About

Our nationally recognized faculty, researchers and professional staff are dedicated to excellence in research, education, innovation and service. Learn more about the individuals who make up the Department of Mechanical Engineering by visiting their profiles.

Research topics

• Computer Science
• Biology
• Remote sensing
• Ecology
• Geography
• Mathematics
• Agronomy
• Artificial Intelligence
• Genetics
• Risk analysis (engineering)
• Business
• Engineering
• Environmental science
• Systems engineering

Selected publications

• Unmanned aircraft systems for precision weed detection and management: Prospects and challenges

  Advances in agronomy · 2020 · 55 citations

  • Computer Science
  • Computer Science
  • Risk analysis (engineering)
  PublisherDOI

• Mapping and Estimating Weeds in Cotton Using Unmanned Aerial Systems-Borne Imagery

  AgriEngineering · 2020 · 26 citations

  • Artificial Intelligence
  • Computer Science
  • Mathematics

  In recent years, Unmanned Aerial Systems (UAS) have emerged as an innovative technology to provide spatio-temporal information about weed species in crop fields. Such information is a critical input for any site-specific weed management program. A multi-rotor UAS (Phantom 4) equipped with an RGB sensor was used to collect imagery in three bands (Red, Green, and Blue; 0.8 cm/pixel resolution) with the objectives of (a) mapping weeds in cotton and (b) determining the relationship between image-based weed coverage and ground-based weed densities. For weed mapping, three different weed density levels (high, medium, and low) were established for a mix of different weed species, with three replications. To determine weed densities through ground truthing, five quadrats (1 m × 1 m) were laid out in each plot. The aerial imageries were preprocessed and subjected to Hough transformation to delineate cotton rows. Following the separation of inter-row vegetation from crop rows, a multi-level classification coupled with machine learning algorithms were used to distinguish intra-row weeds from cotton. Overall, accuracy levels of 89.16%, 85.83%, and 83.33% and kappa values of 0.84, 0.79, and 0.75 were achieved for detecting weed occurrence in high, medium, and low density plots, respectively. Further, ground-truthing based overall weed density values were fairly correlated (r2 = 0.80) with image-based weed coverage assessments. Among the specific weed species evaluated, Palmer amaranth (Amaranthus palmeri S. Watson) showed the highest correlation (r2 = 0.91) followed by red sprangletop (Leptochloa mucronata Michx) (r2 = 0.88). The results highlight the utility of UAS-borne RGB imagery for weed mapping and density estimation in cotton for precision weed management.

  PublisherOA PDFDOI

• Unoccupied aerial system enabled functional modeling of maize height reveals dynamic expression of loci

  Plant Direct · 2020 · 44 citations

  • Biology
  • Agronomy
  • Genetics

  = 0.45-0.68). Functional growth parameters (asymptote, inflection point, growth rate) alone identified 34 genetic loci, each explaining 3-15% of total genetic variation. Plant height was estimated at one-day intervals to 85 DAS, identifying 58 unique temporal quantitative trait loci (QTL) locations. Genomic hotspots on chromosomes 1 and 3 indicated chromosomal regions associated with functional growth trajectories influencing flowering time, growth rate, and terminal growth. Temporal QTL demonstrated unique dynamic expression patterns not previously observable, and no QTL were significantly expressed throughout the entire growing season. UAS technologies improved phenotypic selection accuracy and permitted monitoring traits on a temporal scale previously infeasible using manual measurements, furthering understanding of crop development and biological trajectories.

  PublisherOA PDFDOI

• Unoccupied aerial system enabled functional modeling of maize ( <i>Zea mays</i> L.) height reveals dynamic expression of loci associated to temporal growth

  bioRxiv (Cold Spring Harbor Laboratory) · 2019-11-19 · 2 citations

  preprintOpen access

  Abstract Unoccupied aerial systems (UAS) were used to phenotype growth trajectories of inbred maize populations under field conditions. Three recombinant inbred line populations were surveyed on a weekly basis collecting RGB images across two irrigation regimens (irrigated and non-irrigated/rain fed). Plant height, estimated by the 95 th percentile (P95) height from UAS generated 3D point clouds, exceeded 70% correlation to manual ground truth measurements and 51% of experimental variance was explained by genetics. The Weibull sigmoidal function accurately modeled plant growth (R 2 : &gt;99%; RMSE: &lt; 4 cm) from P95 genetic means. The mean asymptote was strongly correlated (r 2 =0.66-0.77) with terminal plant height. Maximum absolute growth rates (mm d -1 ) were weakly correlated to height and flowering time. The average inflection point ranged from 57 to 60 days after sowing (DAS) and was correlated with flowering time (r 2 =0.45-0.68). Functional growth parameters (asymptote, inflection point, growth rate) alone identified 34 genetic loci, each explaining 3 to 15% of total genetic variation. Plant height was estimated at one-day intervals to 85 DAS, identifying 58 unique temporal quantitative trait loci (QTL) locations. Genomic hotspots on chromosome 1 and 3 indicated chromosomal regions associated with functional growth trajectories influencing flowering time, growth rate, and terminal growth. Temporal QTL demonstrated unique dynamic expression patterns not observable previously, no QTL were significantly expressed throughout the entire growing season. UAS technologies improved phenotypic selection accuracy and permitted monitoring traits on a temporal scale previously infeasible using manual measurements, furthering understanding of crop development and biological trajectories. Author summary Unoccupied aerial systems (UAS) now can provide high throughput phenotyping to functionally model plant growth and explore genetic loci underlying temporal expression of dynamic phenotypes, specifically plant height. Efficient integration of temporal phenotyping via UAS, will improve the scientific understanding of dynamic, quantitative traits and developmental trajectories of important agronomic crops, leading to new understanding of plant biology. Here we present, for the first time, the dynamic nature of quantitative trait loci (QTL) over time under field conditions. To our knowledge, this is first empirical study to expand beyond selective developmental time points, evaluating functional and temporal QTL expression in maize ( Zea mays L.) throughout a growing season within a field-based environment.

