Progress 08/01/23 to 07/31/24
Outputs
Target Audience:The target audience for this reporting period was graduate students (from both AG Science and Engineering departments) at the Texas State University to conduct the research (Developing an AI-based algorithm to estimate the Soil Organic Matter - SOM) and developing 3 course modules on 1- Drones and their applications in precision agriculture, 2- Artificial Intelligence, 3- The application of Sensor fusion and AI in precision agriculture, Undergraduate student teachers from AG science department to deliver the developed curriculum/modules to high school students, and senior engineering undergraduate students (working on the technical projects related smart farm program) Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?The program provided a range of opportunities for training and professional development, including: Research Opportunities: The Smart Farm program created research opportunities in smart agriculture for graduate students, allowing them to collaborate on innovative projects alongside expert faculty. This involvement has given them practical experience in advanced agricultural research. Hands-On Experiences: Underrepresented graduate and undergraduate students gained valuable hands-on experience through this program, working directly with advanced agricultural tools and technologies such as soil sensors and drones. This on-farm experience provided them access to research resources and practical skills essential for careers in agriculture and engineering. Additionally, high school students participated in hands-on drone activities, learning to capture aerial images, which introduced them to emerging technologies in agriculture. Skill Enhancement through Course Modules: Three specialized course modules were developed to expand the skill set of undergraduate students. Focusing on precision agriculture, these modules provide students with hands-on experience in advanced technology applications, building their expertise in the field. Technical Writing and Publishing Training: Graduate students received targeted training in technical writing, enabling them to communicate their research findings effectively. This training also supports their ability to publish peer-reviewed articles in reputable scientific journals, which contributes to their academic and professional profiles. Cross-Disciplinary Collaboration: The program fostered collaboration across engineering, engineering technology, and agricultural departments, enhancing professional development for both students and faculty. By working with experts across disciplines, participants gained exposure to diverse perspectives and expertise. Mentorship Program: A structured mentorship framework supports students at various academic levels. In its first year, a faculty mentorship structure was established for underrepresented graduate students, with the program directors providing one-on-one guidance. A peer-to-peer and faculty mentorship model was also established for undergraduate students and expanded to include high school students in the second year. How have the results been disseminated to communities of interest?In the second year, the Smart Farm program has continued disseminating results to communities of interest through following channels: Website Development: The program's dedicated website (https://smartfarm.wp.txstate.edu/) has been maintained and updated to include all the active members and share the links for course modules. Scientific Publications: Research findings from the first and second years have been documented in two scientific articles. One peer-reviewed paper, titled "Application of Artificial Intelligence and Sensor Fusion for Soil Organic Matter Prediction," has been published in 2024, while the second article is currently under review. What do you plan to do during the next reporting period to accomplish the goals?To accomplish the goals during the next reporting period, the following actions will be taken: Enhanced Undergraduate Student Engagement: Building on our initial outreach, we plan to enhance our efforts to attract more underrepresented undergraduate students. We plan to expand scholarship offerings and establish mentorship programs connecting current students with incoming students. Strengthening Outreach Activities: Following the success of previous outreach initiatives, we will train more undergraduate students to enhance their outreach skills. We will also improve the presentations and hands-on activities for secondary school students based on the received feedback. Course Module Improvements: Using participant feedback from previous modules, we will refine course content and teaching methods to further enhance student engagement and learning outcomes. Senior Design Project Diversification: We will continue introducing agricultural-related design projects that reflect current industry trends and technologies, allowing senior-year students to tackle real-world challenges.
Impacts
What was accomplished under these goals?
