| Affiliations: | Aggie Research Mentoring Program |
| Team Leader: | Bismark Anokye
Soil and Crop Sciences |
| Faculty Mentor: | Bagavathiannan Muthukumar, Ph.D |
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Meeting Times:
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TBA |
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Team Size:
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3 |
| Open Spots: | 0 |
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Special Opportunities:
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🔹 Hands-on Experience – Work with thermal imaging technology to detect crop stress before visible symptoms appear.
🔹 Skill Development – Gain experience in remote sensing, image analysis, and data interpretation with potential exposure to AI applications in agriculture. 🔹 Research & Publication – Outstanding contributions may lead to co-authorship on research papers and conference presentations. 🔹 Networking & Career Growth – Opportunities to connect with experts in agronomy, crop science, and agricultural technology. 🔹 Pathway to Research & Industry – Gain valuable experience for graduate studies, internships, or careers in agriculture, remote sensing, and data science. This is a great opportunity to learn, contribute, and grow in a cutting-edge research project! |
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Team Needs:
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We welcome undergraduate students from all backgrounds who are eager to learn and contribute to this project. No prior experience is required—just curiosity and a willingness to collaborate! Preferred (but not mandatory) skills: ✅ Interest in plant science or agronomy – helpful for understanding crop responses to herbicides. ✅ Basic knowledge of sensors or imaging – experience with thermal cameras, remote sensing, or image analysis is a plus. ✅ Programming & Data Analysis – familiarity with Python, R, or MATLAB for processing thermal images is useful but not required. ✅ Machine Learning or AI Interest – an advantage for students looking to explore AI applications in agriculture. ✅ Attention to detail & problem-solving skills – key for analyzing and interpreting results. We offer a supportive, hands-on learning experience, so whether you’re interested in agriculture, imaging, or technology, this project provides an excellent opportunity to gain new skills and contribute to innovative research! |
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Description:
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Herbicide injury can severely impact cotton yield, but early detection before visible symptoms appear remains a challenge. This project utilizes thermal infrared imaging and thermography techniques to identify herbicide-induced stress in cotton at its earliest stages. By detecting temperature variations in plant tissues, our system aims to capture subtle physiological changes before symptoms become visible, enabling timely intervention to prevent yield loss. Through the use of advanced thermal sensors and imaging analysis, this research will help distinguish different herbicide modes of action and provide insights into optimizing herbicide application. The findings will contribute to reducing crop losses, improving herbicide management practices, and enhancing early stress detection in cotton production. |