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Highlights Generated custom imagery dataset using UAS, UGV, and handheld sensors separately in diseased corn fields. Proposed data pipeline utilizing Google Sheets API to establish communication between each platform and enable access through a web application. Developed deep learning-based disease management system for above and below-the-canopy corn disease diagnosis. Trained and evaluated disease detection models from each platform separately to provide management recommendations. Early disease management following the onset of disease symptoms is crucial for controlling their spread. Heterogenous collaboration between unmanned aerial systems (UAS) and unmanned ground vehicles (UGV) for field scouting and disease diagnosis is a potential approach for developing automated disease management solutions. However, automation of crop-specific disease identification requires the use of above and below-canopy sensors and properly trained deep learning (DL) models. This research proposes to develop a novel disease management system for diagnosing corn diseases from above and below the canopy by collaboratively using edge devices mounted on UAS and UGV, respectively. Three separate datasets were acquired using UAS above the canopy, UGV below the canopy, and handheld imaging platforms within diseased corn fields. DL-based image classification models were first trained for identifying common corn diseases under field conditions, resulting in up to 95.04% testing accuracy using the DenseNet169 architecture. After creating bounding box annotations for disease images, You Only Look Once (YOLO)v7 DL-based object detection models were trained to identify diseases from each platform separately. Trained YOLOv7 models resulted in the highest mAP@IoU=0.5 of 37.6%, 46.4%, and 72.2% for locating and identifying diseases above the canopy using UAS, below the canopy using UGV, and handheld sensors, respectively. A client/server architecture was developed to establish communication between the UAS, UGV, and Google Spreadsheets via Wi-Fi communication protocol. The coordinates of diseased regions and distinct disease types were recorded using the client/server architecture on Google Spreadsheets. A web application utilized the data from the Google Spreadsheet to help users diagnose diseases in real time and provide recommendations for implementing appropriate disease management practices. This study reports findings of independently operated UAS and UGV that can potentially offer disease spread from below and over the corn canopy by combining the information. [ABSTRACT FROM AUTHOR]

The success of a breeding program depends on having adequate diversity in the germplasm. However, as advanced breeding stocks and materials are generated, one casualty is the diversity itself. As a result, breeding programs in many crop species have reached a point of diminishing returns and it is feared that unless new diversity is infused into the breeding germplasm, we face catastrophic reductions in crop productivity if the climate turns adverse. Although some scientists favor transgenic approaches, a 'back to nature' approach to genetic diversity may prove faster and more effective. Wild and exotic relatives of crop plants hold a wealth of alleles that, if we can find them, can help break yield barriers and enhance tolerance to stresses. Many approaches, based largely on quantitative trait loci genetics, have been proposed and used for this purpose, but most are either highly laborious or discover relevant variation inefficiently. Here, we propose a gene-centered approach, dubbed MAGIC (mutant-assisted gene identification and characterization), that uses Mendelian mutants or other genetic variants in a trait of interest as reporters to identify novel genes and variants for that trait. MAGIC is similar to enhancer--suppressor screens, but rather than relying on variation created in the laboratory, it reveals variation created and refined by nature over millions of years of evolution. This approach could be an effective tool for exploring novel variation and a valuable means to harness natural diversity and define genetic networks.

The maize lethal leaf spot 1 (lls1) locus which has been identified as a mutated part of the plant genome responsible for the formation of necrotic lesions in the leaves of corn plants was studied in an attempt to identify suppressors of its expression and function. The results of northern blot analysis as well as DNA sequencing revealed that the Lls1 gene encodes a novel protein that is highly conserved in plants. The protein was found to bear two binding motifs of aromatic ring-hydroxylating dioxygenases, indicating that it can degrade a phenolic mediator of cell death.

Maize leaves are produced from polarized cell divisions that result in clonal cell lineages arrayed along the long axis of the leaf. We utilized this stereotypical division pattern to identify a collection of mutants that form chloroplast pigmentation sectors that violate the clonal cell lineages. Here, we describe the camouflage1 (cf1) mutant, which develops nonclonal, yellow–green sectors in its leaves. We cloned the cf1 gene by transposon tagging and determined that it encodes porphobilinogen deaminase (PBGD), an enzyme that functions early in chlorophyll and heme biosynthesis. While PBGD has been characterized biochemically, no viable mutations in this gene have been reported in plants. To investigate the in vivo function of PBGD, we characterized the cf1 mutant. Histological analyses revealed that cf1 yellow sectors display the novel phenotype of bundle sheath cell-specific death. Light-shift experiments determined that constant light suppressed cf1 sector formation, a dark/light transition is required to induce yellow sectors, and that sectors form only during a limited time of leaf development. Biochemical experiments determined that cf1 mutant leaves have decreased PBGD activity and increased levels of the enzyme substrate in both green and yellow regions. Furthermore, the cf1 yellow regions displayed a reduction in catalase activity. A threshold model is hypothesized to explain the cf1 variegation and incorporates photosynthetic cell differentiation, reactive oxygen species scavenging, and PBGD function. [ABSTRACT FROM PUBLISHER]