J. Alex Thomasson

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.

Comparison of mosaicking techniques for airborne images from consumer-grade cameras

Abstract. Images captured from airborne imaging systems can be mosaicked for diverse remote sensing applications. The objective of this study was to identify appropriate mosaicking techniques and software to generate mosaicked images for use by aerial applicators and other users. Three software packages—Photoshop CC, Autostitch, and Pix4Dmapper—were selected for mosaicking airborne images acquired from a large cropping area. Ground control points were collected for georeferencing the mosaicked images and for evaluating the accuracy of eight mosaicking techniques. Analysis and accuracy assessment showed that Pix4Dmapper can be the first choice if georeferenced imagery with high accuracy is required. The spherical method in Photoshop CC can be an alternative for cost considerations, and Autostitch can be used to quickly mosaic images with reduced spatial resolution. The results also showed that the accuracy of image mosaicking techniques could be greatly affected by the size of the imaging area or the number of the images and that the accuracy would be higher for a small area than for a large area. The results from this study will provide useful information for the selection of image mosaicking software and techniques for aerial applicators and other users.

Change detection of cotton root rot infection over 10-year intervals using airborne multispectral imagery

Airborne multispectral imagery is useful for mapping cotton root rot.NDVI-based classification is effective for creating prescription maps.Spatial patterns of the disease were similar over a 10-year interval.Buffer zones should be added to account for potential expansion of the disease.Results provide guidelines for site-specific management of cotton root rot. Cotton root rot is a very serious and destructive disease of cotton grown in the southwestern United States. Accurate information regarding its spatial and temporal distribution within fields is important for effective management of the disease. The objectives of this study were to examine the consistency and variation of cotton root rot infections within cotton fields over 10-year intervals using airborne multispectral imagery and to assess the feasibility to use historical imagery to create prescription maps for site-specific management of the disease. Airborne multispectral images collected from a 102-ha cotton field in 2001 and 2011 and from a 97-ha field in 2002 and 2012 in south Texas were used in this study. The images were rectified and resampled to the same pixel size between the two years for each field. The normalized difference vegetation index (NDVI) images were generated and unsupervised classification was then used to classify the NDVI images into root rot-infected and non-infected zones. Change detection analysis was performed to detect the consistency and change in root rot infection between the two growing seasons for each field. Results indicate that the spatial patterns of the disease were similar between the two seasons, though variations existed for each field. To account for the potential expansion and temporal variation of the disease, buffer zones around the infected areas were created. The buffered maps between the two years agreed well. The results from this study demonstrate that classification maps derived from historical images in conjunction with appropriate buffer zones can be used as prescription maps for site-specific fungicide application to control cotton root rot.

Characteristics of High-Biomass Sorghum as a Biofuel

High-biomass sorghum will play an important role as the United States’ moves toward alternative energy sources, particularly biomass, to supply transportation and electrical energy to the country. To minimize cost and identify shortfalls through the biomass supply chain, the biomass needs to be characterized as it affects conversion processes and logistics. The objective of this paper is to characterize high-biomass sorghum as it relates to logistics and biofuel conversion. Samples were taken and fractioned into leaves and top, middle, and bottom thirds for moisture content determination, elemental analysis, and total dietary fiber analysis. The sampling methods simulated dual-harvest and single harvest cutting systems to characterize the sorghum as it matured. The fractioned sorghum showed little difference in elemental composition but appears to slightly increase in carbon content from 43% to 45% later in the year, which would eliminate the need to remove fractions for higher value fractions. Additional ash analysis of harvested sorghum showed potential differences in soil entrainment between conditioning equipment, which would increase the ash content and lower the quality of the biomass. Equipment types could be indentified to limit the soil entrainment of the biomass, increasing the value of the biomass. As shown in this paper, the elemental composition of the sorghum varieties are comparable to commonly used biofuel feedstocks with an expected higher yield per unit area. No comparison can be made to compare the sugar composition of the sorghum as different methods were used for analysis from those found it literature.

IMAGE-BASED SWEETPOTATO YIELD AND GRADE MONITOR

An image-based system for monitoring yield and grade of sweetpotatoes was developed. Estimates of weight werebased on multiple-linear regression and neural networks, while grade classifications were based on linear discriminant analysisand neural networks. Sweetpotato features considered were pixel area, polar moment of inertia, rectangular height and width,and length of major and minor axes. The system was tested on stationary sweetpotatoes in the laboratory. Its estimates ofsweetpotato weights were highly correlated (R2 = 0.96) with actual weights, and grade classifications of marketable sweetpotatoeswere over 90% accurate. The system was also tested on sweetpotatoes moving on a harvester’s conveyor belt in the field. In thisportion of the study, estimates of sweetpotato weights were still highly correlated (R2 = 0.91), albeit not as strongly, with actualweights. Grade classifications during harvesting were less accurate (R2 = 0.73 in the best case) than in the laboratory.