  PublisherOA PDFDOI

• Prediction of Maize Grain Yield before Maturity Using Improved Temporal Height Estimates of Unmanned Aerial Systems

  The Plant Phenome Journal · 2019-01-01 · 87 citations

  articleOpen access

  Core Ideas UAS captured increased genetic variation compared with manual terminal height. There were small significant differences in ground filtering methods to extract plant structure. Higher resolution did not improve imagery informativeness with regard to plant height. Logistic function provides informative phenotypes for temporal maize growth. Correlation and prediction accuracy of grain yield increased by ∼20% with UAS heights. Weekly unmanned aerial system (UAS) imagery was collected over the College Station, TX, 2017 Genomes to Fields (G2F) hybrid trial, across three environmental stress treatments, using two UAS platforms. The high‐altitude (120‐m) fixed‐wing platform increased the fraction of variation attributed to genetics and had highly repeatable ( R &gt; 60%) height estimates, increasing the genetic variance explained (10–40%) over traditional terminal plant height measurement (PHT TRML ∼30%), as well as over the low‐altitude rotary‐wing UAS platform (10–20%). A logistic function reduced the dimensionality (&gt;20 flights) of each UAS dataset to three parameters (inflection point, growth rate, and asymptote) and produced a more robust predictive model than independent flight dates, effectively summarizing ( R 2 &gt; 0.98) the UAS flight dates. The logistic model overcame the need to use specific flight dates when comparing different environments. The UAS height estimates ( r = 0.36–0.48) doubled the correlations to grain yield in this G2F experiment compared with PHT TRML ( r = 0.23–0.28). Parameters of the logistical function achieved equivalent correlations ( r = 0.30–0.46) to individual flight dates ( r = 0.36–0.48), improving grain yield prediction by ∼400% ( R 2 = 0.25–0.34) over PHT TRML ( R 2 = 0.06–0.08). Incorporating other UAS‐derived parameters beyond plant height may allow yield to be accurately predicted before maturity, speeding breeding programs. A new public R function to generate ESRI shapefiles for plot research is also described.

  PublisherOA PDFDOI

• High-Resolution UAS Imagery in Agricultural Research Concepts, Issues, and Research Directions

  2018-06-27 · 2 citations

  book-chapter

  The advent of unmanned aerial systems (UASs) permits data collection at high spatial and temporal resolutions, enabling high-throughput phenotyping and precision agriculture. Numerous scientific and technical challenges present themselves associated with methods of UAS data collection, preprocessing, information extraction and synthesis, and decision support. Multiple UAS experiments recently conducted at the Texas A&M University Research Farm near College Station, Texas, highlight issues such as sensor-platform modularity, data acquisition strategies, and preprocessing and information extraction problems that need to be more efficiently addressed. Radiometric and geometric calibration are shown to be both nontrivial and essential for reliable vegetation assessment and monitoring. Various vegetation indices and thematic mapping approaches exist but are currently incapable of diagnostic assessment of plant species or stress status. Evaluation of multiple crop and weed species with spectral and spatial wavelet analysis reveals patterns that may be diagnostic for assessing plant and canopy structure. Ultimately, UASs have great potential for obtaining imagery that can assist in improving crop yield and providing information for decision support, but further research is needed to optimize UAS engineering configurations and to address a variety of issues related to preprocessing of imagery, diagnostic analysis of environmental and plant conditions, and artificial intelligence for decision support.

