Plant diseases are responsible for substantial crop losses each year and pose a threat to global food security and agricultural sustainability. Improving crop resistance to pathogens through breeding is an environmentally sound method for managing disease and minimizing these losses. However, it is challenging to breed varieties with resistance that is effective, stable and broad-spectrum. Recent advances in genetic and genomic technologies have contributed to a better understanding of the complexity of host-pathogen interactions and have identified some of the genes and mechanisms that underlie resistance. This new knowledge is benefiting crop improvement through better-informed breeding strategies that utilize diverse forms of resistance at different scales, from the genome of a single plant to the plant varieties deployed across a region.

Northern leaf blight (NLB) can cause severe yield loss in maize; however, scouting large areas to accurately diagnose the disease is time consuming and difficult. We demonstrate a system capable of automatically identifying NLB lesions in field-acquired images of maize plants with high reliability. This approach uses a computational pipeline of convolutional neural networks (CNNs) that addresses the challenges of limited data and the myriad irregularities that appear in images of field-grown plants. Several CNNs were trained to classify small regions of images as containing NLB lesions or not; their predictions were combined into separate heat maps, then fed into a final CNN trained to classify the entire image as containing diseased plants or not. The system achieved 96.7% accuracy on test set images not used in training. We suggest that such systems mounted on aerial- or ground-based vehicles can help in automated high-throughput plant phenotyping, precision breeding for disease resistance, and reduced pesticide use through targeted application across a variety of plant and disease categories.

Many plants, both in nature and in agriculture, are resistant to multiple diseases. Although much of the plant innate immunity system provides highly specific resistance, there is emerging evidence to support the hypothesis that some components of plant defense are relatively nonspecific, providing multiple disease resistance (MDR). Understanding MDR is of fundamental and practical interest to plant biologists, pathologists, and breeders. This review takes stock of the available evidence related to the MDR hypothesis. Questions about MDR are considered primarily through the lens of forward genetics, starting at the organismal level and proceeding to the locus level and, finally, to the gene level. At the organismal level, MDR may be controlled by clusters of R genes that evolve under diversifying selection, by dispersed, pathogen-specific genes, and/or by individual genes providing MDR. Based on the few MDR loci that are well-understood, MDR is conditioned by diverse mechanisms at the locus and gene levels.

ObjectivesAutomated detection and quantification of plant diseases would enable more rapid gains in plant breeding and faster scouting of farmers’ fields. However, it is difficult for a simple algorithm to distinguish between the target disease and other sources of dead plant tissue in a typical field, especially given the many variations in lighting and orientation. Training a machine learning algorithm to accurately detect a given disease from images taken in the field requires a massive amount of human-generated training data.Data descriptionThis data set contains images of maize (Zea mays L.) leaves taken in three ways: by a hand-held camera, with a camera mounted on a boom, and with a camera mounted on a small unmanned aircraft system (sUAS, commonly known as a drone). Lesions of northern leaf blight (NLB), a common foliar disease of maize, were annotated in each image by one of two human experts. The three data sets together contain 18,222 images annotated with 105,705 NLB lesions, making this the largest publicly available image set annotated for a single plant disease.