Rapid rice seed vigor assessment: A machine learning and deep learning framework with multi-time-point image analysis

Area of Science:

• Agricultural Science
• Computer Science
• Data Science

Background:

• Accurate rice seed vigor classification is crucial for agricultural productivity.
• Traditional methods are often labor-intensive and lack scalability.
• Image-based analysis offers a non-invasive and efficient alternative.

Purpose of the Study:

• To develop and compare machine learning and deep learning frameworks for rice seed vigor classification using RGB images.
• To evaluate the effectiveness of single-time-point versus multi-time-point image analysis strategies.
• To enhance model transparency and reliability through interpretability techniques.

Main Methods:

• Developed machine learning models using hand-crafted morphological and color features.
• Employed convolutional neural networks for automatic feature extraction.
• Investigated single-time-point and multi-time-point image analysis, including an ensemble approach.
• Applied interpretability techniques to understand model decisions.

Main Results:

• The multi-time-point ensemble approach significantly outperformed single-time-point analysis.
• Traditional machine learning models achieved comparable performance to deep learning models with engineered features.
• Interpretability techniques provided insights into model decision-making processes.

Conclusions:

• Incorporating temporal growth dynamics is vital for accurate seed vigor classification.
• Image-based, data-driven approaches show practical potential for seed quality assessment.
• Both traditional and deep learning methods are viable, depending on feature engineering and data integration.