Remote sensing (RS) technologies provide a diagnostic tool that can serve as an early warning system, allowing the agricultural community to intervene early on to counter potential problems before they spread widely and negatively impact crop productivity. With the recent advancements in sensor technologies, data management and data analytics, currently, several RS options are available to the agricultural community. However, the agricultural sector is yet to implement RS technologies fully due to knowledge gaps on their sufficiency, appropriateness and techno-economic feasibilities. This study reviewed the literature between 2000 to 2019 that focused on the application of RS technologies in production agriculture, ranging from field preparation, planting, and in-season applications to harvesting, with the objective of contributing to the scientific understanding on the potential for RS technologies to support decision-making within different production stages. We found an increasing trend in the use of RS technologies in agricultural production over the past 20 years, with a sharp increase in applications of unmanned aerial systems (UASs) after 2015. The largest number of scientific papers related to UASs originated from Europe (34%), followed by the United States (20%) and China (11%). Most of the prior RS studies have focused on soil moisture and in-season crop health monitoring, and less in areas such as soil compaction, subsurface drainage, and crop grain quality monitoring. In summary, the literature highlighted that RS technologies can be used to support site-specific management decisions at various stages of crop production, helping to optimize crop production while addressing environmental quality, profitability, and sustainability.

Cover cropping is a conservation practice that helps to alleviate soil health problems and reduce nutrient losses. Understanding the spatial variability in historic and current adoption of cover cropping practices and their impacts on soil, water, and nutrient dynamics at a landscape scale is an important step in determining and prioritizing areas in a watershed to effectively utilize this practice. However, such data are lacking. Our objective was to develop a spatial and temporal inventory of winter cover cropping practices in the Maumee River watershed using images collected by Landsat satellites (Landsat 5, 7 and 8) from 2008 to 2019 in Google Earth Engine (GEE) platform. Each year, satellite images collected during cover crop growing season (i.e., between October and April) were converted into two seasonal composites based on cover crop phenology. Using these composites, various image-based covariates were extracted for 628 ground-truth (field) data. By integrating ground-truth and image-based covariates, a cover crop classification model based on a random forest (RF) algorithm was developed, trained and validated in GEE platform. Our classification scheme differentiated four cover crop categories: Winter Hardy, Winter Kill, Spring Emergent, and No Cover. The overall classification accuracy was 75%, with a kappa coefficient of 0.63. The results showed that more than 50% of the corn-soybean areas in the Maumee River watershed were without winter crops during 2008–2019 period. It was found that 2019/2020 and 2009/2010 were the years with the largest and lowest cover crop areas, with 34% and 10% in the watershed, respectively. The total cover cropping area was then assessed in relation to fall precipitation and cumulative growing degree days (GDD). There was no apparent increasing trend in cover crop areas between 2008 and 2019, but the variability in cover crops areas was found to be related to higher accumulated GDD and fall precipitation. A detailed understanding of the spatial and temporal distribution of cover crops using GEE could help in promoting site-specific management practices to enhance their environmental benefits. This also has significance to policy makers and funding agencies as they could use the information to localize areas in need of interventions for supporting adoption of cover cropping practice.

This study assesses the potential of using multispectral images collected by an unmanned aerial system (UAS) on machine learning (ML) frameworks to estimate cereal rye (Secale cereal L.) biomass. Multispectral images and ground-truth cereal rye biomass data were collected from 15 farmers’ fields up to three times between March and May in northwest Ohio. Images were processed to derive 13 vegetation indices (VIs). Out of 13 VIs, six optimal sets of VIs, including excess green (ExG), normalized green red difference index (NGRDI), soil adjusted vegetation index (SAVI), blue green ratio (B_G_ratio), red-edge triangular vegetation index (RTVI), and normalized difference red-edge (NDRE) were selected using the variance inflation factor (VIF) based feature selection approach. Six regression models including a multiple linear regression (MLR), elastic net (ENET), multivariate adaptive regression splines (MARS), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGB) were investigated for estimation of cereal rye biomass based on the VIs. For most of the models, the six selected VIs performed better than or similar to the full set of 13 VIs with R² ranging from 0.24 to 0.59 and RMSE ranging from 83.13 to 91.89 g/m² during 10-fold cross-validation. During independent accuracy assessment with the selected set of VIs, XGB exhibited the highest R² (0.67) and lowest RMSE (83.13 g/m²) and MAE (48.13 g/m²) followed by RF and ENET. For all the models, the agreement between observed and predicted biomass was high for biomass less than or equal to 200 g/m² but decreased for biomass greater than 200 g/m². When field-collected structural features were integrated with the selected VIs, the models showed improved performance, with R² and RMSE of the models reaching up to 0.82 and 61.67 g/m² respectively. Among the six VIs, SAVI showed the strongest impact on the model prediction for the best-performing RF and XGB regression models. The findings of this study demonstrate the potential of precisely estimating and mapping cereal rye biomass based on UAS-captured multispectral images. Timely information on cover crop growth can facilitate numerous decision-making processes, including planning the planting operations, and management of nutrients, weeds, and soil moisture to improve agronomic and environmental outcomes.

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.

Accurate estimation of winter cover crop biomass at a landscape scale is key to assessing benefits and promoting widespread adoption. Satellite imagery offers broad coverage but is limited by coarse resolution and spatial mismatch with field measurements. This study introduces a hybrid framework to improve satellite-based estimation of cereal rye cover crop biomass by integrating UAS-derived data. Extreme gradient boosting (XGBoost) and random forest (RF) machine learning models were trained across three scenarios: (1) UAS-based models, using field-measured biomass alongside UAS-derived vegetation indices (VIs) and crop height; (2) satellite-based models, using field-measured biomass and Sentinel-2 satellite-derived VIs and grey level co-occurrence texture measures; and (3) UAS–satellite synergistic models, where UAS-estimated biomass served as surrogate ground truth for calibrating satellite-derived VIs and texture features. Our results show that the error increased by up to 49% for XGBoost and 31% for RF when using field-measured cereal rye biomass at a 0.5 × 0.5 m² to directly train satellite-derived features with 10 m resolution (RMSE = 83.09 g m⁻² for XGBoost and 80.46 g m⁻² for RF), compared to using UAS-derived features at 5 cm (RMSE = 55.78 g m⁻² for XGBoost and 61.63 g m⁻² for RF). Notably, the UAS–satellite synergistic model demonstrated improved alignment with RMSE of 59.79 g m⁻² for XGBoost and 61.45 g m⁻² for RF while potentially overcoming the limitations due to differences in the size of satellite pixels and field measurements. These findings underscore the potential of UAS-derived biomass estimates to improve the accuracy, scalability, and spatial fidelity of satellite-based cover crop biomass estimation.

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.