INTEGRATING DATA FROM GROUND MEASUREMENTS, UNMANNED AERIAL VEHICLES, AND MODELING TO QUANTIFY PLOT SCALE EVAPOTRANSPIRATION

• Sponsoring Institution: National Institute of Food and Agriculture
• Project Status: ACTIVE
• Funding Source: AFRI COMPETITIVE GRANT
• Reporting Frequency: Annual
• Accession No.: 1023316
• Grant No.: 2020-67021-31965
• Cumulative Award Amt.: $374,999.00
• Proposal No.: 2019-06412
• Project Start Date: Jul 1, 2020
• Project End Date: Jun 30, 2026
• Grant Year: 2020
• Program Code: [A1521]- Agricultural Engineering

Recipient Organization
UNIVERSITY OF FLORIDA
G022 MCCARTY HALL
GAINESVILLE,FL 32611

Performing Department
IFAS, Ag & Bio Engineering

Non Technical Summary
Due to population growth and scarcity of natural resources such as freshwater, agriculture, the largest consumer of freshwater, needs to adopt more efficient irrigation scheduling technologies that not only allow maximum water conservation but also improve water quality. Irrigation and nutrient management go hand in hand. Outcomes of improved irrigation management included improved nutrient management and thus water quality. In this regard, the development of improved irrigation technologies has resulted in significant water savings. However, any data-driven technology will be only as good as the input information that it relies on. For example, the effectiveness of weather-based irrigation scheduling methods depends on the availability of accurate climate data, including evapotranspiration,at optimal spatial and temporal resolutions. We propose to develop a method that allows estimating evapotranspiration from agricultural fields. Such information will be critical for implementing precession water management practices at plot and field scales. In addition, results will also be used to fine-tune crop and climate models that in turn can be used to guide policy and management decisions.

Animal Health Component

40%

Research Effort Categories

Basic

50%

Applied

40%

Developmental

10%

Classification

Knowledge Area (KA)	Subject of Investigation (SOI)	Field of Science (FOS)	Percent
402	0210	2020	30%
102	1411	2050	10%
102	1480	2050	10%
111	0210	2050	50%

Knowledge Area
102 - Soil, Plant, Water, Nutrient Relationships; 111 - Conservation and Efficient Use of Water; 402 - Engineering Systems and Equipment;

Subject Of Investigation
1480 - Sweetcorn; 0210 - Water resources; 1411 - Beans (fresh, fresh-processed);

Field Of Science
2020 - Engineering; 2050 - Hydrology;

Keywords

agriculture

drone

evapotranspiration

irrigation

precision water management

thermal infrared

Goals / Objectives
The long-term goal of this project is to develop effective strategies for the implementation of precision irrigation management practices that will not only conserve freshwater resources and increase crop productivity but also improve water and environmental qualities. Our overall objective is to develop a method that will be used for estimating field level actual evapotranspiration rates and crop water stress levels, which would allow the implementation of precision irrigation management practices at different scales. Calculating ET at a field scale will provide critical information that would have a wide range of applications in weather, climate, and hydrologic modeling and water resource management, especially for irrigated agriculture. This project brings together a diverse team of multi-disciplinary scientists, with the expertise to lead the project and address its objectives. The overall objective of the project will be attained by pursuing the following four specific objectives:Specific Objective #1: Quantify actual ET and crop water stress for green beans and sweet corn by integrating data from canopy temperature measurements and modeling.Specific Objective #2: Improve water use efficiency for green beans and sweet corn using crop simulation models.Specific Objective #3: Develop irrigation scheduling methods for green beans and sweet corn based on measured ET.

