Progress 03/01/24 to 02/28/25
Outputs
Target Audience:During this reporting period, our target audiences were students, researchers, and teachers. We conducted several middle school and high schools' visits to showcase the use of Unmanned Aerial Systems (UAS) in agriculture. We also hosted field trips for high school students at Texas A&M AgriLife Research and Extension Center to demonstrate the use of UAS in agriculture especially focusing on how data can be collected using drones and sensors to understand plant growth and development. The students also learned about data processing pipelines. In addition to the middle school and high school students, we also hosted field trips for K-12 teachers and Undergraduate students in the Summer of 2024. The project team introduced fundamental concepts and knowledge to undergraduate students and K-12 teachers through a Research and Extension Experiences for Undergraduates (REEU) program and a Professional Development for Agricultural Literacy (PDAL) funded by USDA at Texas A&M University-Kingsville. In addition to reaching out to students and teachers through a training program, we also presented the results of our experiments in several national and international conferences showcasing the potential of energycane (life cycle analysis results) and the use of UAS-based phenotyping for evaluating energycane cultivars. Changes/Problems:Weather-Related Challenges: As anticipated in the project narrative, weather conditions posed significant challenges to our agronomic field trials this year. Rainfall levels were above average, making it necessary to collect an additional year of data to accurately assess the effects of drought in therainfed experimental plots. Furthermore, the region experienced two consecutive years of freezing temperatures, which have delayed the intercroppingof the two top-performing cultivarswith cool-season legumes at the Corpus Christi location. What opportunities for training and professional development has the project provided?1. Opportunities for graduate students Benjamin Ghansah began his Ph.D. program in Computer and Geospatial Science at Texas A&M University-Corpus Christi in Fall 2023, with support from this project. His research centers on developing remote sensing-based crop phenotyping methods for energy cane. In addition to his academic coursework, Benjamin presented his research at the 2024 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) in Athens, Greece, The Third Annual Artificial Intelligence in Agriculture Conference, April 2024, Collage Station, Texas-USA (oral presentation), and ASA-CSSA-SSSA International Annual Meeting, November 2024, San Antonio, TX (oral). Joaquin Haces-Garcia, a master's student in Industrial Engineering at Texas A&M University-Kingsville under the guidance of Dr. Hua Li, is contributing to the project's Life Cycle Assessment (LCA) component. He participated in one conference and attended several workshops related to his research. Additionally, Graduate Research Assistant Ittipon Khuimphukhieo (PhD in Plant Breeding at TAMU), working under the supervision of Dr. Jorge Da Silva, received support from this project. His training included sensor installation, data analytics, UAS data collection and processing, and feature extraction. He also published a manuscript based on data generated through this project. 2. Opportunities for Post-Doctoral Researcher: Dr. Sayantan Sarkar is working as a Post-Doctoral Researcher on this project. His major research area includes the integration of AI and Multisource Remote Sensing for High-Throughput Phenotyping of Energy cane Water Use Efficiency. He also participated and presented his research at the ASA-CSSA-SSSA International Annual Meeting, November 2024, San Antonio, TX (oral). 3. Opportunities for Undergraduate students: FourUndergraduate students were also trained under this project on field work and the UAS data processing pipeline. How have the results been disseminated to communities of interest?We organized multiple outreach activities targeting middle and high school students to highlight the application of Unmanned Aerial Systems (UAS) in agriculture. These included school visits and hosting field trips at the Texas A&M AgriLife Research and Extension Center, where students were introduced to how drones and sensors are used to collect agricultural data for analyzing plant growth and development. The sessions also covered data processing techniques. In addition to engaging students, we hosted field trips and educational sessions for K-12 teachers and undergraduate students during the summer of 2024. Partnering with the USDA-funded Research and Extension Experiences for Undergraduates (REEU) program and the Professional Development for Agricultural Literacy (PDAL) program at Texas A&M University-Kingsville, the project team provided foundational knowledge on UAS technologies and their role in agriculture, supporting both student learning and teacher professional development. What do you plan to do during the next reporting period to accomplish the goals? Continue the data collection of the first ratoon crop using the soil moisture sensors, UAS, and ground measurements of biomass Publish two peer-reviewed journal articles Continuing the advancement of UAS-based HTP pipeline for it to be used for energy cane breeding programs, as well as in other crops Explore satellite-based remote sensing applications for crop height estimations in farmers' fields as well as in research plots for phenotypic data collection Grow the top two genotypes identified from this year's data to further evaluate their performance intercropping with cool-season legumes Develop the protocol to measure greenhouse gas measurements and perform environmental impact analysis (obj 3) Perform LCA analysis of energy cane-based biomass production system using the data collected from the field trials: The project team has conducted a thorough literature review to identify possible databases, especially life cycle inventory databases, to be used. To further assess environmental impacts, the Building for Environmental and Economic Sustainability (BEES+) module will be incorporated as a standardized benchmarking framework inside the OpenLCA software. The BEES+ module serves as a critical reference point, offering a reliable foundation for evaluating key environmental parameters using the chemical composition of the product chain and resulting in the varying environmental indicators, including carbon dioxide (CO?) emissions, ecotoxicity, and acidification potential. The project team will use OpenLCA, BEES+, and data collected from the field trials to assess the environmental impacts of the biomass products when the data from the field trials are collected. To test the LCA framework and the functionality of OpenLCA, the project team used data from a previous project to conduct a Well-to-Tank LCA study on energy cane. The results were published as a peer-reviewed conference paper and will be presented at the 2025 Institute of Industrial and Systems Engineers Annual Conference. Learning modules related to Life Cycle Assessment will be developed in the Summer of 2025
Impacts
What was accomplished under these goals?
