Progress 05/01/24 to 04/30/25
Outputs
Target Audience:1. Faculty, researchers, farm managers, graduate, and undergraduate students at Florida A&M University, the University of Nebraska-Lincoln, and Purdue University. 2. Researchers, stakeholders, and the general public through the ASABE international annual conference, the Florida Wine and Grape Growers Association annual conference, and the 4th annual AI in Agriculture conference. 3. Tallahassee State College STEM major students. 4. General public via the 2025 FAMU day at the Florida Capitol. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?Hands-On Technical Training: Participants, including students and researchers, engaged in practical training on installing and maintaining IoT sensors, data networks, and monitoring systems. Activities included setting up canopy cameras, sensor networks, and a weather station--providing hands-on experience with advanced agricultural technologies. Data Management and Analytics: The project leveraged the PLAN online portal (https://phrec-irrigation.com/#/f/68/dash) for aggregating and visualizing real-time data, offering participants training in managing and analyzing sensor-driven information. This helped them develop skills in using digital tools for data-informed vineyard management. Curriculum Enhancement: The project supported curriculum enrichment at FAMU by integrating modules on precision engineering systems and data analytics into the Biological Systems Engineering program. This effort helped align academic instruction with modern technological advances in agriculture. Experiential Learning and Internships: Internship placements and research opportunities at the FAMU Center for Viticulture and Small Fruit Research provided students with real-world experience, connecting classroom learning with practical applications in digital and precision agriculture. Professional Engagement and Networking: Participants presented their work and networked with professionals at events such as the Annual FAMU Grape Harvest Festival and various scientific conferences at national and international levels, gaining exposure and feedback from the wider agricultural community. Extension and Community Outreach: The project prioritized outreach to audiences, including African American farmers, by promoting digital agriculture and plant phenotyping technologies through interactive demonstrations and extension programming. How have the results been disseminated to communities of interest?1. Interactive PLAN Website (https://phrec-irrigation.com/#/f/68/dash): An interactive website was developed to provide real-time access to sensor data and analytical results. 2. FAMU day at the Florida Capitol: the project team has organized demonstration exhibitions on precision engineering systems of near-realtime monitoring for muscadine grape vineyards, providingdemonstrations of digital agriculture and plant phenotyping technologies. During this event, stakeholders were able to interact with project team members, ask questions, and provide feedback on the technologies being implemented. 3. Presentations and Conferences: Conference presentations included detailed explanations of the sensor technologies, data collection methods, preliminary results from the test sites, and students' experiential learning experiences. 4. EngagedAfrican American students in experiential learning experiences through internships and research opportunities.The project provided an opportunity for our undergrad and grad students to acquire experiences in onsite leaf sampling, imaging, data processing, modeling and reporting. The students also had communication skills during teamwork and collaborations with vineyard managers. 5. Integrated project findings into course modules and classroom instruction at FAMU. What do you plan to do during the next reporting period to accomplish the goals?1. Develop deep-learning algorithms for automatic data collection and analysis, enabling precise irrigation and disease management decisions. 2. Collect additional imaging data during the 2025 growing season. We will also continue image processing and modeling efforts with the new images, with the hope of improving the disease detection accuracy. We will continue our outreach to growers and other potential users of the technology and seek more opportunities to apply and test the technologies in practical cases.
Impacts
What was accomplished under these goals?