In the second year of the program, the team continued advancing both the technical and educational components of the smart farm program. Building on the foundation from the first year, the team expanded training and professional growth opportunities, emphasizing experiential learning through hands-on activities, the creation of course modules, technical writing, interdisciplinary collaboration, and mentorship. The program continued to support four graduate students (1- Jordan Sherrell, 2- Kenedy Kornegay, 3- Sarah Parks), who improved and expanded the machine learning-based algorithm designed to estimate SOM using data from planting sensors and drones. Additionally, the educational modules and videos developed in the first year of the project have been further improved, and more materials have been added to the modules. The main topics covered in these modules include drone applications in Precision Agriculture (PA), the role of planting sensors and drones in PA, and AI applications in smart farming. Furthermore, capstone projects were introduced to senior engineering students, aiming to enhance the wireless transmission of planting sensor data to a central base. To summarize, the list of accomplished goals is as follows: Goal 1: Create an Educational Pipeline for Underrepresented Students Improvement of Undergraduate Students' Skills: Undergraduate students Joseph Nino and Freddy Jimenez, trained through this program, visited high school classes to teach the modules, guiding students through hands-on activities and demonstrating how to operate drones for capturing aerial images. This experience allowed the undergraduate students to practice near-peer mentorship. Undergraduate students of a senior design course offered in the engineering technology department participated in interdisciplinary projects and gained valuable hands-on experience that bridged theory and practical applications. This approach strengthened their problem-solving abilities and technical skills. Improvement of Graduate Students' Skills: Graduate students, supported by the Smart Farm program, have made significant notable progress in their problem-solving, critical thinking, scientific reasoning, presentation skills, and technical writing. This support has equipped them with the essential skills necessary for successful careers in precision agriculture and other related fields. Publication and Presentation Opportunities: The work of graduate students supported by the Smart Farm program has led to the publication of a peer-reviewed article in scientific journals, and a second journal article has been submitted and is currently under review. Goal 2: Develop Peer & Near Peer and Faculty Mentorship Structure for Underrepresented Students Peer & Near-Peer for Undergraduate Students: Undergraduate students supported by the Smart Farm program, all from underrepresented backgrounds (Hispanic students) received training to mentor high school students and their peers in activities focused on smart agriculture. This mentorship initiative empowers them to share knowledge and practical skills in innovative agricultural practices. Through these interactions, they foster a deeper understanding of smart agriculture concepts among younger students and their colleagues. Peer-Near-Peer for Graduate Students: Graduate students supported by the Smart Farm program, all from underrepresented backgrounds engaged in mentorship by guiding undergraduate students and incoming graduate students who joined the program later. This collaborative environment fostered strong connections between graduate and undergraduate students, promoting knowledge sharing and skill development. As a result, both groups benefited from diverse perspectives, enhancing their understanding of smart agriculture and building a supportive community focused on innovation and growth. Faculty Mentorship: Faculty members play a crucial role in supporting underrepresented students by providing guidance and resources throughout their academic journeys. Through regular one-on-one meetings, faculty effectively mentor both graduate and undergraduate students, helping them navigate challenges and achieve success in their academic pursuits. Goal 3: Create Multi-Disciplinary Course Modules and Experiential Learning Opportunities Course Modules Development: Course Modules and Experiential Learning Opportunities In the first year, we developed three specialized course modules covering topics such data analysis, Artificial Intelligence (AI), the application differenet sensors, sensor fusion and AI in precision agricluture, and applications of drones in precision agriculture. Building on that foundation, we have now improved and enhanced the content in year two, ensuring that these modules provide undergraduate students with even greater hands-on experience and expertise, further aligning with the program's goal of fostering practical knowledge and skill development. Capstone Design Projects: Senior engineering technology students worked on design and development projects focused on advanced agricultural technologies as part of their capstone courses. This hands-on experience bridges theoretical knowledge with real-world applications, strengthening their problem-solving abilities and technical expertise. Goal 4: Recruit Talented Undergraduate and Graduate Students Graduate Student Recruitment and Training: The program successfully recruited and trained three talented graduate students: 1- Jordan Sherrell, 2- Kenedy Kornegay, 3- Sarah Parks, all from underrepresented backgrounds in engineering and agricultural science to work on research and mentorship activities within the program. Undergraduate Student Recruitment and Training: The program successfully recruited and trained 2 undergraduate students, Joseph Nino and Freddy Jimenez, all from underrepresented backgrounds in agricultural science. These students actively contribute to the project's completion and mentor high school students.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2024
Citation:
Uddin, Md Jasim, Jordan Sherrell, Anahita Emami, and Meysam Khaleghian. "Application of Artificial Intelligence and Sensor Fusion for Soil Organic Matter Prediction." Sensors 24, no. 7 (2024): 2357.