Project Methods
The execution of this project requires multiple approaches, which reflecta team of scientists with different expertise involved in this project. There are three major but interconnected approaches (1) Field Experiment (2) Data Analytics and Developing Artificial Intelligence (AI) Algorithm, and (3) Hydrologic and Crop Modeling. These approaches are described below. (1) Field experiment: The project will be conducted at the Tropical Research and Education Center (TREC) of the University of Florida. Thirty-two experimental plots will be established. On these plots, green beans and sweet corn will be grown. The experimental setup will involve four irrigation treatments with four replications. The experiment will employ a linear move irrigation system with Variable Rate Irrigation (VRI) and remote control and troubleshooting capabilities that allow precision irrigation applications at plot scales. However, in case, the linear move irrigation machine is not available for use due to unforeseen reasons, a drip irrigation system will be installed on all experimental plots. Once the experimental plots are set up, the following data will be collected: (i) soil physical and hydrologic properties: before the start of the experiment during the first year, soil physical and hydrological properties will be determined soil bulk density, total porosity, particle size distribution, organic matter, saturated hydraulic conductivity, and moisture release curves; (ii) soil moisture and recharge dynamics: Soil moisture dynamics within the root zone will be continuously measured using soil moisture sensors connected with data loggers. Drainage lysimeters will be installed to collect percolating water below the rootzone. A weather station will be installed at the experimental site to monitor weather parameters. (iii): Thermal and multispectral imaging: drone-based thermal and multispectral images will be continuously acquired from experimental plots of the two crops. A DJI Matrice M210 v2 drone (SZ DJI Technology Co., Ltd., China), equipped with a 13 mm Zenmuse XT radiometric thermal camera (FLIR Systems Inc., USA), Micasense RedEdge-M multispectral camera will be used for image accusation. Also, stationary infrared thermocouples will be used to monitor diurnal temperature dynamics and verify temperature readings from drone imaging; (iv): Leaf water potential and stomatal conductance: Leaf water potential will be once a week during the growth period of each crop. In addition, stomatal conductance will be measured. Stomatal conductance and leaf water potential measurements will be done on the same leaves; (v): Crop phenology and growth: Leaf chlorophyll content will be monitored using SPAD 502 Chlorophyll Meter (Spectrum Technologies, Inc.). At the end of the crop growing season, fresh and dry crop yield and yield components will be measured for each plot and treatment by harvesting the entire plot except two outer rows to account for border effects. Plant samples will be collected for vegetative biomass and marketable yield measurements. Samples will be weighed for fresh weight. In addition, crop parameters (e.g., height, leaf area index, etc.) will be manually measured once a week. (2) Data Analytics and Developing Artificial Intelligence (AI) Algorithm: (i) Time-series data analysis: this involves the analysis of crop, weather and soil hydrologic time series data using appropriate data analysis techniques; (ii) Image processing: multispectral and thermal images collected from experimental plots will be processed using a suite of image processing tools and relevant crop indices will be generated such as crop water stress index (CWSI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Soil Adjusted Vegetation Index (SAVI), etc. (iii) Developing an Artificial Intelligence (AI) Algorithm: An Artificial Intelligence (AI) based algorithm will be developed to automatically calculate canopy temperatures from the UAV-based thermal images. An AI-based technique will be developed in this task to automatically distinguish plant canopy cover from soil and weeds and calculate plant canopy temperatures. (3): Hydrologic and Crop Modeling; (i) Modeling evapotranspiration: evapotranspiration rates will be calculated using three approaches: water balance, crop water stress index, and energy balance. The water balance approach is one of the most widely used methods to estimate evapotranspiration rates at different scales. On the other hand, the CWSI (crop water stress index) approach developed by Jackson et al. (1981) reported that evapotranspiration can be estimated based on crop water stress index and potential evapotranspiration rate. The third approach will test a suite of energy balance models such as the Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) (Allen et al., 2007), Surface Energy Balance System (SEBS) (Su, 2002), Surface Energy Balance Algorithm for Land (SEBAL) (Bastiaanssen et al., 1998), and the Operational Simplified Surface Energy Balance (SSEBop) (Senay, 2018; Singh and Senay, 2016). (ii) Hydrologic modeling: In this study, we will evaluate and fine-tune hydrologic models at plot and field scales to simulate major components of the water balance such as evapotranspiration rates, soil moisture dynamics, and recharge. Not only will we test the models but also will assess their performances and applicability under south Florida conditions. A suite of field scale hydrologic models exist that can account for soil water content and its dynamic (e.g., Hydrus 1D (Simunek et al., 2016); APEX - Agricultural Policy/Environmental eXtender Model (Williams et al., 2010); CREAMS/GLEAMS (Knisel and Douglas-Mankin, 2012; Rekolainen et al., 2000). A field-scale hydrologic model will be calibrated to root zone soil moisture dynamics, recharge rates, and evapotranspiration estimates calculated using the three approaches discussed above. Our modeling efforts will primarily focus on field-scale applications of hydrologic models to implement efficient water management efforts in Florida. Once the hydrologic models have been evaluated for local field conditions, they will be used to simulate the effects of different crop and land management scenarios on soil hydrologic properties, and nutrient and chemical transport at plot and field scales. (iii) Crop modeling for water use efficiency: The Decision Support System for Agrotechnology Transfer (DSSAT v4.7.5; www.DSSAT.net) software (Hoogenboom et al., 2019) will be used to simulate crop and water. The Cropping System Model (CSM) of DSSAT model simulates crop growth and development on a daily time-step as a function of local weather and soil conditions, CROPGRO genetics, and management (Jones et al., 2003). The CSM-CROPGRO model for green bean and CSM-CERES model for sweet corn will be calibrated with the local experimental data. Following the evaluation of the models, they will be used to develop best management practices for sweet corn and green bean using long-term historical weather data, local soil profile characteristics, and different irrigation and fertilizer management inputs.