Objective 1 focuses on adapting cutting-edge plant phenotyping technology into an affordable tool for muscadine vineyards. This innovation will expedite grape leaf diseases identification, subsequently deploying AI-driven models for disease detection, led by Purdue and FAMU. A library of images of single grape leaves, both diseased and healthy, from three varieties (Floriana, Noble, and A-27). The leaf images include VNIR hyperspectral images, 6 bands visible+NIR multispectral images, RGB images, and microscope images. Images were processed and classification models to differentiate between diseased and healthy leaves were developed. Notably, a new spatial-spectral image processing algorithm was developed to process the multispectral images and generated >20 spatial-spectral features related to the disease. These features were used to successfully classify between the diseased and healthy leaves. The random forest model achieved 93.3% classification accuracy rate with the first year's images: As a side project (not included in the proposal), we also acquired microscope images with Purdue's newest handheld microscope scanner which can quickly focus and take an up to 400X microscope image within 5-10 seconds. We were able to clearly see pathogen infection spots in the microscope images, much earlier before the spot is visible to human eyes. Objective 2 revolves around establishing a digital agriculture IoT pilot, integrating advanced irrigation scheduling and disease sensing in muscadine vineyards. Spearheaded by UNL and FAMU, this project will demonstrate precision water management and disease monitoring. Research activities: Deployment of IoT Sensors and Network Communications: (1) Installed 9 setups of canopy cameras and sensor arrays, including soil water probes, rain gauges, RH, temperature, and leaf wetness sensors, on three varieties of grapevines in 2024 and at the start of March 2025. (2) Installed a weather station at the research center. Data Analysis A comprehensive environmental monitoring system was deployed at the FAMU vineyard to capture high-resolution data on climate and soil conditions influencing muscadine grape growth. Sensors measured variables including air temperature, relative humidity, solar radiation, precipitation, wind speed/direction, soil temperature, soil moisture, and electrical conductivity (EC) at multiple depths (1 ft, 2 ft, and 3 ft). These variables were continuously logged throughout the 2024 growing season to evaluate how environmental fluctuations affect plant development and yield variability. This multi-layered dataset formed the foundation for assessing vineyard microclimate conditions and soil-plant interactions across nine plots with differing vine cultivars and soil textures. a. Soil Moisture and EC Profile Analysis Data from SENTEK probes (10-60 cm) were analyzed to examine soil moisture and electrical conductivity (EC) variability across muscadine grape cultivars. Analysis focused on the 0-1 ft (10-30 cm) depth, where muscadine roots are concentrated. Soil moisture at 0-1 ft showed marked temporal and spatial variability in response to irrigation and rainfall, with moisture declining rapidly after peaks, indicating active evapotranspiration and drainage. Noble plots (np4, np6) retained more moisture--likely due to finer-textured soils--while A-27 (a27p17) and Floriana (flp4) had drier profiles, likely reflecting sandier textures. This shallow soil layer is critical for water management due to the grapevines' shallow roots. Soil EC at 0-1 ft exhibited seasonal increases, peaking mid-summer, with plot np6 consistently showing the highest EC--attributed to its clay content and higher salt retention. In contrast, Floriana (flp2) and A-27 (a27p17) maintained low EC levels, indicating better drainage. EC fluctuations often followed drying cycles, with spikes reflecting salt concentration. These patterns illustrate how soil texture influences root zone salinity and nutrient mobility. b. Air Temperature, RH, and Solar Radiation Seasonal environmental data showed peak temperatures in June-July and reduced relative humidity during this time. Growing degree days (GDD) were calculated per vine post to track heat accumulation and phenological stages. Accumulated GDD ranged from 2002 to 2038, with budbreak GDD between 107 and 180, allowing cultivar comparisons. Solar radiation data helped track canopy photosynthesis and vine vigor. c. Soil Texture Data Analysis Soil samples (0-1 ft) were collected on 10/18/2024 from nine vine posts. Samples were analyzed at Waters Agricultural Laboratories for texture and nitrogen content. Floriana plots (P2, P4, P6) ranged from loamy sand to sandy clay loam, with nitrogen increasing from ~20 to 50 lbs/a. Noble plots (P2, P4, P6) were mainly sandy loam with consistent nitrogen (~50-55 lbs/a). A27 posts (P15, P16, P17) were loamy sand with rising nitrogen from ~35 to 58 lbs/a--the highest level recorded. The data emphasize how soil texture affects nitrogen retention and leaching, underscoring the importance of site-specific practices in precision viticulture. d. Canopy Cover and Image-Based Analysis Ground-based camera systems tracked canopy development in 2024. Images were processed to segment vegetation and quantify canopy dynamics. Data analysis is ongoing. Objective 3 entails curriculum enhancement and experiential learning forAfrican American students in AI/ML applications. (1)Provided undergraduate and graduate students with hands-on training in precision agriculture technologies through internships and research opportunities at the FAMU Biological Systems Engineering and FAMU Center for Viticulture andSmall Fruit Research. (2)Developed and implemented course modules on precision engineering systems and data analytics for FAMU's Biological Systems Engineering program. Objective 4 is dedicated to promoting AI/ML technologies among underserved farmers and stakeholders through innovative Extension programs. Initiated the showcase facility for digital agriculture and plant phenotyping technologies at the FAMU research vineyard, which will serve as a hub for engaging outreach activities, linking research with events like the FAMU Grape Harvest Festival.