- Type:
Journal Articles
Status:
Under Review
Year Published:
2024
Citation:
Rosales, A., Uddin, M. J., Sherrell, J. M., Emami, A., & Khaleghian, S. "Application of Artificial Intelligence and UAV on Precision Agriculture: A Comprehensive Review"
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Progress 08/01/22 to 07/31/23
Outputs
Target Audience:The target audience for this reporting period was graduate students (from both AG Science and Engineering departments) at the Texas State University to conduct the research (Developing an AI-based algorithm to estimatetheSoil Organic Matter - SOM) and developing 3 course modules on 1- Drones and their applications in precision agriculture, 2- Artificial Intelligence, 3- Sensor fusion and senior engineering undergraduate students (working on the technical projects related smart farm program) Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?The program provided several opportunities for training and professional development as follows: Research Opportunities: The smart farm program provided research opportunities in smart agriculture for graduate students where they collaborate on cutting-edge projects, gaining practical experience and working with expert faculty. Hands-On Experiences: The smart farm program provided hands-on experience for underrepresented students and access to research resources. Students involved in this program engaged in working with advanced tools and technologies such as soil sensors and drones on the farm, preparing them for successful careers in agriculture and engineering. Skill Enhancement Course Modules: Three specialized course modules have been designed and developed to enhance the skill set of undergraduate students. These modules focus on the practical application of advanced technology in precision agriculture, equipping students with hands-on experience and expertise. Technical Writing and Publishing: Graduate students received training in technical writing which equipped them with the necessary skills to effectively communicate their research findings and contributes to their ability to publish peer-reviewed articles in reputable scientific journals. Cross-Disciplinary Collaboration: The smart farm program has encouraged collaborative research efforts among departments of engineering, engineering technology, and agriculture, fostering the professional development of students and faculty members involved in this program through interaction with experts in the field. Mentorship Program: The program has established a robust mentorship structure to guide students at different academic levels. In its first year, a faculty mentorship structure was initiated for underrepresented graduate students, with the PD team offering one-on-one guidance and mentorship. Furthermore, a peer-to-peer and faculty mentorship structure has been implemented for undergraduate students, which will expand in the second year to include secondary students. This mentorship approach enhancedthe professional development of all participants, creating a supportive environment for learning and growth. How have the results been disseminated to communities of interest?In the first year of the project, the results of smart farm program have been disseminated through the following approaches: Website Development: The Smart Farm program has created a dedicated website at https://smartfarm.wp.txstate.edu/ to promote the program and disseminate its results. This platform serves as a hub for updates, research highlights, and educational content, ensuring ongoing information sharing throughout the project. This approach effectively reaches a broad online audience, encouraging engagement and discussions on topics related to smart farm project. Scientific Publications and Journals: The results and research findings collected in the first year have been documented in two scientific articles and submitted to publishers for dissemination through peer-reviewed scientific publications and journals. Although these articles are technically oriented, they play a crucial role in advancing academic discourse and serve as a valuable resource for those seeking in-depth scientific insights.? What do you plan to do during the next reporting period to accomplish the goals?To accomplish the goals during the next reporting period, the following actions will be taken:? Undergraduate Students Engagement: The PD team will engage more undergraduate students in smart program offering experiential learning opportunities related to advanced technology applications in precision agriculture to a larger group of underrepresented students. This will involve the identification of talented underrepresented undergraduate students and actively encourage them to participate in the program by offering scholarships. Updates on Website: The project will focus on adding the developed modules (1- drones and their applications on PA 2- the application of planting sensors and drones in PA 3- AI) , latest results and findings to the project website, ensuring that online audiences have access to the most up-to-date information regarding research and outcomes. Outreach Activities: Undergraduate students will be trained to conduct outreach activities in secondary schools to increase awareness and interest in science and technology among younger students, ultimately inspiring them to consider careers in STEM and agriculture. Continued Agricultural-related Senior Design Projects: The project will continue offering agricultural-related design projects to senior-year students in the engineering technology department. This hands-on approach to learning reinforces the connection between theory and practical applications, fostering students' problem-solving skills and technical competence. Course Module Improvements: Course modules will be improved and updated based on feedback received from participants. This iterative process ensures that educational content remains relevant and effective, aligning with the evolving needs and expectations of the students.
