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Dev Day, held ahead of CS Expo 2026, focuses on real-world perspectives from industry speakers as they discuss current technologies, evolving trends, and the realities of working in today’s tech industry. It also opens conversations on the role of academe, industry, and government in supporting growth and innovation in the tech sector.
CS Expo Day showcases student projects evaluated by faculty and industry experts, with awards recognizing top innovations. The event also includes talks from tech leaders discussing current trends and insights in technology.
Farmulate: Optimizing Crop Rotation and Companion Planting Through XGBoost and YOLOv8n Algorithms Based on Soil Analysis
Farmulate: Optimizing Crop Rotation and Companion Planting Through XGBoost and YOLOv8n Algorithms Based on Soil Analysis
Group: BandWitt
Mentor: Mrs. Rain Marlyn Sanzchez
Members: Nate, Nikka C, Tomimbang, Margaret Anne C,, Victoria, Vea Vannez, Yago, Lyka Joie C
This study addresses the lack of simple and accessible tools for soil analysis, crop rotation, and companion planting, especially for small-scale and beginner farmers. Titled “Farmulate: Optimizing Crop Rotation and Companion Planting Through Machine Learning and Image Processing Algorithms Based on Soil Analysis,” it aims to help users choose suitable crops and planting strategies using machine learning and image processing. Designed for farmers, agricultural practitioners, and learners, the system analyzes soil images and sensor-based nutrient data to identify soil type and recommend appropriate crops and companion plants. It is expected to improve soil health, increase crop yield, and reduce the need for expensive laboratory soil testing, contributing to more sustainable and data-driven farming practices.
Farmulate: Optimizing Crop Rotation and Companion Planting Through XGBoost and YOLOv8n Algorithms Based on Soil Analysis
Group: BandWitt
Mentor: Mrs. Rain Marlyn Sanzchez
Members: Nate, Nikka C, Tomimbang, Margaret Anne C,, Victoria, Vea Vannez, Yago, Lyka Joie C
This study addresses the lack of simple and accessible tools for soil analysis, crop rotation, and companion planting, especially for small-scale and beginner farmers. Titled “Farmulate: Optimizing Crop Rotation and Companion Planting Through Machine Learning and Image Processing Algorithms Based on Soil Analysis,” it aims to help users choose suitable crops and planting strategies using machine learning and image processing. Designed for farmers, agricultural practitioners, and learners, the system analyzes soil images and sensor-based nutrient data to identify soil type and recommend appropriate crops and companion plants. It is expected to improve soil health, increase crop yield, and reduce the need for expensive laboratory soil testing, contributing to more sustainable and data-driven farming practices.
Farmulate: Optimizing Crop Rotation and Companion Planting Through XGBoost and YOLOv8n Algorithms Based on Soil Analysis