Progress 07/01/23 to 06/30/24

Outputs
Target Audience:The target audience for our research during the reporting period encompasses researchers, scientists, graduate students, extension agents, vegetable growers, and allied industries. Changes/Problems:We have been using a DJI M210 drone equipped with customized thermal and multispectral sensors for the first three years. However, with the new Florida law that took effect in January 2024, state and local government agencies are prohibited from using DJI drones. The law mandates the use of drones from an approved list of manufacturers, excluding DJI, which led to funding challenges for our team. With support from the University of Florida, we were able to acquire a drone approved for use in the state. However, the process of securing funding and purchasing the new drone took time, resulting in no drone imagery being collected during the 2024 cropping season. Additionally, rising labor costs, which are now significantly higher than at the start of the project, have presented further challenges. What opportunities for training and professional development has the project provided?This project has been instrumental in providing valuable training and professional development opportunities. Two graduate students (one Ph.D. and one M.S.) have been actively conducting research aligned with the project's objectives, making significant contributions to its goals. Additionally, a research coordinator and a postdoctoral research associate involved in the project have received training in multidisciplinary concepts through one-on-one mentorship and regular interactions with the project team. Several researchers and OPS personnel in the lab have gained essential skills and training. Many lab members have participated in writing scientific abstracts and manuscripts and presenting oral and poster sessions. Graduate students, postdoctoral associates, and other lab researchers have led several refereed publications. The Ph.D. student has authored three refereed publications based on project data, all published in the highly reputable Computers and Electronics in Agriculture journal. The M.S. student has led a manuscript that is currently under review in SoftwareX. Using data from this project, the project team has also received training in hydrologic and crop simulation models. Currently, four refereed publications are under review in various journals. How have the results been disseminated to communities of interest?Our project team has actively shared its findings with a wide range of communities, including through six presentations at scientific conferences such as the American Society of Agricultural and Biological Engineers (ASABE) International Annual Meeting, the ASABE Florida Section, and the AI in Agriculture Conference, among others. Although this project is entirely research-based, we have disseminated findings through various extension activities. These included field days and workshops designed to engage high school students and showcase precision irrigation and advanced irrigation and agricultural technologies. What do you plan to do during the next reporting period to accomplish the goals?The field experiment has been completed, but several key project deliverables remain to be finalized this year. A critical focus is the improvement of the app, which is currently under development. The integration of machine learning models is a major task still pending. The app is intended to streamline drone imagery processing, data analysis, visualization, and modeling to support decision-making in agricultural water management. A PhD student, currently in their second year of study, is working on this project.

Impacts
What was accomplished under these goals? Over the past four years, we conducted a comprehensive field experiment involving 32 plots within a variable-rate irrigation system. Sweet corn and green beans were planted in 16 plots each, with four different irrigation treatments applied: full irrigation (FI) and three deficit irrigation levels at 75%, 50%, and 25% of FI. Irrigation was scheduled using the Maximum Allowable Depletion (MAD) strategy. Data collection included continuous root zone soil moisture monitoring with sensors connected to data loggers and measuring deep percolation using drainage lysimeters. Diurnal canopy temperature dynamics were monitored using 24 stationary infrared thermocouples (IRT) installed on the fourth row of each plot. The sensor heights were regularly adjusted to maintain a minimal distance from the plant canopy and avoid soil temperature interference. These factory-calibrated IRT sensors, accurate to within +/- 0.5 ?C, continuously recorded canopy and air temperatures at 1-minute intervals, with a measurement range of up to 50?C. We measured various plant parameters throughout the growing season, including leaf water potential, stomatal conductance, plant height, canopy width, leaf area, chlorophyll content, fresh and dry biomass, and crop yield. Plant height and biomass were assessed bi-weekly to capture growth stages, with five height measurements and four biomass collections. Plants were harvested just above the soil surface, and fresh and dry biomass was recorded, along with marketable and non-marketable yields. This multifaceted approach has provided deep insights into the interactions between irrigation practices and crop performance, informing precision water management and field-scale evapotranspiration modeling strategies. Additionally, weather parameters were monitored throughout the growing season using multiple weather stations at the experimental site.

