Ruggedized, field-ready snapshot light-guide-based imaging spectrometer for environmental and remote sensing applications

A field-ready, fiber-based high spatial sampling snapshot imaging spectrometer was developed for applications such as environmental monitoring and smart farming. The system achieves video rate frame transfer and exposure times down to a few hundred microseconds in typical daylight conditions with ∼63,000 spatial points and 32 spectral channels across the 470nm to 700nm wavelength range. We designed portable, ruggedized opto-mechanics to allow for imaging from an airborne platform. To ensure successful data collection prior to flight, imaging speed and signal-to-noise ratio was characterized for imaging a variety of land covers from the air. The system was validated by performing a series of observations including: Liriope Muscari plants under a range of water-stress conditions in a controlled laboratory experiment and field observations of sorghum plants in a variety of soil conditions. Finally, we collected data from a series of engineering flights and present reassembled images and spectral sampling of rural and urban landscapes.

Development of Multiple UAV Collaborative Driving Systems for Improving Field Phenotyping

Unmanned aerial vehicle-based remote sensing technology has recently been widely applied to crop monitoring due to the rapid development of unmanned aerial vehicles, and these technologies have considerable potential in smart agriculture applications. Field phenotyping using remote sensing is mostly performed using unmanned aerial vehicles equipped with RGB cameras or multispectral cameras. For accurate field phenotyping for precision agriculture, images taken from multiple perspectives need to be simultaneously collected, and phenotypic measurement errors may occur due to the movement of the drone and plants during flight. In this study, to minimize measurement error and improve the digital surface model, we proposed a collaborative driving system that allows multiple UAVs to simultaneously acquire images from different viewpoints. An integrated navigation system based on MAVSDK is configured for the attitude control and position control of unmanned aerial vehicles. Based on the leader–follower-based swarm driving algorithm and a long-range wireless network system, the follower drone cooperates with the leader drone to maintain a constant speed, direction, and image overlap ratio, and to maintain a rank to improve their phenotyping. A collision avoidance algorithm was developed because different UAVs can collide due to external disturbance (wind) when driving in groups while maintaining a rank. To verify and optimize the flight algorithm developed in this study in a virtual environment, a GAZEBO-based simulation environment was established. Based on the algorithm that has been verified and optimized in the previous simulation environment, some unmanned aerial vehicles were flown in the same flight path in a real field, and the simulation and the real field were compared. As a result of the comparative experiment, the simulated flight accuracy (RMSE) was 0.36 m and the actual field flight accuracy was 0.46 m, showing flight accuracy like that of a commercial program.

Field-Based Calibration of Unmanned Aerial Vehicle Thermal Infrared Imagery with Temperature-Controlled References

Accurate and reliable calibration methods are required when applying unmanned aerial vehicle (UAV)-based thermal remote sensing in precision agriculture for crop stress monitoring, irrigation planning, and harvesting. The primary objective of this study was to improve the calibration accuracies of UAV-based thermal images using temperature-controlled ground references. Two temperature-controlled ground references were installed in the field to serve as high- and low-temperature references, approximately spanning the expected range of crop surface temperatures during the growing season. Our results showed that the proposed method using temperature-controlled references was able to reduce errors due to ambient conditions from 9.29 to 1.68 °C, when tested with validation panels. There was a significant improvement in crop temperature estimation from the thermal image mosaic, as the error reduced from 14.0 °C in the un-calibrated image to 1.01 °C in the calibrated image. Furthermore, a multiple linear regression model (R2 = 0.78; p-value < 0.001; relative RMSE = 2.42%) was established to quantify soil moisture content based on canopy surface temperature and soil type, using UAV-based thermal image data and soil electrical conductivity (ECa) data as the predictor variables.

Adsorbent-SERS Technique for Determination of Plant VOCs from Live Cotton Plants and Dried Teas

We developed a novel substrate for the collection of volatile organic compounds (VOCs) emitted from either living or dried plant material to be analyzed by surface-enhanced Raman spectroscopy (SERS). We demonstrated that this substrate can be utilized to differentiate emissions from blends of three teas, and to differentiate emissions from healthy cotton plants versus caterpillar-infested cotton plants. The substrate we developed can adsorb VOCs in static headspace sampling environments, and VOCs naturally evaporated from three standards were successfully identified by our SERS substrate, showing its ability to differentiate three VOCs and to detect quantitative differences according to collection times. In addition, volatile profiles from plant materials that were either qualitatively different among three teas or quantitatively different in abundance between healthy and infested cotton plants were confirmed by collections on Super-Q resin for dynamic headspace and solid-phase microextraction for static headspace sampling, respectively, followed by gas chromatography to mass spectrometry. Our results indicate that both qualitative and quantitative differences can also be detected by our SERS substrate although we find that the detection of quantitative differences could be improved.