Publications
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2025
Citation:
Jingqiu Chen, Wei-zhen Liang, Xin Qiao, Violeta Tsolova. Growing Precision Engineering Systems for Muscadine Grape Vineyards: Research, teaching, and Extension outcomes. The 2025 AI in Agriculture & Natural Resources Conference hosted by Mississippi State University, Starkville, MS. March 31- April 2, 2025.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2025
Citation:
Jingqiu Chen, Wei-zhen Liang, Xin Qiao, Violeta Tsolova, Jian Jin. Growing Precision Engineering Systems and Developing Early Disease Detection Devices for Muscadine Grape Vineyards to Aid Climate-smart Agriculture. 2025 Florida Wine & Grape Growers Association (FWGGA) Annual Conference, Deland, FL, Jan. 16-17, 2025.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2025
Citation:
Shomar Bullen; Wei-zhen Liang, Violeta Tsolova, Jingqiu Chen. Integrating Internet of Things (IoT) Sensor Network Time-Series Data to Improve Vineyard Operational Efficiency. The 2025 AI in Agriculture & Natural Resources Conference hosted by Mississippi State University, Starkville, MS. March 31, 2025
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2024
Citation:
Jingqiu Chen, Wei-zhen Liang, Xin Qiao, Violeta Tsolova. Developing a Peer Learning Agricultural Network (PLAN) for Muscadine Grape Vineyards in Southeastern U.S. 2024 Florida Section American Society of Agricultural and Biological Engineers (ASABE) Annual Conference, Jensen Beach, FL, Jun. 12-15, 2024.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2025
Citation:
Ryan E. Henry, Jingqiu Chen. Engineering Design of Bird Spikes for Rain Gauges Protection at Muscadine Vineyard. The FAMU Undergraduate Research Symposium, April 10, 2025. Tallahassee, FL.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2025
Citation:
Shomar Bullen; Wei-zhen Liang, Violeta Tsolova, Jingqiu Chen. Integrating Time-series Agroclimatic Data to Improve Vineyard Operational Efficiency. The 2025 FAMU Graduate Poster Competition. March 27, 2025. Tallahassee, FL.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2024
Citation:
Shomar K. Bullen; Wei-zhen Liang; Violeta M. Tsolova; Jingqiu Chen. Improving Muscadine Vineyard Productivity Operations Using Remote Sensing & Agroclimatic Data for Yield Estimation. The 2024 Florida Classic Mini Research Showcase. November 22, 2024, Orlando, FL.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2024
Citation:
Shomar K. Bullen; Wei-zhen Liang; Violeta M. Tsolova; Jingqiu Chen. Improving Muscadine Vineyard Productivity Operations Using Remote Sensing & Agroclimatic Data for Yield Estimation. The 2024 FAMU Student Research Forum. October 30, 2024, Tallahassee, FL. (1st Place of Graduate Poster Presentation Competition, Engineering/Technology Research Discipline)
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2024
Citation:
Shomar Bullen, Wei-zhen Liang, Violeta Tsolova, Jingqiu Chen. Experiential Learning on Digital Agriculture and Image Analysis using Machine Learning Techniques. 2024 American Society of Agricultural and Biological Engineers (ASABE) Annual International Meeting, July 28-31, 2024, Anaheim, CA.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2024
Citation:
Shomar Bullen, Wei-zhen Liang, Violeta Tsolova, Jingqiu Chen. Experiential Learning on Digital Agriculture and Image Analysis using Machine Learning Techniques. 2024 Florida Section American Society of Agricultural and Biological Engineers (ASABE) Annual Conference, Jensen Beach, FL, Jun. 12-15, 2024.
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