Progress 05/01/23 to 04/30/24
Outputs
Target Audience:We reached underserved farmers, extension agents, university undergraduate and graduate students, K-12 students, and the general public during the field day and field demonstrations organized as part of PVAMU land grand program outreach activities. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?We have been training several undergraduate and graduate studentsand enrichedAgricultural and Computer Science curricula with AI-UAS-Ag content with support from partners. We also conducted several training and hands-on exercises for k-12 students as part of field day and field demonstration activities. How have the results been disseminated to communities of interest?We also conducted several demonstrations and hands-on exercises for farmers, extension agents, university students, and k-12 students as part of field day and field demonstration activities. What do you plan to do during the next reporting period to accomplish the goals?We will report on all the project objectives as we are still processing data connected during the past crop production season.
Impacts
What was accomplished under these goals?
We established a field research experiment where wecollected data on crop nutrients and water stress using multi-scale sensors and remote sensing platforms. Collected data were processed, and analyzedtargetingcrop nutrient and water stress. We started building hierarchical AI-based multi-scale crop water and nutrient stress models. We organized and participated in several outreach activities conducted by the college, including field days and field demonstrations. We have been training several undergraduate and graduate students and enrichedAgricultural and Computer Science curricula with AI-UAS-Ag content with support from partners
Publications
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2024
Citation:
R Awal, A Veetil, A Fares, B Thapa, A Elhassan, A Rahman. 2024. Monitoring of Sorghum Fields Using Multispectral and Thermal Imagery from Truss-Mounted Sensors.
ASA, CSSA, SSSA International Annual Meeting
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2023
Citation:
R Awal, A Veettil, A Rahman, A Fares, B Thapa, ND Melaku, A El Hassan.2023.
Machine Learning-Based Soil CO2 Emissions Prediction From Climate-Smart Agricultural Systems Enhanced by Organic Amendments. AGU Fall Meeting Abstracts 2023 (857), GC51P-0857
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2023
Citation:
R Awal, A Veetil, A Rahman, A Fares
Predicting Crop Yield of Major Crops Using Machine Learning Techniques in Northern High Plains, Texas. ASA, CSSA, SSSA International Annual Meeting.
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2023
Citation:
B Thapa, A Fares, R Awal, A Veetil, A Rahman, A Elhassan. 2023. Sorghum Growth and Yield in Response to Irrigation and Nitrogen Levels. ASA, CSSA, SSSA International Annual Meeting
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Progress 05/01/22 to 04/30/23
Outputs
Target Audience:Students from k-12, undergraduates, and graduates. Farmers and growers also benefitted from this project. Changes/Problems:We did not substantially change our experiments, data collection, or analysis. We are pleased with our research plan. What opportunities for training and professional development has the project provided?One postdoctoral researcher, two undergraduate students, and three graduate students received training in installing, collecting, and analyzing various research components. The training included measuring chlorophyll content, Leaf Area Index (LAI), soil moisture, and soil CO2 emissions; collecting weather parameters, soil samples, and biomass; and conducting data analysis. Additionally, the students had the opportunity to present their research findings at local and regional conferences, showcasing their work and gaining valuable experience in scientific communication. How have the results been disseminated to communities of interest?We organized field demonstrations and extended invitations to farmers, growers, and researchers from PVAMU and TAMU. This event effectively showcased the significance of artificial intelligence and related technologies in modern agriculture. By demonstrating real-world applications and benefits, we highlighted how AI tools can enhance farming practices, improve crop management, and drive innovation in the agricultural sector. The successful event fostered valuable interactions and discussions, reinforcing the impact of technology on advancing agricultural practices. What do you plan to do during the next reporting period to accomplish the goals?We plan to have a second growing season to repeat the same experimental setup, collect similar data, publish project findings, organize field demonstrations for our audience, and train students and junior researchers on AI-related techniques.
Impacts
What was accomplished under these goals?
Goal 1: Analyzed Crop Nutrients and Water Use We set up an experiment with sorghum at the PVAMU research farm to understand how different levels of irrigation and nitrogen affected crop performance. We examined how the crops grew, their nitrogen levels, their yield, and their water use efficiency. Our study included four irrigation treatments (no irrigation, 75%, 100%, and 125% of the crop's water needs) and three nitrogen levels (half the recommended amount, the full amount, and double the recommended amount). We established 48 plots for this study and used various sensors to collect data on these factors. Goal 2: Utilized Drones to Monitor Crops We acquired drones to capture aerial images of our crops. These images helped us assess how well the crops handled nutrient and water stress, providing valuable insights for improving crop management. Goal 3: Developed AI Models for Crop Stress We worked on creating advanced AI models to predict crop stress related to water and nutrient levels. This goal was successfully addressed as we made significant progress in building these sophisticated tools. Goal 4: Shared Insights with Farmers We hosted a field day at the PVAMU research farm to demonstrate how AI could enhance agricultural practices. This event was designed to share our findings and show how technology could benefit farmers, particularly those with limited resources. Goal 5: Enhanced Education with AI and Drone Technology and Training Future Professionals We enriched our Agricultural and Computer Science curricula with content related to AI and drone technology. This involved collaborating with external partners to provide students with valuable, hands-on learning experiences. This initiative was successfully implemented. One postdoctoral researcher, two undergraduate students, and three graduate students received training to install, collect, and analyze various research components. The training included: (a)Chlorophyll Content:We measured the nitrogen content in plant leaves using a SPAD meter. This tool provided accurate readings of chlorophyll levels, which correlate with nitrogen concentration. (b)Leaf Area Index (LAI):The LAI, which quantifies the amount of leaf material in the canopy, was measured using an LAI-2200 meter. This non-destructive tool allowed us to gauge leaf density effectively. (c)Weather Parameters:Weather conditions, including solar radiation, temperature, relative humidity, and precipitation, were recorded every 15 minutes using a weather station installed in the experimental plot. (d)Soil Moisture:We utilized 10HS soil moisture sensors to measure moisture levels at two depths (5 cm and 35 cm) from each plot, ensuring comprehensive data on soil water content. (e)Soil CO2 Emissions:Soil CO2 emissions were measured across all plots using an LI-8100A survey system. Measurements were taken from a standardized plot area using a 20 cm survey chamber collar to ensure consistency. (f)Soil Sample Collection:Soil samples were collected for nutrient analysis to assess soil fertility and nutrient availability. (g)Biomass Harvest:Sorghum biomass, including both shoot and root systems, was harvested from each plot to evaluate overall plant growth and yield.
Publications
- Type:
Websites
Status:
Published
Year Published:
2023
Citation:
Prairie View sponsored Agricultural Field Days for exhibitors, youth, and farmers on July 14, 2023. https://x.com/i/status/1679909925105946625.
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