Complexes Formed by Hydrophobic Interaction between Ag-Nanospheres and Adsorbents for the Detection of Methyl Salicylate VOC

Surface-enhanced Raman spectroscopy (SERS) has been widely investigated in many applications. However, only little work has been done on using SERS for the detection of volatile organic compounds (VOCs), primarily due to the challenges associated with fabricating SERS substrates with sufficient hotspots for signal enhancement and with the surface interfacially compatible for the VOCs. This study investigated the phase transfer of Ag-nanospheres (AgNSs) from the aqueous phase to the non-aqueous phase by electrostatic interaction induced by cationic surfactants, and the feasibility of the transferred AgNSs as SERS substrates for the determination of methyl salicylate VOC. Results indicated that one of three cationic surfactants, tetraoctylammonium bromide (TOAB) dissolved in organic solvent showed successful phase transfer of the AgNSs confirmed by several characterization analyses. The complex formed by hydrophobic interaction between the transferred AgNSs and Tenax-TA adsorbent polymer was able to be utilized as a SERS substrate, and the volatile of methyl salicylate could be easily determined from SERS measurements at 4 h static volatile collection. Therefore, the proposed new techniques can be effectively employed to areas where many VOCs relevant to food and agriculture need to be analyzed.

Measurement and Calibration of Plant-Height from Fixed-Wing UAV Images

Continuing population growth will result in increasing global demand for food and fiber for the foreseeable future. During the growing season, variability in the height of crops provides important information on plant health, growth, and response to environmental effects. This paper indicates the feasibility of using structure from motion (SfM) on images collected from 120 m above ground level (AGL) with a fixed-wing unmanned aerial vehicle (UAV) to estimate sorghum plant height with reasonable accuracy on a relatively large farm field. Correlations between UAV-based estimates and ground truth were strong on all dates (R² > 0.80) but are clearly better on some dates than others. Furthermore, a new method for improving UAV-based plant height estimates with multi-level ground control points (GCPs) was found to lower the root mean square error (RMSE) by about 20%. These results indicate that GCP-based height calibration has a potential for future application where accuracy is particularly important. Lastly, the image blur appeared to have a significant impact on the accuracy of plant height estimation. A strong correlation (R² = 0.85) was observed between image quality and plant height RMSE and the influence of wind was a challenge in obtaining high-quality plant height data. A strong relationship (R² = 0.99) existed between wind speed and image blurriness.

Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research

Advances in automation and data science have led agriculturists to seek real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices. Breeders have recently gained massive data-collection capability in genome sequencing of plants. Faster phenotypic trait data collection and analysis relative to genetic data leads to faster and better selections in crop improvement. Furthermore, faster and higher-resolution crop data collection leads to greater capability for scientists and growers to improve precision-agriculture practices on increasingly larger farms; e.g., site-specific application of water and nutrients. Unmanned aerial vehicles (UAVs) have recently gained traction as agricultural data collection systems. Using UAVs for agricultural remote sensing is an innovative technology that differs from traditional remote sensing in more ways than strictly higher-resolution images; it provides many new and unique possibilities, as well as new and unique challenges. Herein we report on processes and lessons learned from year 1-the summer 2015 and winter 2016 growing seasons-of a large multidisciplinary project evaluating UAV images across a range of breeding and agronomic research trials on a large research farm. Included are team and project planning, UAV and sensor selection and integration, and data collection and analysis workflow. The study involved many crops and both breeding plots and agronomic fields. The project's goal was to develop methods for UAVs to collect high-quality, high-volume crop data with fast turnaround time to field scientists. The project included five teams: Administration, Flight Operations, Sensors, Data Management, and Field Research. Four case studies involving multiple crops in breeding and agronomic applications add practical descriptive detail. Lessons learned include critical information on sensors, air vehicles, and configuration parameters for both. As the first and most comprehensive project of its kind to date, these lessons are particularly salient to researchers embarking on agricultural research with UAVs.