Extreme weather events, including drought and heat stress, continue to pose significant threats to global agricultural production, impacting food security worldwide. High biomass crops like energy cane are anticipated to contribute substantially as feedstock for bioenergy and bio-based industries, particularly when cultivated under low-input systems. Successful cultivation in such conditions requires strategic selection of plant species, genotypes, and optimized agronomic practices. To support this goal, we conducted integrated field studies combining crop selection and breeding, field production trials, high-throughput phenotyping (HTP) using unmanned aerial systems (UAS), and life cycle assessments (LCA). We aim to identify warm-season perennial grass genotypes that demonstrate strong biomass productivity alongside favorable water and carbon footprints across diverse environmental conditions in the southern United States. A key accomplishment this year was the development of a UAS-based high-throughput phenotyping pipeline to assist breeders in selecting energy cane cultivars. This pipeline includes the identification and validation of key traits associated with canopy growth dynamics, which can inform weed competitiveness and water-use efficiency assessments. Moreover, we developed and applied a UAS-based thermal imaging workflow to extract canopy temperature data, enabling the assessment of drought stress tolerance among energy cane genotypes. Another key accomplishment in his year was to evaluate energy cane genotypes using datasets from multiple platforms, including soil moisture sensors and ground measurements, and UAS-based canopy growth features. Seven energy cane genotypes were grown in a randomized complete block design with four replications under three irrigation treatments. Throughout the season, we conducted 26 UAS flight missions to capture data on canopy cover (CC), canopy height (CH), and various vegetation indices. These data were used to analyze growth patterns, including growth rates and key developmental stages. Soil moisture sensors provided daily water use data, allowing us to calculate total seasonal water requirements per unit of biomass for each genotype. Additionally, we developed a biomass estimation model leveraging UAS-based LiDAR data and validated it with in-season and final biomass harvests. Data analysis, both independently and combined across platforms, identified two promising energy cane genotypes, TH16-22 and TH16-16, which will be further evaluated under legume-based cropping systems in a new location.
Publications
- Type:
Peer Reviewed Journal Articles
Status:
Published
Year Published:
2025
Citation:
Khuimphukhieo, I., Zhao, L., Ghansah, B., Scott, J. L. L., Fernandez-Montero, O., da Silva, J. A., ... & Bhandari, M. (2025). Use of Uncrewed Aerial System (UAS)-Based Crop Features to Perform Growth Analysis of Energy Cane Genotypes. Plants, 14(5), 654.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Ghansah, B., Khuimphukhieo, I., Scott, J. L. L., Bhandari, M., Starek, M. J., Foster, J., ... & Li, H. (2024, July). Utilizing UAS-Lidar for High Throughput Phenotyping of Energy Cane. In IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium (pp. 6251-6254). IEEE.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Ghansah B., Khuimphukhieo I., Landivar Scott J. L., Bhandari M., Starek J. M., Foster J., Da Silva J., Li H. High Throughput Phenotyping of the Energy Cane Crop Using UAS Lidar. 2024 16th International Conference on Precision Agriculture, July 2024, Manhattan, Kansas, USA, 21-24 July 2024 (oral presentation).
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Ghansah B., Khuimphukhieo I., Landivar Scott J. L., Bhandari M., Starek J. M., Foster J., Da Silva J., Li H. High Throughput Phenotyping of the Energy Cane Crop Using UAS Lidar. 2024 IEEEGARSS, July 2024, Athens, Greece (oral presentation).
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Ghansah B., Khuimphukhieo I., Landivar Scott J. L., Bhandari M., Starek J. M., Foster J., Da Silva J., Li H. High Throughput Phenotyping of the Energy Cane Crop Using UAS-LiDAR, Multispectral and RGB Sensors. The Third Annual Artificial Intelligence in Agriculture Conference, April 2024, Collage Station, Texas-USA (oral presentation).