Publications

• Type: Journal Articles Status: Published Year Published: 2024 Citation: Teshome, F.T., H.K. Bayabil, B. Schaffer, Y. Ampatzidis, G., and G. Hoogenboom. 2024. Improving soil moisture prediction with deep learning and machine learning models. Computers and Electronics in Agriculture 224. 109414. https://doi.org/10.1016/j.compag.2024.109414
• Type: Journal Articles Status: Published Year Published: 2024 Citation: Teshome, F.T.*, H.K. Bayabil, B. Schaffer, G. Hoogenboom, Y. Ampatzidis, and A. Singh. 2024. Simulating soil hydrologic dynamics using crop growth and machine learning models. Computers and Electronics in Agriculture 224. 109186. https://doi.org/10.1016/j.compag.2024.109186.
• Type: Conference Papers and Presentations Status: Accepted Year Published: 2024 Citation: Bayabil, H.K. 2024. Use of Ground and UAV-Based Sensors for Agriculture. AI in Agrotechnology Workshop. Colar Gables, FL. September 3, 2024. Invited talk.
• Type: Conference Papers and Presentations Status: Accepted Year Published: 2024 Citation: Hussain, S., F. Teshome, B. Tulu, G. Awoke, and H. K. Bayabil. 2024. Levering Artificial Intelligence and UAV Images-based Vegetation Indices for Leaf Area Index (LAI) Estimation. Florida Section, American Society of Agricultural and Biological Engineers (FLASABE), Annual Meeting. Jensen Beach, FL. June 12-15, 2024. Oral presentation.
• Type: Conference Papers and Presentations Status: Accepted Year Published: 2024 Citation: Tulu, B., Bayabil, H.K., and Ampatzidis, Y. 2024. Streamlining Precision Irrigation: A Decision Support Tool. 9th Water Institute Symposium Annual Meeting. Gainesville, Florida. Feb 20-21, 2024. Oral presentation.
• Type: Conference Papers and Presentations Status: Accepted Year Published: 2024 Citation: Tulu, B., Bayabil, H.K., and Ampatzidis, Y. 2024. Enhancing Agricultural Water Management: A Desktop Application Integrating UAV Imagery and Ground Sensing for Precision Irrigation. AI in Agriculture and Natural Resources Conference and Annual Meeting. Texas A&M University, College Station, Texas. April 15-17, 2024. Oral presentation.

Progress 07/01/22 to 06/30/23

Outputs
Target Audience:The target audience for our research during the reporting period encompasses researchers, scientists, graduate students, extension agents, vegetable growers, and allied industries. Changes/Problems:The ongoing labor shortage, exacerbated by recent minimum wage increases and inflation in Florida, posed challenges for our project as the initial budget allocation for OPS personnel was at an hourly rate of $8.46. Consequently, we had to adjust and allocate additional resources to secure reliable OPS personnel. Overall, budget limitations have become a growing challenge in accomplishing all project tasks effectively. Frequent rainfall events continued to present significant challenges throughout this project year. Given the project's multiple irrigation treatments, establishing treatment effects became a formidable task due to frequent rainfalls. Moreover, the occurrence of multiple equipment and sensor failures resulted in minor delays and gaps in data collection. What opportunities for training and professional development has the project provided?This project has been instrumental in providing valuable training and professional development opportunities. A Ph.D. student is actively conducting research aligned with the project's objectives, contributing significantly to its goals. Additionally, a research coordinator directly engaged in the project has received training in multidisciplinary concepts through one-on-one mentorship and regular interactions with the project team. Multiple researchers and OPS personnel in the lab have had the opportunity to acquire essential training and skills. The graduate student and research coordinator have actively participated in scientific abstract manuscript writing and oral and poster presentations. Notably, the project team collectively contributed to 12 conference abstracts and presentations during this reporting period alone. Moreover, the Ph.D. student has achieved notable publications, serving as a lead author in a highly reputable journal (Computers and Electronics in Agriculture) and having another manuscript accepted for publication in the Agricultural Water Management Journal. The project team has also received training in various hydrologic and crop simulation models, including APEX, Hydrus-1D, and DSSAT, utilizing data collected through this project. Currently, the Ph.D. student is in the process of developing two peer-reviewed manuscripts for journal publications. How have the results been disseminated to communities of interest?Our project has been actively engaged in sharing its findings with diverse communities of interest. The project team members collectively delivered a total of 12 presentations at various conferences and meetings, including the American Society of Agricultural and Biological Engineers (ASABE) International Annual Meeting, the ASABE Florida Section, and the AI in Agriculture Conference, among others. Despite not directly involving extension activities, the project conducted several field days and workshops aimed at high school students to showcase precision irrigation and advanced irrigation and agricultural technologies. What do you plan to do during the next reporting period to accomplish the goals?As we enter the final year of this four-year project, we plan to build upon our achievements and further our research goals. We will replicate the field experiments, planting green beans and sweet corn on existing plots equipped with a linear move variable-rate irrigation system. Data collection will encompass time series data for root zone soil moisture, deep percolation, leaf canopy temperature, and weather data. To ensure comprehensive data collection, we will utilize a combination of sensors, drones, equipment, and manual methods. Our aim is to submit a minimum of three peer-reviewed manuscripts for journal publication and present our project findings at a minimum of four conferences, including international, state, and local meetings. Additionally, we plan to conduct at least one field day to disseminate project findings to growers, extension agents, and students, fostering knowledge exchange and practical insights.

