Real-Time Seed Scanning and Germination Prediction Using Machine Learning: The GermiScan Project

Accurate prediction of seed germination is critical for improving agricultural yield and resource management. This paper explores the use of machine learning to predict seed germination rates in real-time through image analysis. The project, named GermiScan, utilizes a camera-based system to capture seed images and predict germination based on physical features such as size and colour. A machine learning model was trained using labelled data to assess germination potential. The system was tested on multiple seed types, demonstrating promising results in predicting germination rates. Additionally, GermiScan's real-time scanning approach offers potential for large-scale agricultural applications, providing farmers with actionable insights to optimize planting strategies. The study also discusses challenges in model accuracy and suggests future work to enhance prediction capabilities by incorporating more seed varieties and refining the machine learning algorithm.