| 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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Joining this project offers unique learning experiences and professional growth opportunities, including:
🔹 Hands-on AI & Machine Learning Experience – Gain practical skills in deep learning, digital image processing, and data analysis. 🔹 Interdisciplinary Research Exposure – Work at the intersection of computer vision, agriculture, and artificial intelligence, contributing to real-world applications in precision agriculture. 🔹 Potential Co-Authorship on Research Publications – Outstanding contributions may lead to co-authorship in academic journals or conference proceedings, boosting your research profile. 🔹 Networking & Conference Attendance – Exceptional team members may have the opportunity to present findings at conferences related to AI, agriculture, or precision farming. 🔹 Skill Development – Learn Python programming, deep learning frameworks (TensorFlow/PyTorch), and image processing techniques, applicable in various tech and research careers. 🔹 Career & Research Pathway Support – Gain mentorship and guidance on pursuing graduate studies, internships, or industry roles in AI, agriculture, or data science. This project provides a collaborative and supportive environment where students from various backgrounds can explore AI-driven agricultural solutions while developing valuable technical and research skills. |
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Team Needs:
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We welcome motivated undergraduate students from diverse backgrounds who are interested in applying AI, digital imaging, and precision agriculture to real-world problems. No prior expertise is required—just a willingness to learn and collaborate! Preferred Skills (Not Mandatory): ✅ Basic programming knowledge (Python, R, or MATLAB) – helpful for working with machine learning models. ✅ Interest in digital image processing – exposure to tools like OpenCV, ImageJ, or related software is a plus. ✅ Familiarity with machine learning or AI concepts – students eager to learn about deep learning applications are encouraged. ✅ Background in agriculture, biology, or environmental science – helpful for understanding seed classification. ✅ Data analysis and statistics – useful for model evaluation and interpretation of results. What We Offer: 🔹 Hands-on experience with AI and digital imaging techniques 🔹 Exposure to real-world agricultural challenges 🔹 Opportunities to contribute to a research publication or presentation 🔹 A collaborative and supportive learning environment We encourage students from all disciplines to apply, whether they have a background in computer science, engineering, agriculture, or life sciences. If you’re interested in AI, digital imaging, or sustainable farming solutions, we’d love to have you on the team! |
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Description:
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Seed purity is essential for agricultural productivity, yet the presence of weed seeds in crop seed mixtures poses a significant challenge to farmers and seed industries. This project leverages AI-powered digital image processing and deep learning algorithms to detect and classify weed seeds within crop seed mixtures accurately. By utilizing high-resolution imaging and machine learning techniques, the system can automatically distinguish between crop and weed seeds, improving seed quality assessment and sorting efficiency. This research has practical applications in precision agriculture, seed certification, and automated sorting technologies, reducing manual labor while enhancing seed purity and farm productivity. The integration of artificial intelligence and computer vision ensures a fast, scalable, and cost-effective solution for seed purity analysis, benefiting farmers, agronomists, and seed processing industries. |