Impacts
What was accomplished under these goals?
During the first year of the program, the team worked on both technical and educational aspects of the proposed smart farm program. The program provided several opportunities for training and professional development, with a focus on experiential learning, hands-on experiences, course module development, technical writing, cross-disciplinary collaboration, and mentorship.A total number of 4 graduate students (1- Jordan Sherrell, 2- Meridith Keith, 3- Kenedy Kornegay, 4- Sarah Parks) were supported through smart farm program and worked under the direct supervision of the PD teamto develop a machine learning-based algorithm to estimate the SOM using the data from the planting sensors and drones, also to develop three course modules/educational videoson 1- drones and their applications on PA 2- the application of planting sensors and drones in PA 3- AI. Additionally, projects on different aspects of "developing an array of planting sensors that send the planting sensors' data to a base wirelessly" were defined for senior engineering undergrad students (as their capstone project). To summarize, the list of accomplished goals is as follows: Goal 1: Create an Educational Pipeline for Underrepresented Students Improvement of Undergraduate Students' Skills: Undergraduate students in a senior design course offered in the engineering technology department got involved in cross cross-disciplinary project that offered hands-on experience to reinforce learning the connection between theory and practical applications, fostering students' problem-solving skills and technical competence. Improvement of Graduate Students' Skills: Graduate students, supported by the Smart Farm program, made substantial advancements in their problem-solving, critical thinking, scientific approach, presentation skills, and technical writing. This development equips them with the essential skills needed for successful careers in precision agriculture and beyond. Publication and Presentation Opportunities: Graduate students conducted cutting-edge research, leading to the creation of peer-reviewed articles in scientific journals. This dissemination of research findings contributes to academic discourse and enhances their professional development. Goal 2: Develop Peer-Near-Peer and Faculty Mentorship Structure for Underrepresented Students Mentoring Undergraduate Students: Undergraduate students participating in a senior design course within the engineering technology department engaged in cross-disciplinary projects. These projects provided hands-on experience, reinforcing the connection between theoretical knowledge and practical applications. This approach facilitated the development of students' problem-solving skills and technical competence. Mentoring Graduate Students: Graduate students, under the guidance of the Smart Farm program, experienced significant advancements in their problem-solving, critical thinking, scientific approach, presentation skills, and technical writing. These skills are pivotal in preparing them for successful careers in precision agriculture and related fields. Professional Development: The program actively improved the professional development skills of underrepresented students by involving them in various project activities. This engagement allowed students to gain practical experience, fostering their growth and learning within the project's supportive Goal 3: Create Multi-Disciplinary Course Modules and Experiential Learning Opportunities Course Modules Development: Three specialized course modules focusing on the applications of drones, planting sensors, data analysis, and Artificial Intelligence (AI) in precision agriculture were developed. These modules provide undergraduate students with hands-on experience and expertise, aligning with the program's goal to foster practical knowledge and skill development. Capstone Design Projects: Senior year engineering technology students engaged in design and development projects related to advanced agricultural technologies as part of their capstone courses. This practical experience connects theoretical knowledge to real-world applications, further enhancing problem-solving skills and technical competence. Goal 4: Recruit Talented Undergraduate and Graduate Students Graduate Student Recruitment and Training: The program successfully recruited and trained four talented graduate students from underrepresented groups with backgrounds in engineering or agricultural science. They have been engaged in extensive research work through their master's theses, preparing them for future careers in the food and agriculture sectors.
Publications
- Type:
Journal Articles
Status:
Under Review
Year Published:
2023
Citation:
Uddin, M. J., Sherrell, J. M., Emami, A., & Khaleghian, S. "Application of Artificial Intelligence and UAV on Precision Agriculture: A Comprehensive Review".International Journal of Modern Agriculture. (Submitted /Under Review).
- Type:
Journal Articles
Status:
Under Review
Year Published:
2023
Citation:
Uddin, M. J., Sherrell, J. M., Emami, A., & Khaleghian, S. "Integrating AI and Sensor Fusion for Smart Agriculture". Electronics. (Submitted /Under Review).
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