One of the most important and widespread corn/maize virus diseases is maize dwarf mosaic (MDM), which can be induced by sugarcane mosaic virus (SCMV). This study explores a machine learning analysis of five-band multispectral imagery collected via an unmanned aerial system (UAS) during the 2021 and 2022 seasons for SCMV disease detection in corn fields. The three primary objectives are to (i) determine the spectral bands and vegetation indices that are most important or correlated with SCMV infection in corn, (ii) compare spectral signatures of mock-inoculated and SCMV-inoculated plants, and (iii) compare the performance of four machine learning algorithms, including ridge regression, support vector machine (SVM), random forest, and XGBoost, in predicting SCMV during early and late stages in corn. On average, SCMV-inoculated plants had higher reflectance values for blue, green, red, and red-edge bands and lower reflectance for near-infrared as compared to mock-inoculated samples. Across both years, the XGBoost regression model performed best for predicting disease incidence percentage (R² = 0.29, RMSE = 29.26), and SVM classification performed best for the binary prediction of SCMV-inoculated vs. mock-inoculated samples (72.9% accuracy). Generally, model performances appeared to increase as the season progressed into August and September. According to Shapley additive explanations (SHAP analysis) of the top performing models, the simplified canopy chlorophyll content index (SCCCI) and saturation index (SI) were the vegetation indices that consistently had the strongest impacts on model behavior for SCMV disease regression and classification prediction. The findings of this study demonstrate the potential for the development of UAS image-based tools for farmers, aiming to facilitate the precise identification and mapping of SCMV infection in corn.

Animal manure is an agricultural by-product that presents both nutrient value and environmental challenges, particularly through nutrient leaching and greenhouse gas (GHG) fluxes. Hydrothermal carbonization (HTC) offers a potential management strategy that converts wet manures into hydrochar, which is a more stable and nutrient-retentive amendment. This study evaluated how hydrochar derived from dairy manure, swine manure, and poultry litter, applied at 15 or 30 metric tons/ha, influences GHG fluxes and corn biomass production in a controlled greenhouse experiment using soil mesocosms with two fertilization histories (dairy-manure vs. inorganic/chemical-only). All treatments received an equal total nitrogen input through combinations of hydrochar-derived N and urea. Hydrochar treatments tended to reduce cumulative N₂O fluxes, although not always significantly, compared to the urea-only control, with the largest reductions occurring after the second urea application. Across feedstocks, hydrochar treatments tended to show higher cumulative CO₂ fluxes, although not statistically significant, relative to the control, with 15 tons/ha applications producing higher CO₂ fluxes and more corn biomass compared to 30 tons/ha. Methane fluxes exhibited no consistent treatment-level trends. Corn biomass tended to be higher in the 15 tons/ha hydrochar treatments and lower in the 30 tons/ha treatments relative to the control. Overall, findings indicate that partially replacing urea fertilizer with hydrochar, can potentially increase biomass while reducing cumulative N₂O fluxes. Generally, the increases in CO₂ fluxes from hydrochar did not fully offset the reductions in N₂O fluxes at the system level. The results suggest that hydrochar amendments can offer an agronomically efficient and environmentally beneficial nutrient-management strategy.

Soybean cyst nematode (SCN) is the most economically devastating pathogen of soybean in North America. Even at low to moderate infestation levels, SCN can cause 20–30% yield loss without producing any visible aboveground symptoms. In severely infested fields, yield reductions can reach 60–70% and, in extreme cases, exceed 80%. Prior research on identifying SCN infestations has primarily relied on traditional machine-learning methods applied to Unmanned Aerial System (UAS)-based multispectral imagery, with limited success. This study hypothesizes that deep-learning (DL) methods can more effectively capture the subtle spectral and spatial signatures in multispectral images of SCN stress. To address this gap, we evaluate the performance of advanced DL architectures, including Vision Transformer (ViT) and a customized Convolutional Neural Network (CNN), for detecting SCN infestation in soybean fields using multispectral UAS imagery. Spectral analysis of the multispectral imagery revealed that the near-infrared (NIR) band is a strong discriminator between non-detected and SCN-infested areas. The DL models trained and tested across multiple growth stages showed promising results. The four-timestamp ViT model (3 June, 29 July, 19 August, and 2 September) achieved an F1-score of 0.74, while the five-timestamp SCN–CNN model (3 June, 22 July, 29 July, 19 August, and 2 September) achieved an F1-score of 0.75. Although overall performance was comparable, ViT demonstrated more stable performance across varying training and test data distributions. These findings highlight the effectiveness of DL architectures to automatically extract subtle, complex plant features from multispectral imagery throughout the growing season. Compared with manual, time-consuming soil-sampling techniques, the proposed framework enables more precise spatial and temporal monitoring of SCN infestations across fields.

This is the supplemental material document corresponding to the article entitled 'Development of a Subsurface Sensing Probe for Measurement of Nitrogen Oxide Gases'. The document primarily includes supplementary figures and plots that display gas concentration measurements for N2O and NO sensor devices during additional 14-day fertilizer trials. Also included are data plots regarding soil properties during all fertilizer trials.

This is the supplemental material document corresponding to the article entitled 'Development of a Subsurface Sensing Probe for Measurement of Nitrogen Oxide Gases'. The document primarily includes supplementary figures and plots that display gas concentration measurements for N2O and NO sensor devices during additional 14-day fertilizer trials. Also included are data plots regarding soil properties during all fertilizer trials.