Impacts
What was accomplished under these goals? We executed an extensive field experiment involving 32 carefully designed plots within a variable-rate irrigation system for the last three consecutive years. Across these plots, we cultivated sweet corn and green beans, each occupying 16 plots. This comprehensive experiment comprised four irrigation treatments, replicated four times to ensure robust data collection. Our data collection efforts included continuously monitoring root zone soil moisture dynamics using soil moisture sensors connected to data loggers. Additionally, we measured deep percolation through drainage lysimeters installed beneath the root zone. To gain a comprehensive perspective of crop responses, we incorporated cutting-edge technology, such as daily drone images captured by thermal and multispectral sensors. Furthermore, we gathered diverse plant parameters throughout the cropping season, encompassing essential parameters such as leaf water potential, stomatal conductance, plant height, canopy width, leaf area, chlorophyll content, fresh and dry biomass, and crop yield. This multifaceted approach has allowed us to delve deeply into the intricate interactions between irrigation practices and crop performance, offering invaluable insights into precision water management practices and strategies to develop field-scale evapotranspiration modeling approaches using different techniques.

Publications

• Type: Journal Articles Status: Published Year Published: 2023 Citation: Teshome, F.T., H.K. Bayabil, B. Schaffer, G. Hoogenboom, Y. Ampatzidis, and A. Singh. 2023. Unmanned Aerial Vehicle (UAV) Imaging and Machine Learning Applications for Plant Phenotyping. Computers and Electronics in Agriculture 212. 108064. https://doi.org/10.1016/j.compag.2023.108064
• Type: Journal Articles Status: Published Year Published: 2023 Citation: Bayabil, H.K., F.T. Teshome, S. Guzman, and B. Schaffer. 2023. evapotranspiration rates of three sweet corn cultivars under different irrigation levels. HortTechnology 33(1) 16-26. https://doi.org/10.21273/HORTTECH05114-22.
• Type: Journal Articles Status: Published Year Published: 2023 Citation: Teshome, F.T.G, H.K. Bayabil, B. Schaffer, G. Hoogenboom, Y. Ampatzidis, and A. Singh. 2023. Exploring Deficit Irrigation as a Water Conservation Strategy: Insights from Field Experiments and Model Simulation. Agricultural Water Management 289. 108490. https://doi.org/10.1016/j.agwat.2023.108490.
• Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Bayabil, H.K. and N.S. Hilegaw. 2023. Developing Efficient Evapotranspiration Modeling Approaches for Sustainable Agricultural Water Management. Greater Everglades Ecosystem Restoration Conference. Coral Springs, Florida. April 17 - 20, 2023. Invited.
• Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Teshome, F.T. and H.K. Bayabil. 2023. Unmanned Aerial Vehicle (UAV)-based Imaging and Artificial Intelligence (AI) Models for Plant Phenotyping. AI in Agriculture Conference. Orlando, FL. April 17-19, 2023. Oral presentation.
• Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Teshome, F.T., H.K. Bayabil, N.S. Hailegnaw, and M. Berihun. 2023. Evaluating field scale hydrologic and crop simulation models in South Florida. Greater Everglades Ecosystem Restoration (GEER) Science Conference. Coral Springs, FL. April 17-20, 2023. Invited.
• Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Teshome, F.T., M. Berihun, N.S. Hailegnaw, and H.K. Bayabil. 2023. Predicting Root Zone Soil Moisture Using Machine Learning Models. Florida Section American Society of Agricultural and Biological Engineers (FLASABE) Annual Meeting. Duck Key, FL. June 4-6, 2023. Oral presentation.
• Type: Conference Papers and Presentations Status: Other Year Published: 2023 Citation: Teshome, F.T., M. Berihun, N.S. Hailegnaw, and H.K. Bayabil. 2023. Evaluating DSSAT Model Performance in Simulating Sweet Corn and Green Beans Growth Under Variable Irrigation Rates. Florida Section American Society of Agricultural and Biological Engineers (FLASABE) Annual Meeting. Duck Key, FL. June 4-6, 2023. Oral presentation.
• Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Teshome, F.T. and H.K. Bayabil. 2023. Application of Unmanned Aerial Vehicle (UAV)-Based Imaging and Machine Learning for Sweet Corn Plant Phenotyping. American Society of Agricultural and Biological Engineers (ASABE) Annual International Meeting. Omaha, NE. July 9-12, 2023. Oral presentation.
• Type: Conference Papers and Presentations Status: Accepted Year Published: 2023 Citation: Madni, S.S., F. Teshome, N.S. Hailegnaw, M. Berihun, M.S. Waqas, H.K. Bayabil. 2023. Precision Irrigation to improve water efficiency & agricultural productivity: Insights from Sensor Networks, UAVs, and IoT Platforms. Florida Section-American Society of Agricultural and Biological Engineers (FL-ASABE) Annual Meeting. Duck Key, FL. June 4 - 6, 2023. Oral presentation.
• Type: Conference Papers and Presentations Status: Accepted Year Published: 2023 Citation: Waqas, M.S., F. Teshome, S. Madni, N.S. Hailegnaw, M. Berihun, H.K. Bayabil. 2023. Applications of Infrared Sensors for Crop Water Stress Monitoring. Florida Section-American Society of Agricultural and Biological Engineers (FL-ASABE) Annual Meeting. Duck Key, FL. June 4 - 6, 2023. Oral presentation.
• Type: Conference Papers and Presentations Status: Accepted Year Published: 2023 Citation: Berihun, M., F. Teshome, N.S. Hailegnaw, and H.K. Bayabil. 2023. Simulating soil hydrology of irrigated fields using the APEX model. Florida Section-American Society of Agricultural and Biological Engineers (FL-ASABE) Annual Meeting. Duck Key, FL. June 4 - 6, 2023. Oral presentation.
• Type: Conference Papers and Presentations Status: Other Year Published: 2023 Citation: Bayabil, H.K., N.S. Hailegnaw, and F. Teshome 2022. Evaluating Different Evapotranspiration Modeling Approaches in Florida. ASA, CSSA, SSSA International Annual Meeting. Baltimore, MD. Virtual. Nov 6-9, 2022. Oral presentation.
• Type: Conference Papers and Presentations Status: Other Year Published: 2023 Citation: Bayabil, H.K. 2022. Machine Learning Applications for Water Resources Management: Challenges and Opportunities. AI for sustainable water resources management. Bahir Dar University, Ethiopia. Virtual. Oct 12, 2022.Invited.
• Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Teshome, F.T., H.K. Bayabil, N.S. Hailegnaw, and M. Berihun. 2023. Evaluating Hydrologic and Crop Simulation Models for Field-Level Evapotranspiration Modeling. American Society of Agricultural and Biological Engineers (ASABE) Annual International Meeting. Omaha, NE. July 9-12, 2023. Oral presentation.

