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

Jianan Chi, Xiangxin Bu, Xiao Zhang (), Lijun Wang and Nannan Zhang ()
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Jianan Chi: School of Information Engineering, Tarim University, Alaer 843300, China
Xiangxin Bu: Southern Xinjiang Research Center for Information Technology in Agriculture, Tarim University, Alaer 843300, China
Xiao Zhang: School of Information Engineering, Tarim University, Alaer 843300, China
Lijun Wang: School of Information Engineering, Tarim University, Alaer 843300, China
Nannan Zhang: School of Information Engineering, Tarim University, Alaer 843300, China

Agriculture, 2023, vol. 13, issue 4, 1-17

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.

Keywords: cottonseed; Raman spectroscopy; explainable machine learning; SHAP; XGBoost (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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