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Ghansah B., Zhao L., Bhandari M., Landivar Scott J. L., Starek J. M., Landivar J. Crop Canoypy Height Estimation using High-Resolution SkySat Imagery and Machine Learning Models. The Third Annual Artificial Intelligence in Agriculture Conference, April 2024, Collage Station, Texas-USA (poster).
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Sarkar S., B. Ghansah, I. Khuimphukhieo, J. Landivar Scott, O. Fernandez, M. Starek, J.L. Foster Malone, J. Da Silva, H. Li, & M. Bhandari. High-Throughput Estimation of Above-Ground Biomass of Energycane Using Aerial RGB Images. ASA-CSSA-SSSA International Annual Meeting, November 2024, San Antonio, TX (oral).
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Ghansah, B., Bhandari, M., Scott, J. L., Starek, M., Foster Malone, J. L., & Landivar, J. (2024) Very High-Resolution Crop Canopy Height Estimation Using Icesat-2, Skysat Images and Deep Convolutional Neural Network (CNN) [Abstract]. ASA, CSSA, SSSA International Annual Meeting, San Antonio, TX.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Jorge Da Silva (2024). Production of Biomass for Bioenergy from Feedstock System with Positive Water Footprint (invited speaker, Oral) at the Brazilian Bioenergy Science and Technology Conference (BBEST) and International Energy Agency (IEA) 2024 Conference from October 22 to 24, 2024, at the Renaissance Hotel Convention Center in S�o Paulo, Brazil.
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Progress 03/01/23 to 02/29/24
Outputs
Target Audience:
Nothing Reported
Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?PhD student training: 1. Benjamin Ghansah started his PhD in Computer and Geospatial Science inthe Fall of 2023 at Texas A&M University-Corpus Christi. How have the results been disseminated to communities of interest?Conference presentation Joaquin Haces-Garcia, Hua Li, Jorge da Silva, Jaime Foster, Mahendra Bhandari, Well-to-Tank Life Cycle Assessment of Various Energy Canes as Biomass Crops, INFORMS 2023 Annual Conference. What do you plan to do during the next reporting period to accomplish the goals?1. Finalizing UAS data processing pipeline: We plan to finalize the UAS data processing pipeline for RGB, multi-spectral, and Lidar sensors. (obj 1) Manuscript: Twomanuscripts will be publishedusing this pipeline to obtain canopy height and estimate biomass of energy crops for High-throughput phenotyping. 2. Identify top biomass-producing genotypes (by using all the data collected in year 1 at Weslaco)and establish an experiment at the Corpus Christi location (experiment 2) by inter-cropping cool season leguminous crops. (obj 2) 3. Develop the protocol to measure greenhouse gas measurements and perform environmental impact analysis (obj 3) 4.Develop research-based learning modules for different levels of students:The project team will visit a few more high schools to establish a collaboration for sharing the learning modules with high school teachers. Additionally, in the summer of 2024, the project team will introduce fundamental concepts and knowledge to undergraduate students and K-12 teachers through a Research and Extension Experiences for Undergraduates (REEU) program and a Professional Development for Agricultural Literacy (PDAL) funded by USDA at Texas A&M University-Kingsville. (obj 4) 5. Results will be presented in several national and international conferences.
Impacts
What was accomplished under these goals?
