Southern Blight (SB), caused by the fungus Athelia rolfsii, has increasingly impacted Mississippi soybean production with estimated economic losses in recent years rising from $181,616 in 2016 to $9,508,412 in 2021. Currently, there are no recommended fungicides and no known commercially available cultivar resistance. Eleven cultivars were evaluated for their response to the presence of Athelia rolfsii in the 2021 field trial. An in vitro fungicide assay was conducted to evaluate the efficacy of five fungicides to determine the respective EC50. These fungicides were then evaluated in combination with three cultivars, one mild, one moderate, and one severe in the 2022 field trial and in the growth chamber trial. A rapid and quantitative method was developed to evaluate the response of these commercially available soybean cultivars in combination with fungicides to SB. Determining effective management options has the potential to decrease disease losses for Mississippi Soybean growers.

Peanuts are an important legume crop cultivated in Mississippi with an annual production of 71 million kg per hectare. Southern blight (SB), caused by Athelia rolfsii, is a devastating disease resulting in significant losses to peanut production. Cultural practices may not adequately control diseases; therefore, the application of fungicides is important. Recently, the rapid growth of weed species has emphasized herbicide usage and studied weed interaction with SB. In this study, Athelia rolfsii isolates were collected from different counties to identify their biological characteristics and determine the in vitro efficacy in both 2022 and 2023. In the field trial, seven fungicides were applied in-furrow in both 2022 and 2023. Additionally, eight herbicides were applied at pre-emergence, post-emergence, and mid-post emergence along with the application of fungicides, Revytek, and Omega 500. The introduction of appropriate management practices has led to significant SB losses and an increased pod yield.

Stem rot of peanut (Arachis hypogaea L.), caused by a soilborne fungus Athelia rolfsii (Curzi) C. C. Tu and Kimbr. (anamorph: Sclerotium rolfsii Sacc.), is one of the most important diseases in peanut production worldwide. Though new varieties with increased partial resistance to this disease have been developed, there is still a need to utilize fungicides for disease control during the growing season. Fungicides with activity against A. rolfsii are available, and several new products have been recently registered for control of stem rot in peanut. However, fungicides are most effective when applied before or during the early stages of infection. Current scouting methods can detect disease once signs or symptoms are present, but to optimize the timing of fungicide applications and protect crop yield, a method for early detection of soilborne diseases is needed. Previous studies have utilized optical sensors combined with machine learning analysis for the early detection of plant diseases, but these studies mainly focused on foliar diseases. Few studies have applied these technologies for the early detection of soilborne diseases in field crops, including peanut. Thus, the overall goal of this research was to integrate sensor technologies, modern data analytic tools, and properties of standard and newly registered fungicides to develop improved management strategies for stem rot control in peanuts. The specific objectives of this work were to 1) characterize the spectral and thermal responses of peanut to infection with A. rolfsii under controlled conditions, 2) identify optimal wavelengths to detect stem rot of peanut using hyperspectral sensor and machine learning, and 3) evaluate the standard and newly registered peanut fungicides with different modes of action for stem rot control in peanuts using a laboratory bioassay. For Objective 1, spectral reflectance and leaf temperature of peanut plants were measured by spectral and thermal sensors in controlled greenhouse experiments. Differences in sensor-based responses between A. rolfsii-infected and non-infected plants were detected 0 to 1 day after observation of foliar disease symptoms. In addition, spectral responses of peanut to the infection of A. rolfsii were more pronounced and consistent than thermal changes as the disease progressed. Objective 2 aimed to identify specific signatures of stem rot from reflectance data collected in Objective 1 utilizing a machine learning approach. Wavelengths around 505, 690, and 884 nm were repeatedly selected by different methods. The top 10 wavelengths identified by the recursive feature selection methods performed as well as all bands for the classification of healthy peanut plants and plants at different stages of disease development. Whereas the first two objectives focused on disease detection, Objective 3 focused on disease control and compared the properties of different fungicides that are labeled for stem rot control in peanut using a laboratory bioassay of detached peanut tissues. All of the foliar-applied fungicides evaluated provided inhibition of A. rolfsii for up to two weeks on plant tissues that received a direct application. Succinate dehydrogenase inhibitors provided less basipetal protection of stem tissues than quinone outside inhibitor or demethylation inhibitor fungicides. Overall, results of this research provide a foundation for developing sensor/drone-based methods that use disease-specific spectral indices for scouting in the field and for making fungicide application recommendations to manage stem rot of peanut and other soilborne diseases. / Doctor of Philosophy / Plant diseases are a major constraint to crop production worldwide. Developing effective and economical management strategies for these diseases, including selection of proper fungicide chemistries and making timely fungicide application, is dependent on the ability to accurately detect and diagnose their signs and/or symptoms prior to widespread development in a crop. Optical sensors combined with machine learning analysis are promising tools for automated crop disease detection, but research is still needed to optimize and validate methods for the detection of specific plant diseases. The overarching goal of this research was to use the peanut-stem rot plant disease system to identify and evaluate sensor-based technologies and different fungicide chemistries that can be utilized for the management of soilborne plant diseases. The specific objectives of this work were to 1) characterize the temporal progress of spectral and thermal responses of peanut to infection and colonization with Athelia rolfsii, the causal agent of peanut stem rot 2) identify optimal wavelengths to detect stem rot of peanut using hyperspectral sensor and machine learning, and 3) evaluate standard and newly registered peanut fungicides with different modes of action for stem rot control in peanuts using a laboratory bioassay. Results of this work demonstrate that spectral reflectance measurements are able to distinguish between diseased and healthy plants more consistently than thermal measurements. Several wavelengths were identified using machine learning approaches that can accurately differentiate between peanut plants with symptoms of stem rot and non-symptomatic plants. In addition, a new method was developed to select the top-ranked, non-redundant wavelengths with a custom distance. These selected wavelengths performed better than using all wavelengths, providing a basis for designing low-cost optical filters to specifically detect this disease. In the laboratory bioassay evaluation of fungicides, all of the foliar-applied fungicides provided inhibition of A. rolfsii for up to two weeks on leaf tissues that received a direct application. Percent inhibition of A. rolfsii decreased over time, and the activity of all fungicides decreased at a similar rate. Overall, the findings of this research provide a foundation for developing sensor-based methods for disease scouting and making fungicide application recommendations to manage stem rot of peanut and other soilborne diseases.