Understanding the physiological and molecular aspects of charcoal rot resistance mechanisms in sorghum and soybean

Doctor of PhilosophyDepartment of Plant PathologyChristopher R LittleCharcoal rot (CR) of soybean (Glycine max (L.) Merr.) and sorghum (Sorghum bicolor (L.) Moench) is caused by the hemibiotrophic soilborne fungus Macrophomina phaseolina (MP) and is an important pathogen in the midwestern United States. Complex molecular mechanisms underlie the interaction of MP with these two hosts, which impedes resistance breeding. To select for charcoal rot resistance, a thorough understanding of the host’s physiological and molecular responses to MP along with screening of genotypes with resistance to CR is essential. To understand MP induced host’s physiological and molecular responses, first we investigated MP-induced oxidative stress-mediated senescence by using the reactive oxygen species (ROS) scavenger ascorbic acid in soybean seedlings. Three soybean isolates of MP were tested for their sensitivity to ascorbic acid using an in-vitro assay. An in-planta soybean cut-stem assay was used for the exogenous application of ascorbic acid (oxidized and reduced form) following inoculation with MP. A ROS (H2O2) quantification assay was used to validate H2O2 induced by MP and ascorbic acid pre-treatment. All three MP isolates were sensitive to ascorbic acid concentrations of ≥ 15 mM. Ascorbic acid (10mM) pre-treatment following MP inoculation reduced CR lesion length compared to inoculated treatment. MP induced a significantly higher H2O2 than ascorbic acid pre-treated inoculated plant. Second, through comparative transcriptomics, MP-resistant and susceptible soybean genotypes revealed contrasted responses to MP-induced senescence. Gene Ontology and pathway analysis showed MP-induced receptor kinase like genes in both genotypes while down-regulated defense related antioxidant, hormonal, and other metabolic pathways in both genotypes. Ascorbic acid pre-treatment induced a more significant number of photosynthesis genes in both genotypes. Hydrogen peroxide pre-treatment following inoculation showed up-regulation of oxidative stress responsive pathways while down-regulated photosynthesis and hormonal signal transduction pathways. Third, the NAM phenotyping for CR resistance results of location- and year-wise data showed strong genotype by environment interactions. Overall, using MP screening, charcoal rot resistance phenotyping in the NAM parental lines revealed the genotype SC1103 as the most resistant line and Segaolane and Macia as the most susceptible. The SC1103 NAM family-derived population can be used for charcoal rot resistance in association studies to map charcoal rot resistance

The Utility of the Upcoming HyspIRI’s Simulated Spectral Settings in Detecting Maize Gray Leafy Spot in Relation to Sentinel-2 MSI, VENµS, and Landsat 8 OLI Sensors

Abstract: Considering the high maize yield loses caused by incidences of disease, as well as incomprehensive monitoring initiatives in crop farming, there is a need for spatially explicit, cost-effective, and consistent approaches for monitoring, as well as for forecasting, food-crop diseases, such as maize Gray Leaf Spot. Such approaches are valuable in reducing the associated economic losses while fostering food security. In this study, we sought to investigate the utility of the forthcoming HyspIRI sensor in detecting disease progression of Maize Gray Leaf Spot infestation in relation to the Sentinel-2 MSI and Landsat 8 OLI spectral configurations simulated using proximally sensed data. Healthy, intermediate, and severe categories of maize crop infections by the Gray Leaf Spot disease were discriminated based on partial least squares–discriminant analysis (PLS-DA) algorithm. Comparatively, the results show that the HyspIRI’s simulated spectral settings slightly performed better than those of Sentinel-2 MSI, VENµS, and Landsat 8 OLI sensor. HyspIRI exhibited an overall accuracy of 0.98 compared to 0.95, 0.93, and 0.89, which were exhibited by Sentinel-2 MSI, VENµS, and Landsat 8 OLI sensor sensors, respectively. Furthermore, the results showed that the visible section, red-edge, and NIR covered by all the four sensors were the most influential spectral regions for discriminating different Maize Gray Leaf Spot infections. These findings underscore the potential value of the upcoming hyperspectral HyspIRI sensor in precision agriculture and forecasting of crop-disease epidemics, which are necessary to ensure food securit

Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review

Disease diagnosis is one of the major tasks for increasing food production in agriculture. Although precision agriculture (PA) takes less time and provides a more precise application of agricultural activities, the detection of disease using an Unmanned Aerial System (UAS) is a challenging task. Several Unmanned Aerial Vehicles (UAVs) and sensors have been used for this purpose. The UAVs’ platforms and their peripherals have their own limitations in accurately diagnosing plant diseases. Several types of image processing software are available for vignetting and orthorectification. The training and validation of datasets are important characteristics of data analysis. Currently, different algorithms and architectures of machine learning models are used to classify and detect plant diseases. These models help in image segmentation and feature extractions to interpret results. Researchers also use the values of vegetative indices, such as Normalized Difference Vegetative Index (NDVI), Crop Water Stress Index (CWSI), etc., acquired from different multispectral and hyperspectral sensors to fit into the statistical models to deliver results. There are still various drifts in the automatic detection of plant diseases as imaging sensors are limited by their own spectral bandwidth, resolution, background noise of the image, etc. The future of crop health monitoring using UAVs should include a gimble consisting of multiple sensors, large datasets for training and validation, the development of site-specific irradiance systems, and so on. This review briefly highlights the advantages of automatic detection of plant diseases to the growers

Legume Crops

In a sustainable agricultural system, legume crops are one of the essential components. However, improving the productivity of legume crops and improving their tolerance to adverse environments are essential tasks for plant biologists. This book includes nine comprehensive chapters addressing various aspects of legume crop biology, production and importance. There are several chapters on the adaptation of legumes to an adverse environment. Particular focus is provided on the sustainable production of legume crops under changing environments. This book will be useful for undergraduate and graduate students, teachers, and researchers, particularly from the field of Crop Science, Soil Science, Plant Breeding and Agronomy