Insights into Cottonseed Cultivar Identification Using Raman Spectroscopy and Explainable Machine Learning

Abstract

Securing authentic cottonseed identity information is crucial for preserving the livelihoods of farmers. Traditional seed identification methods are generally time-consuming, and have a high degree of difficulty. Raman spectroscopy, in combination with machine learning (ML), has opened up new avenues for seed identification. In this study, we explored the feasibility of using Raman spectroscopy combined with ML for cottonseed identification. Using Raman confocal microscopy, we constructed fingerprints of cottonseeds and analyzed their important Raman peaks. We integrated two feature exploration methods (Principal Component Analysis and Harris Hawk optimization) and three ML algorithms (Support Vector Machine, eXtreme Gradient Boosting, and Multi-Layer Perceptron) into a Raman spectroscopy analysis framework to accurately identify cottonseed cultivars. Through the utilization of SHapley Additive exPlanations (SHAP), we provide an in-depth explanation of the model’s decision-making process. Our results demonstrate that XGBoost, a tree-based model, exhibits outstanding accuracy (overall accuracy of 0.94–0.88) in cottonseed identification. Notably, lignin emerged as a pivotal factor that strongly influenced the model’s prediction of cottonseed cultivars, as revealed by the XGBoost interpretation. Overall, our study illustrates the effectiveness of combining Raman spectroscopy with ML to precisely identify cottonseed cultivars. The SHAP framework used in our study enables seed-related personnel to better comprehend the model’s prediction mechanism. These valuable insights are expected to enhance seed planting and management practices in the future.