This is the project's first year, and we focused on establishing the experiment during this project period. Following are the details on the progress made: Overall experimental setup: Experimental site:The firstexperiment to evaluate the genotypes for drought tolerance and development of Unmanned Aerial Systems (UAS)-based phenotyping techniques has been established at the Texas A&M AgriLife Research Center at Weslaco (Figure 1). Figure 1. Experimental site Soil sample collection: Before planting, 18 soil samples to 15 cm depth were taken with a foot pedal probe with 2.5 cm diameter and composited to three samples (six in each composite) that were sent to the Texas A&M AgriLife Extension Soil, Water, and Forage Testing Laboratory, College Station, TX. Soils were typical of the calcareous soils of the region with moderate alkalinity (8 pH) with an average of 41 ppm nitrate-N, 32 ppm P, and 443 ppm K. An additional seven samples were taken to 20 cm depth with a 5 cm diameter soil probe to determine bulk density which averaged 1.13 g/cm3. On October 3, 2023, a local NRCS scientist assisted us with deep core sampling at the site. Five samples were collected with a truck probe in each irrigation treatment (dryland, ½, and full irrigation). The probe was 4.1 cm diameter and samples were separated into 10 cm depth increments to facilitate baseline data integration into various crop models and life cycle analyses. The goal was to reach 80 cm, but due to drought, the majority of samples were to 60 cm. One sample was used for bulk density analysis which averaged 1.7 g/cm3 for the top 30 cm. The remaining samples were split to determine soil moisture and sent to Ward Laboratories, Inc. (Kearney, NE) for analyses of pH, electrical conductivity, soil fertility parameters, total N, and organic and total carbon. Figure 2. Soil sampling crew with help from NRCS MLRA Survey Leader Mr. Gary Harris Energy cane genotypes and experimental design: Six energy cane genotypes selected from the Texas A&M AgriLife energy cane germplasm improvement program were planted on August 29, 2023, along with switchgrass and seeded perennial sorghum (n = 5) (Table 1). The field was divided into three blocks for irrigation treatments based on evapotranspiration (ET) needs (100% ET, 50% ET, and rainfed). The experimental design is a completely randomized split-plot design with four replications, with irrigation as a main plot and genotype as a subplot. Plots are 4 rows × 10 m long. Six GoField Plus systems (Goanna Ag. Pvt. Ltd., Queensland Australia) are placed across irrigation treatments. The GoField Plus system consists of a 1 m deep capacitance probe that provides soil moisture and temperature data in 10 cm increments and a single-pixel infrared canopy temperature sensor. Data was reported every hour through a web application along with continuous weather data, growing day degrees, and modeled ET. Based on these measurements, irrigation events as well as the amount to apply are determined. So far, we have not been able to irrigate our crop because of continuous rainfall and low temperatures in the experimental site. Two irrigation events were made right after planting for germination Figure 3. Drip lines for irrigation and GoFieldPlus Systems Table 1: Planted materials along with experimental set-up. Soil moisture sensors: Thirty-three one-meter-deep capacitance probes that provide soil moisture and temperature data in 10 cm increments were installed (one replication in 100% ET, one replication in 50% ET, and two replications in rainfed) to continuously monitor the soil water use. The data are reported using the WoodPecker System webpage and we are currently developing a program to automatically summarize the data atplot level. Figure 4. Soil moisture sensors are placed across the field along with the data visualization webpage. Objective 1: Develop remote sensing-based phenotyping techniques to assess drought tolerance rapidly and non-destructively (in progress) Unmanned Aerial Systems (UAS) data collection and validation: UAS platforms equipped with red-green-blue (RGB), multi-spectral (MS), and LIDAR sensors are being flown weekly to bi-weekly intervals to generate multi-temporal data on crop growth and development. UAS data collection and feature extraction protocols developed by our team for other crops have been successfully tested for energy cane. Additionally, we explored the utility of UAS-LiDAR for phenotyping energy cane crops. Figure 5: Unmanned Aerial Vehicle (UAV) along with different sensors used to collect data Preliminary results from the UAS data: Simple statistical analysis showed a strong correlation between modeled crop height from UAS-LiDAR and field-measured crop height (r= 0.73, p< 0.05, n=48) and biomass (r= 0.83, p< 0.05, n=48), providing empirical relation from which biomass of energy cane could be estimated. While our results provide initial insights, we continue to explore how other features such as vegetation density from UAS-LiDAR can influence the biomass of energy cane. Thermal imagery data will be collected once irrigation differentiation is initiated and plants are under stress. Figure 6:Relationship between LiDAR-derived height, biomass, and field-measured height and biomass Objective 3: Measure the environmental impacts of the monoculture feedstock biomass system compared to the legume integrated biomass system using a life cycle analysis of carbon and water footprint. (in progress, the focus will be on years 2 and 3) Create the life cycle inventory database for the two biomass product systems: The team chose OpenLCA as the life cycle analysis (LCA) software since it has open-source databases related to agriculture activities. The LCA framework has been studied and created in OpenLCA. The list of data needed for conducting the LCA studies has been identified. Compared to the previous data collection activities, we will have enough data to conduct the LCA analysis. To test the LCA framework and the functionality of OpenLCA, the project team used data from a previous project to conduct a Well-to-Tank LCA study on energy cane. The results were published as a peer-reviewed abstract and presented at the INFORMS 2023 Annual Conference. Assess the environmental impacts of the two biomass product systems and provide necessary interpretation for stakeholders: The project team has conducted a thorough literature review to identify possible databases, especially life cycle inventory databases, to be used. Objective 4: Train the next-generation bio economy workforce (students and farmers) through an integrated education curriculum (in progress) Develop research-based learning modules for different levels of students: The project team is developing initial learning modules related to drone and life cycle analysis and will test them with K-12 teachers in two summer research programs at Texas A&M University-Kingsville, which will have up to 26 K-12 teachers involved in Summer 2024. Increase farmer's and students' awareness and knowledge of biomass and bioenergy by implementing learning modules in classrooms and webinars: The project team has visited one middle school to showcase the use of drones in agriculture fields.
Publications
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