Progress 07/01/21 to 06/30/22

Outputs
Target Audience:The target audience for our research during the reporting period includes researchers, scientists, graduate students, extension agents, vegetable growers, and allied industries. Changes/Problems:Recently the Florida legislature passed a law (section 934.50, Florida Statutes:https://www.dms.myflorida.com/business_operations/state_purchasing/approved_drone_manufacturers) that would ban flying drones that are not on an approved list. The approved list currently has only five manufacturers and the drone that is used for this project is not on the list. Unless changes are made, the law prohibits flying drones on the list starting from Jan 1, 2023. This will affect the project half of the experiment in 2022/2023 and the full experiment in the 2023/20224 project period.Due to funding issues, we won't be able to acquire a drone approved by the state. Due to the labor shortage following Florida's recent efforts to increase the minimum wage and inflation rate, the initial OPS budget at $8.46 hourly rate was not sufficient to attract skilled OPS personnel that could help with the project. As a result, we needed to pay more to get reliable OPS personnel. Overall, project budget limitation is becoming a challenge to accomplish all project tasks. As last year, rainfall was a also major challenge during this project year. The project involves different irrigation treatments, and it was a big challenge to establish treatment effects due to frequent rainfall events. Multiple failures of equipment and sensors were encountered. What opportunities for training and professional development has the project provided?A graduate student is conducting his Ph.D. dissertation research that will contribute to the objectives of this project. Another Ph.D. student is contributing to the project and is being trained in data collection and data processing skills related to crop modeling. Two research technicians and two OPS personnel are involved in the project and are trained in several multidisciplinary concepts through one-to-one mentorship and regular interactions with the project team. The graduate students and research technicians were involved in writing draft scientific abstracts, and factsheets, and giving oral and poster presentations. In total, the project team was involved in five conference abstracts and presentations. In addition, the graduate students were trained on different hydrologic and crop simulation models including APEX, Hydrus-1D, and DSSAT using data collected through this project. The students are currently developing peer-reviewed manuscripts for journal publications. How have the results been disseminated to communities of interest?A total of five presentations were given by faculty and students involved in this project including at the American Society of Agricultural and Biological Engineers (ASABE) International Annual Meeting, the Florida Snap bean conference, and other local and state meetings. Even though his project does not involve extension activities, two field days were conducted for vegetable growers, extension agents, and high school students. The potential of Variable Rate Irrigation for vegetable production in South Florida was discussed. Fifteen high school students and three teachers visited the field research project. In addition, twelve people attend the field day. What do you plan to do during the next reporting period to accomplish the goals?This is a four-year project and for next year (3rd year of the project), we plan to repeat the field experiments. Green beans and sweet corn will be planted on existing plots with a linear move variable rate irrigation system. Time series data collection will include root zone soil moisture, deep percolation, leaf canopy temperature, and weather data. Data collection will be performed using sensors, drones, using equipment, and manually. At least one peer-reviewed manuscript will be submitted for journal publication. In addition, at least four presentations will be presented at international, state, and local meetings. We also plan to conduct at least one field day to share project findings with growers, extension agents, and students.