  PublisherDOI

• Calibrated plant height estimates with structure from motion from fixed-wing UAV images

  2018-05-15 · 5 citations

  articleSenior author

  Field-based high-throughput phenotyping is a bottleneck to future breeding advances. The use of remote sensing with unmanned aerial vehicles (UAVs) can change the way agricultural research operates by increasing the spatiotemporal resolution of data collection to monitor status of plant growth. A fixed-wing UAV (Tuffwing) was operated to collect images of a sorghum breeding research field with 70% overlap at an altitude of 120 m. The study site was located at Texas A and M AgriLife Research’s Brazos Bottom research farm near College Station, Texas, USA. Relatively high-resolution (&gt;2.7cm/pixel) images were collected from May to July 2017 over 880 sorghum plots (including six treatments with four replications). The collected images were mosaicked and structure from motion (SfM) calculated, which involves construction of a digital surface model (DSM) by interpolation of 3D point clouds. Maximum plant height for each genotype (plot) was estimated from the DSM and height calibration implemented with aerial measured values of groundcontrol points with known height. Correlations and RMSE values between actual height and estimated height were observed over sorghum across all genotypes and flight dates. Results indicate that the proposed height calibration method has a potential for future application to improve accuracy in plant height estimations from UAVs.

  PublisherDOI

• Estimation of plant health in a sorghum field infected with anthracnose using a fixed-wing unmanned aerial system

  Journal of Crop Improvement · 2018-10-29 · 9 citations

  article

  Diseases cause enormous losses of yield and quality for crop producers worldwide. To meet future food demands, crops are bred for resistance to as many of these maladies as possible. One such disease, anthracnose [Colletotrichum sublineola], is a fungal disease of great importance to sorghum [Sorghum bicolor, L. Moench] production because it causes significant annual economic losses in the crop. Breeding for anthracnose resistance requires time-consuming phenotyping, which is subjective and conditional to the evaluator. It is possible that quantitative assessment using high-throughput methodologies to estimate the trait may be more effective. In this study, we present an in-depth statistical analysis of fixed-wing, unmanned aerial system (UAS) evaluation of anthracnose incidence and severity in sorghum using normalized difference vegetation index (NDVI). In early phases of infection, correlations between ground-truth and UAS estimates of anthracnose were moderate but they increased substantially by the end of the season (r = −0.55 to −0.95). Additionally, both metrics had moderate-to-high repeatabilities throughout the growth period (R = 0.60–0.90), indicating they were consistently able to differentiate genotypes. Finally, we found that the UAS-derived measurements (R2 = 0.377, 0.473) were better associated with ground-truth measurements (R2 = 0.278, 0.347) for grain yield under anthracnose pressure. The results of this study indicated that fixed-wing UAS could potentially be effective for evaluating anthracnose disease present in sorghum, and the greater range of the UAS allowed the effective evaluation of larger numbers of plants than ground truth or traditional remote sensing methods.

  PublisherDOI

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

  Sensors · 2018-11-22 · 91 citations

  articleOpen accessSenior author

  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.

  PublisherOA PDFDOI

• A case study of comparing radiometrically calibrated reflectance of an image mosaic from unmanned aerial system with that of a single image from manned aircraft over a same area

  Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2017-05-19

  article

  Though sharing with many commonalities, one of the major differences between conventional high-altitude airborne remote sensing and low-altitude unmanned aerial system (UAS) based remote sensing is that the latter one has much smaller ground footprint for each image shot. To cover the same area on the ground, it requires the low-altitude UASbased platform to take many highly-overlapped images to produce a good mosaic, instead of just one or a few image shots by the high-altitude aerial platform. Such an UAS flight usually takes 10 to 30 minutes or even longer to complete; environmental lighting change during this time span cannot be ignored especially when spectral variations of various parts of a field are of interests. In this case study, we compared the visible reflectance of two aerial imagery – one generated from mosaicked UAS images, the other generated from a single image taken by a manned aircraft – over the same agricultural field to quantitatively evaluate their spectral variations caused by the different data acquisition strategies. Specifically, we (1) developed our customized ground calibration points (GCPs) and an associated radiometric calibration method for UAS data processing based on camera’s sensitivity characteristics; (2) developed a basic comparison method for radiometrically calibrated data from the two aerial platforms based on regions of interests. We see this study as a starting point for a series of following studies to understand the environmental influence on UAS data and investigate the solutions to minimize such influence to ensure data quality.

  PublisherDOI

Frequent coauthors

• J. Alex Thomasson

  Mississippi State University

  9 shared

• Jinha Jung

  Purdue University West Lafayette

  6 shared

• Anjin Chang

  Tennessee State University

  6 shared

• Lonesome Malambo

  Texas A&M University

  6 shared

• Sorin Popescu

  Texas A&M University

  6 shared

• Seth C. Murray

  Texas A&M University

  5 shared

• William L. Rooney

  5 shared

• Chenghai Yang

  Agricultural Research Service

  4 shared

Awards & honors

• 2021 College of Engineering - Instructional Faculty Teaching…
• 2021 Aggie Club of Engineers Professor of Excellence Award
• 2018 ASME Best Teacher Award
• 2012 Associate Fellow, American Institute of Aeronautics and…
• 2007 Meritorious Service Medal, U.S. Air Force (USAF)