Wheat streak mosaic (WSM) has long been considered the most serious disease of winter wheat in the Great Plains of America. The most effective control method of the WSM disease complex is to manage volunteer wheat that is often caused by the pre-harvest hailstorms in the Great Plains. A significant WSM disease outbreak over a large geographic area in western Nebraska, United States, in the 2017 wheat growing season was studied in relation to a pre-harvest hail in 2016 using open-data Landsat and Sentinel imagery and NOAA MRMS MESH hail data. Wheat growing areas were delineated and a WSM disease severity heat map was generated based on field NDRE readings from the satellite images. The spatial relationship between the hail event and WSM disease severity was demonstrated. For this extensive hail event, the impact from the WSM disease complex extended well beyond the area of impact from the pre-harvest hail. The dramatic extent of the virus impacts beyond the area of direct hail impact demonstrated in this study, strongly reinforces the need for community-wide attention to virus management in cases where large hail streaks occur at the optimum time for the development of pre-harvest hail (ca. within the 3 weeks before harvest). These findings not only agree with the assumption that the pre-harvest hail damaged wheat fields serve as the source fields of mites and virus, but also improves our understanding of the spatial pattern of virus spread which can benefit growers in making management decisions. The remote sensing methods used to delineate these findings proved valuable tools in studying this relationship.

Screening for drought tolerance is critical to ensure high biomass production of bioenergy sorghum in arid or semi-arid environments. The bottleneck in drought tolerance selection is the challenge of accurately predicting biomass for a large number of genotypes. Although biomass prediction by low-altitude remote sensing has been widely investigated on various crops, the performance of the predictions are not consistent, especially when applied in a breeding context with hundreds of genotypes. In some cases, biomass prediction of a large group of genotypes benefited from multimodal remote sensing data; while in other cases, the benefits were not obvious. In this study, we evaluated the performance of single and multimodal data (thermal, RGB, and multispectral) derived from an unmanned aerial vehicle (UAV) for biomass prediction for drought tolerance assessments within a context of bioenergy sorghum breeding. The biomass of 360 sorghum genotypes grown under well-watered and water-stressed regimes was predicted with a series of UAV-derived canopy features, including canopy structure, spectral reflectance, and thermal radiation features. Biomass predictions using canopy features derived from the multimodal data showed comparable performance with the best results obtained with the single modal data with coefficients of determination (R2) ranging from 0.40 to 0.53 under water-stressed environment and 0.11 to 0.35 under well-watered environment. The significance in biomass prediction was highest with multispectral followed by RGB and lowest with the thermal sensor. Finally, two well-recognized yield-based drought tolerance indices were calculated from ground truth biomass data and UAV predicted biomass, respectively. Results showed that the geometric mean productivity index outperformed the yield stability index in terms of the potential for reliable predictions by the remotely sensed data. Collectively, this study demonstrated a promising strategy for the use of different UAV-based imaging sensors to quantify yield-based drought tolerance.

Leaf area index (LAI) is an important phenotypic trait closely related to plant vigor and biomass. It is also a key parameter used in crop growth modeling. However, manually measuring LAI in the field can be slow and labor intensive. High resolution remote sensing, such as unmanned aircraft systems (UAS), has been explored for LAI estimation but with limited data sources, usually RGB and multispectral imagery. As UAS-based thermal infrared (TIR) imaging becoming readily available in agriculture, it is worth investigating the potential of its role in improving LAI estimation. In this study we evaluated the importance of canopy temperature measured by UAS-based TIR and multispectral imagery on maize LAI quantification within a breeding context (23 genotypes). Five plot-level features (canopy temperature, structure and two common vegetation indices) were extracted from the images, and used as inputs of machine learning models for the LAI estimation. The performance of the estimation was evaluated with a 5-fold cross validation with 30 random repeats for 162 samples. Results showed that, canopy temperature, together with canopy structure as model predictors, slightly improved LAI estimation (root mean square error, RMSE of 0.853 m(2)/m(2) and coefficient of determination, R-2 of 0.740) than those models without temperature difference (RMSE of 0.917 m(2)/m(2) and R-2 of 0.706) for the various genotypes included in this study. In addition, canopy temperature showed moderate and more stable significance in estimating LAI than plant height and image uniformity. Its contribution to the estimation was comparable or even higher than those from vegetation indices when being modeled with random forest in this study. These relationships may be changed with a single or less genotypes which can be explored in future studies.

Woody plant encroachment into grasslands ecosystems causes significantly ecological destruction and economic losses. Effective and efficient management largely benefits from accurate and timely detection of encroaching species at an early development stage. Recent advances in unmanned aircraft systems (UAS) enabled easier access to ultra-high spatial resolution images at a centimeter level, together with the latest machine learning based image segmentation algorithms, making it possible to detect small-sized individuals of target species at early development stage and identify them when mixed with other species. However, few studies have investigated the optimal practical spatial resolution of early encroaching species detection. Hence, we investigated the performance of four popular semantic segmentation algorithms (decision tree, DT; random forest, RF; AlexNet; and ResNet) on a multi-species forest classification case with UAS-collected RGB images in original and down-sampled coarser spatial resolutions. The objective of this study was to explore the optimal segmentation algorithm and spatial resolution for eastern redcedar (Juniperus virginiana, ERC) early detection and its classification within a multi-species forest context. To be specific, firstly, we implemented and compared the performance of the four semantic segmentation algorithms with images in the original spatial resolution (0.694 cm). The highest overall accuracy was 0.918 achieved by ResNet with a mean interaction over union at 85.0%. Secondly, we evaluated the performance of ResNet algorithm with images in down-sampled spatial resolutions (1 cm to 5 cm with 0.5 cm interval). When applied on the down-sampled images, ERC segmentation performance decreased with decreasing spatial resolution, especially for those images coarser than 3 cm spatial resolution. The UAS together with the state-of-the-art semantic segmentation algorithms provides a promising tool for early-stage detection and localization of ERC and the development of effective management strategies for mixed-species forest management.