Ticks associated with domestic dogs and cats in Florida, USA

Voluntary collections of ticks from domestic dogs and cats by veterinary practitioners across Florida, USA, were conducted over a 10 month period. Of the 1337 ticks submitted, five species of ixodid ticks were identified and included Rhipicephalus sanguineus, Amblyomma americanum, A. maculatum, Dermacentor variabilis, and Ixodes scapularis. Most ticks were collected from dogs (98.4%) with the most predominant species being R. sanguineus (94.3%). Of the ticks collected from cats (1.6%), A. americanum were the most common (74%). Only R. sanguineus were collected throughout the state, with the other species collected only in central and north Florida. The tick species collected from dogs and cats represent a risk to these domestic species as well as associated humans for a range of tick-borne diseases in Florida.

Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research

Advances in automation and data science have led agriculturists to seek real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices. Breeders have recently gained massive data-collection capability in genome sequencing of plants. Faster phenotypic trait data collection and analysis relative to genetic data leads to faster and better selections in crop improvement. Furthermore, faster and higher-resolution crop data collection leads to greater capability for scientists and growers to improve precision-agriculture practices on increasingly larger farms; e.g., site-specific application of water and nutrients. Unmanned aerial vehicles (UAVs) have recently gained traction as agricultural data collection systems. Using UAVs for agricultural remote sensing is an innovative technology that differs from traditional remote sensing in more ways than strictly higher-resolution images; it provides many new and unique possibilities, as well as new and unique challenges. Herein we report on processes and lessons learned from year 1-the summer 2015 and winter 2016 growing seasons-of a large multidisciplinary project evaluating UAV images across a range of breeding and agronomic research trials on a large research farm. Included are team and project planning, UAV and sensor selection and integration, and data collection and analysis workflow. The study involved many crops and both breeding plots and agronomic fields. The project's goal was to develop methods for UAVs to collect high-quality, high-volume crop data with fast turnaround time to field scientists. The project included five teams: Administration, Flight Operations, Sensors, Data Management, and Field Research. Four case studies involving multiple crops in breeding and agronomic applications add practical descriptive detail. Lessons learned include critical information on sensors, air vehicles, and configuration parameters for both. As the first and most comprehensive project of its kind to date, these lessons are particularly salient to researchers embarking on agricultural research with UAVs.

Development and characterization of food-grade tracers for the global grain tracing and recall system

Tracing grain from the farm to its final processing destination as it moves through multiple grain-handling systems, storage bins, and bulk carriers presents numerous challenges to existing record-keeping systems. This study examines the suitability of coded caplets to trace grain, in particular, to evaluate methodology to test tracers' ability to withstand the rigors of a commercial grain handling and storage systems as defined by physical properties using measurement technology commonly applied to assess grain hardness and end-use properties. Three types of tracers to dispense into bulk grains for tracing the grain back to its field of origin were developed using three food-grade substances [processed sugar, pregelatinized starch, and silicified microcrystalline cellulose (SMCC)] as a major component in formulations. Due to a different functionality of formulations, the manufacturing process conditions varied for each tracer type, resulting in unique variations in surface roughness, weight, dimensions, and physical and spectroscopic properties before and after coating. The applied two types of coating [pregelatinized starch and hydroxypropylmethylcellulose (HPMC)] using an aqueous coating system containing appropriate plasticizers showed uniform coverage and clear coating. Coating appeared to act as a barrier against moisture penetration, to protect against mechanical damage of the surface of the tracers, and to improve the mechanical strength of tracers. The results of analysis of variance (ANOVA) tests showed the type of tracer, coating material, conditioning time, and a theoretical weight gain significantly influenced the morphological and physical properties of tracers. Optimization of these factors needs to be pursued to produce desirable tracers with consistent quality and performance when they flow with bulk grains throughout the grain marketing channels.

Cotton fiber quality characterization with light scattering and fourier transform infrared techniques

Fiber quality measurement is critical to assessing the value of a bale of cotton for various textile purposes. An instrument that could measure numerous cotton quality properties by optical means could be made simpler and faster than current fiber quality measurement instruments, and it might be more amenable to on-line measurement at processing facilities. To that end, a laser system was used to investigate cotton fiber samples with respect to electromagnetic scattering at various wavelengths, polarization angles, and scattering angles. A Fourier transform infrared (FT-IR) instrument was also used to investigate the transmission of electromagnetic energy at various mid-infrared wavelengths. Cotton samples were selected to represent a wide range of micronaire values. Varying the wavelength of the laser at a fixed polarization resulted in little variation in scattered light among the cotton samples. However, varying the polarization at a fixed wavelength produced notable variation, indicating that polarization might be used to differentiate among cotton samples with respect to certain fiber properties. The FT-IR data in the 12 to 22 microm range produced relatively large differences in the amount of scattered light among all samples, and FT-IR data at certain combinations of fixed wavelengths were highly linearly related to certain measures of cotton quality including micronaire.