Impacts
What was accomplished under these goals? A field experiment was conducted for a second year on 32 experimental plots established under a variable rate irrigation system. Sweet corn and green beans were planted on 16 plots each. The experiment involved four irrigation treatments replicated four times. Root zone soil moisture was monitored continuously using soil moisture sensors connected with data loggers. Deep percolation was measured using drainage lysimeters that were installed below the root zone. Drone images were collected daily using thermal and multispectral sensors. In addition, different plant parameters were collected throughout the cropping season. Parameters include leaf water potential, stomatal conductance, plant height, canopy width, leaf area, chlorophyll content, fresh and dry biomass, and yield.

Publications

• Type: Conference Papers and Presentations Status: Accepted Year Published: 2022 Citation: Bayabil, H.K., F.T. Teshome, F. Getachew. 2022. Effects of Irrigation Rate on Green Beans and Sweet Corn Yield and Yield Components. 2022 American Society of Agricultural and Biological Engineers (ASABE) Annual International Meeting. July 17-20, Houston, Texas.
• Type: Conference Papers and Presentations Status: Accepted Year Published: 2022 Citation: Bayabil, H.K., F.T. Teshome, C. Bartell. 2022. Potentials of Variable Rate Irrigation for Vegetable Production in South Florida. 8th University of Florida Water Institute Symposium. February 22-23, 2022. Gainesville, Florida. (invited)
• Type: Other Status: Published Year Published: 2022 Citation: Bayabil, H.K. 2022. Integrating Science and Technology for Efficient Use of Freshwater Resources. Certified Crop Advisors Course series. Online. (invited)
• Type: Other Status: Accepted Year Published: 2022 Citation: Bayabil, H.K., F.T. Teshome, C. Bartell, Fikadu Getachew. 2022. Potentials of Variable Rate Irrigation for Vegetable Production in South Florida. Vegetable Field Day. March 16, 2022. Homestead, Florida.
• Type: Conference Papers and Presentations Status: Accepted Year Published: 2022 Citation: Bayabil, H.K. Potentials of Variable Rate Irrigation With a Soil Moisture Sensor Feedback System For Snap Bean Production In South Florida. Florida Snap Bean Conference. May 06, 2022. Belle Glade, Florida. (invited)

Progress 07/01/20 to 06/30/21

Outputs
Target Audience:The target audience for our research during the reporting period includes researchers, scientists, graduate students, extension agents, and vegetable growers, and allied industry. Changes/Problems:The project start date (June 1, 2020) was in the midst of the COVID-19 pandemic. At the time, the Tropical Research and Education Center of the University of Florida, where the project is being conducted, was under lockdown. Installation of a linear move irrigation system with variable rate (VRI) technology, a critical infrastructure needed to start the project, was delayed by at least three months. These delayed the project delayed by several months. In addition, the start date for a Ph.D. student was delayed by one semester. Rainfall was a major challenge during the first year. The project involves four irrigation treatments, and it was a big challenge to establish treatment effects due to frequent rainfall events. One of the strategies the team identified was to conduct the field experiment during relatively dry months (November - February) based on long-term rainfall records. However, several rain events appeared during the first year that affected irrigation treatments. We expect that this could be an issue in the coming years. Equipment and sensors failure were also encountered during the first year. An M210 v2 DJI drone crashed mid-air during takeoff. This resulted in a week worth of UAV image data loss that was not collected due to the unavailability of a drone. Our efforts to acquire a backup drone were not successful due to a lack of funds. Battery and solar panel malfunctioning resulted in a loss of a week's worth of data from infrared canopy temperature sensors. Labor shortage and increases in minimum wages. Our requested budget in the proposal for OPS personnel was $8.46/hr. However, with the current labor market and following Florida's recent efforts to increase the minimum wage to $15/hr., it is becoming very difficult to get a reliable person who can help the team with the project. What opportunities for training and professional development has the project provided?One Ph.D. student is conducting his research on this project. A second Ph.D. student is being trained in data collection and processing skills related to crop model simulations tasks of the project. Two research technicians and one OPS personnel are involved in the project and are trained in several multidisciplinary concepts through one-to-one mentorship and regular interactions with the project team. The graduate students and research technicians were trained in writing scientific abstracts, factsheets, and give oral and poster presentations. The graduate students were involved in six conference abstracts and presentations and writing one factsheet. How have the results been disseminated to communities of interest?During this reporting period, a factsheet was accepted for publication anda total of seven abstracts and presentations were given by faculty and students involved in this project at different conferences including the American Society of Agricultural and Biological Engineers (ASABE and American Water Resources Association (AWRA). A Virtual field day was conducted for vegetable growers and extension agents and strategies for optimizing irrigation rates for beans and sweetcorn to improve plant health and yield were shared with participants. A total of 30 people attend the virtual field day. In addition, three field visits were arranged to faculty, extension agent, and graduate students to discuss the project. These field visits were arranged mostly for one audience at a time following the CDC guidelines for COVID-19. What do you plan to do during the next reporting period to accomplish the goals?Repeat the field experiment for green beans and sweet corn using exiting plots with a linear move variable rate irrigation system. Collect time series soil moisture and recharge dynamics, canopy temperature, and weather data. Acquire daily drone images using multispectral and thermal sensors. Process drone images using Pix4D software. Collect plant growth parameters including leaf water potential, stomatal conductance, plant height, canopy width, leaf area, chlorophyll content, fresh and dry biomass, and yield. Estimate evapotranspiration rates at plot scale using water balance and energy balance models.