Leaf stomata regulate the process of gas exchange between the plant and the atmosphere, therefore play an important role in plant growth and water use. Thermal infrared sensing of leaf surface temperature is proved to be an indirect but effective approach to estimate leaf stomatal conductance, and shows the potential to rapidly differentiate genotypes for water-use related traits. The objective of this study was to estimate leaf stomatal conductance from thermal IR images of crops and relevant environmental parameters. The experiment was conducted in the NU-Spidercam field phenotyping facility near Mead, NE. Leaf stomatal conductance was measured from soybean, sorghum, maize, and sunflower using a leaf porometer. Thermal IR images of the crop canopies were captured by a thermal IR camera and then processed to extract crop canopy temperature (Tc). In addition, weather variables including solar radiation, air temperature, relative humidity, and wind speed were extracted from a nearby weather station. Correlation analysis was implemented to explore the relationships between these variables. Multiple linear regression (MLR), random forest (RF), gradient boosting machine (GBM) were applied to model stomatal conductance from Tc and weather variables. The Pearson correlation coefficients between predicted and measured stomatal conductance were 0.495 for MLR, 0.591 for RF, and 0.878 for GBM when Tc was not used as an input variable. After adding Tc as input, Pearson correlation coefficients were improved to 0.584 for MLR, 0.593 for RF, and 0.896 for GBM. The mean absolute errors for the three models were 225, 237, and 129 mmol/(m2·s) when Tc was included as a model input. This research would lead to rapid assessment of leaf stomatal conductance and crop water status using thermal IR imaging.

Though unmanned aircraft systems (UAS) are widely used in agriculture, their current positioning accuracy in a radius of 0.5 to 2 meters is still too low to pinpoint a crop row or to precisely overlay temporal multi-source field maps together without a valid geometric calibration. The positioning accuracy of UAS deployed with real time kinematic (RTK) global navigation satellite system (GNSS) can be largely increased to a centimeter level, which was claimed from the manufacturers. This paper includes the preliminary test results of positioning accuracy of a commercial RTK UAS over a set of fixed position panels in our customized scenarios. Images were collected in three GNSS modes (regular GNSS without RTK, RTK mode 1 - not corrected by the positioning error of the base station, and RTK mode 2 - corrected by the positioning error of the base station) in static and in-flight settings. In the static setting, horizontal accuracies were 2.17 cm for the RTK mode 2, 12.11 cm for the RTK mode 1, and 11.46 cm for the regular GNSS mode. The significant result of horizontal accuracy in the in-flight setting was that RTK mode 2 without GCPs (2.82 cm) showed comparable accuracy with the commonly used regular GNSS mode with GCPs (1.34 cm). The vertical positioning accuracy in the static setting were 6.01 cm for the RTK mode 2, 5.65 cm for the RTK mode 1, and 10.48 cm for the regular GNSS mode. The accuracy of height measurement from digital surface models (DSMs) without and with GCPs in RTK mode 2 were 4.81 cm and 3.72 cm, respectively, which were the best performance among the three modes. In summary, the RTK UAS tested in this study showed great potential in eliminating the requirement of using GCPs and in high-positioning-accuracy application. The next phase is to test the system in field for accurate crop height measurement at different growth stages in agricultural application.

In view of the shortage of most current research results on the basis of iBeacon localization technology based on the ideal single static environment, the problems in positioning based on iBeacon technology are analyzed and summarized through a large number of experiments. The research problems include: the impact of the iBeacon parameter configuration on iBeacon positioning, including the effect of transmit power of the iBeacon, the configuration of the transmit interval, and the factory error of iBeacon device on the positioning of the iBeacon; iBeacon's power consuption factor analysis, including the impact of transmit power, transmission interval, temperature sensing interval, inductance interval on iBeacon power consumption; RSSI correction, including RSSI attenuation analysis and the use of window to correct the RSSI; mobile device adaptation, including the comparison of RSSI reception between different Android terminal, adaptation methods and cost. And give the solutions of part of the above problems, and achieve the campus positioning system.

We used time series Beijing-1 images and ASAR WSM images in the classification of wetland aquatic macrophytes in terms of aquatic plant functional types (PFTs). We found that: (1) after speckle noise filtering, sensor systematic calibration within the same polarization and relative radiation calibration between different polarization, and accurate geo-registration, time series radar data could improve the separation among aquatic plant functional types. (2) hydrophytes from different aquatic PFTs exhibit distinct phenological, structural, moisture and roughness characteristics due to the impact of the annual water inundation of Poyang Lake wetland.(3) phenological curves and permittivity curves extracted from time series Beijing-1 images and ASAR WSM images provide vital information for distinguishing different aquatic PFTs. Multi-sensor image combination could complement previous data for time series aquatic PFT analysis.