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

Abstract

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.

Introduction

Machine learning is increasingly important in agriculture for improving seed quality assessment and predicting germination rates, which are crucial for optimizing crop yields. The GermiScan project uses machine learning and real-time image analysis to predict seed germination by examining seed characteristics like size and color. A diverse image dataset was used to train and test the model, showing promising initial results that can help farmers make better planting decisions. However, further refinement is needed to improve performance across more seed types and real-world conditions.

The literature review highlights previous research on machine learning and deep learning techniques for seed germination and classification, noting challenges such as limited dataset diversity, computational demands, lack of real-time processing, and difficulties in applying models broadly in agriculture.

The methodology for GermiScan involves collecting diverse seed images, preprocessing with augmentation, and fine-tuning a pre-trained CNN model using transfer learning. The system was trained and tested on an 80/20 split and developed into a real-time scanning tool with a user-friendly dashboard. Continuous improvements focus on expanding the dataset and enhancing the model’s adaptability to various seed types, aiming to provide a scalable, practical solution for real-time germination prediction to support sustainable farming.

Conclusion

In conclusion,this study emphasizes the significance of seed germination prediction using machine learning techniques, particularly with real-time image analysis. While the initial model achieved basic functionality, the need for further improvements was evident. By implementing and refining this approach, we aim to enhance the accuracy of seed germination predictions and contribute towards more efficient agricultural practices through a user-friendly system.

References

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Copyright

Copyright © 2025 Madhura Deshpande, Rituja More, Ruchita Ahire, Vivek Chopade, Prof. Supriya Jagtap. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.