Imaging for High-Throughput Phenotyping in Energy Sorghum

The increasing energy demand in recent years has resulted in a continuous growing interest in renewable energy sources, such as efficient and high-yielding energy crops. Energy sorghum is a crop that has shown great potential in this area, but needs further improvement. Plant phenotyping—measuring physiological characteristics of plants—is a laborious and time-consuming task, but it is essential for crop breeders as they attempt to improve a crop. The development of high-throughput phenotyping (HTP)—the use of autonomous sensing systems to rapidly measure plant characteristics—offers great potential for vastly expanding the number of types of a given crop plant surveyed. HTP can thus enable much more rapid progress in crop improvement through the inclusion of more genetic variability. For energy sorghum, stalk thickness is a critically important phenotype, as the stalk contains most of the biomass. Imaging is an excellent candidate for certain phenotypic measurements, as it can simulate visual observations. The aim of this study was to evaluate image analysis techniques involving K-means clustering and minimum-distance classification for use on red-green-blue (RGB) images of sorghum plants as a means to measure stalk thickness. Additionally, a depth camera integrated with the RGB camera was tested for the accuracy of distance measurements between camera and plant. Eight plants were imaged on six dates through the growing season, and image segmentation, classification and stalk thickness measurement were performed. While accuracy levels with both image analysis techniques needed improvement, both showed promise as tools for HTP in sorghum. The average error for K-means with supervised stalk measurement was 10.7% after removal of known outliers.

Moisture Loss and Ash Characterization of High-Tonnage Sorghum

Field drying dedicated energy crops may be required to improve biomass logistics by using conditioning and windrow manipulation to facilitate in-field moisture loss. These methods are thought to significantly impact ash concentrations, which reduces the feedstock quality. The objectives of the paper are to characterize ash compositions of two high-tonnage sorghum varieties by fraction and maturity, and quantify ash increases due to conditioning, raking harvesting, and storage. A sorghum-sudan variety was planted under a ratoon-cropping system, and energy sorghum planted under a single-harvest system. Standing crop samples were collected – prior to harvest – and fractioned according to stalk thirds and leaf to determine ash composition differences by plant fraction and maturity. After conditioning, windrow samples were collected to monitor moisture loss and ash entrainment and provide a basis for subsequent raking manipulation. A modified cotton module builder was used to create large packages for storage, and samples gathered at three heights and various depths to characterize ash compositional changes during the storage duration. The plant leaf fractions were found to be significantly greater than the stalk fractions, and ash composition differed by variety and with maturity. No significant amount of soil was entrained in the windrows due to conditioning and multiple raking events. Moisture distributions within the modules significantly changed with storage, and ash was only significant for the low moisture module at the surface where degradation was apparent. The higher moisture package did not significantly change ash compositions.

Toward On-line Measurement of Algal Properties

Algae is a potential source of large amounts of lipids for conversion to hydrocarbon fuels. Industrial-scale algae production requires process control, which further requires sensors to measure critical algal properties. One of the principal properties that needs to be measured in algae production is optical density. In this work an opto-electronic sensor was developed for the initial purpose of measuring optical density in real time in situ. A prototype was built and proved to be very accurate, with an R2 value greater than 0.98 when compared to laboratory OD measurements. The prototype also worked very well in a field test in which the range of OD values measured was limited.

A Comparison of Regression and Regression-kriging for Soil Characterization Using Remote Sensing Imagery

In precision agriculture regression has been used widely to quantify the relationship between soil attributes and other environmental variables. However, spatial correlation existing in soil samples usually makes the regression model suboptimal. In this study, a regression-kriging method was attempted in relating soil properties to the remote sensing image of a cotton field near Vance, Mississippi. The regression-kriging model was developed and tested by using 273 soil samples collected from the field. The result showed that by properly incorporating the spatial correlation information of regression residuals, the regression-kriging model generally achieved higher prediction accuracy than the stepwise multiple linear regression model. Most strikingly, a 50% increase in prediction accuracy was shown in Na. Potential usages of regression-kriging in future precision agriculture applications include real-time soil sensor development and digital soil mapping.

Wireless-and-GPS system for cotton fiber-quality mapping

A system including wireless-communication and GPS technologies was designed, constructed and field tested to enable site-specific crop management in cotton production in the form of fiber-quality mapping. The system is comprised of three functional sub-systems associated with the three machines typically used in cotton harvesting: harvester, boll buggy and module builder. Harvest area for a basket load of cotton is recorded with GPS, and the module into which a basket is dumped is tracked through wireless communication among the sub-systems. In three field tests, the system was easily installed on equipment and performed as designed. Fiber-quality maps were produced by combining the GPS-based module area data collected during harvest with bale-level fiber-quality data measured at a cotton classing office after ginning. Statistical analysis showed significant differences in most cotton fiber properties among mapped modules, and spatial trends were identified. The system provides a useful tool for studying spatial variability in cotton fiber quality.