Impacts
What was accomplished under these goals? Experimental data collection: A total of 32 experimental plots (5.5 m x 9.1 m with a 6.1m buffer between plots) were established on a 1-acre field under sweet corn and green beans. The field is equipped with a linear move irrigation system with a variable rate capability that allows conducting precision irrigation experiments at plots scales. In addition, plots are equipped with soil moisture, electrical conductivity, and temperature sensors, free drainage lysimeters, above canopy infrared sensors (IRT sensors), and a weather station. A variable-rate irrigation experiment was completed for one cropping season (November 2020 - February 2021) and several data on soil-water-plant parameters were collected. The experiment involved four irrigation treatments with four replications. Before the start of the experiment, soil samples were collected to determine baseline soil physical and hydrological parameters. Soil moisture and recharge dynamics within the root zone (0-20 cm) were monitored continuously using soil moisture sensors connected with data loggers. Drainage lysimeters were installed to collect percolating water below the root zone and estimate the recharge amount before irrigation events. UAV-based thermal and multispectral images were collected daily at solar noon except during inclement weather using a 13 mm Zenmuse XT thermal (FLIR Systems Inc., USA) and Micasense RedEdge-M multispectral sensors. In addition, several plant parameters were also collected throughout the cropping season. Parameters include leaf water potential, stomatal conductance, plant height, canopy width, leaf area, chlorophyll content, fresh biomass, and yield.

Publications

• Type: Conference Papers and Presentations Status: Other Year Published: 2021 Citation: Bayabil, H.K., F.T. Teshome, F.G. Welidehanna. 2021. Soil Moisture and Canopy Temperature Dynamics of Sweet Corn and Green Beans Under Variable Rate Irrigation. Florida Section American Society of Agricultural and Biological Engineers (FL ASABE) Annual Meeting. June 09-12, 2021. Daytona Beach Shores, FL.
• Type: Other Status: Under Review Year Published: 2021 Citation: Bartell, C., H.K. Bayabil, B. Schaffer, F.T. Teshome, and F. Getachew. 2021. Measuring Leaf Water Potential. University of Florida Institute of Food and Agricultural Sciences (UF/IFAS) Extension Factsheet.
• Type: Conference Papers and Presentations Status: Other Year Published: 2020 Citation: Bayabil, H.K. and F.T. Teshome, and B. Schaffer. 2020. Effects of Irrigation Level on Water Use and Yield of Sweet Corn Cultivars. American Water Resources Association (AWRA) Annual Water Resources Conference. Virtual. November 9-12, 2020.
• Type: Conference Papers and Presentations Status: Other Year Published: 2021 Citation: Bayabil, H.K. and F.T. Teshome. 2021. Canopy Temperature Dynamics of Sweet Corn and Green Beans Under A Variable Rate Irrigation System. American Society of Agricultural and Biological Engineers (ASABE) Annual Meeting. July 11 - 14, 2021. Virtual. Oral presentation.
• Type: Conference Papers and Presentations Status: Other Year Published: 2021 Citation: Teshome F.T., H.K. Bayabil, C. Bartell., and B. Schaffer. Comparison of a Pump-Up Chamber and a Pressure Bomb for Measuring Leaf Water Potential in Sweet Corn and Green Beans. American Society of Agricultural and Biological Engineers (ASABE) Annual International Meeting. July 11 - 14, 2021. Virtual. Oral presentation.
• Type: Conference Papers and Presentations Status: Other Year Published: 2021 Citation: Teshome F.T., H.K. Bayabil, C. Bartell, and B. Schaffer. Application of Unmanned Aerial Vehicles for Estimating Sweetcorn Height and Biomass. Florida Section American Society of Agricultural and Biological Engineers (FL ASABE) Annual International Meeting. June 9  12, 2021. Daytona Beach Shores, FL. Oral presentation.
• Type: Conference Papers and Presentations Status: Other Year Published: 2020 Citation: Teshome F.T., H.K. Bayabil, and F.G. Welidehanna. Evaporation Trends over South Florida. AWRA 2020 Annual Water Resources Conference, November 9-12, 2020. Virtual. Oral presentation.
• Type: Conference Papers and Presentations Status: Other Year Published: 2021 Citation: Teshome F.T., H.K. Bayabil, S.M. Guzman, and B. Schaffer. 2020. Evapotranspiration Rates of Three Sweet Corn Cultivars Under Different Irrigation Levels. Florida State Horticultural Soceity Annual Meeting. September 27-28, 2021. Daytona Beach, FL.