of the United Nations suggests increasing the food supply by 70% to feed the world population by 2050, although approximately one third of all food is wasted because of plant diseases or disorders. To achieve this goal, researchers have proposed many deep learning models to help farmers detect diseases in their crops as efficiently as possible to avoid yield declines. These models are usually trained on personal or public plant disease datasets such as PlantVillage or PlantDoc. PlantVillage is composed of laboratory images captured under laboratory conditions, with one leaf each and a uniform background. The models trained on this dataset have very low accuracies when running on field images with complex backgrounds and multiple leaves per image. To solve this problem, PlantDoc was built using 2,569 field images downloaded from the Internet and annotated to identify the individual leaves. However, this dataset includes some laboratory images and the absence of plant pathologists during the annotation process may have resulted in misclassification. In this study, FieldPlant is suggested as a dataset that includes 5,170 plant disease images collected directly from plantations. Manual annotation of individual leaves on each image was performed under the supervision of plant pathologists to ensure process quality. This resulted in 8,629 individual annotated leaves across the 27 disease classes. We ran various benchmarks on this dataset to evaluate state-of-the-art classification and object detection models and found that classification tasks on FieldPlant outperformed those on PlantDoc.