Seed multispectral imaging combined with machine learning algorithms for distinguishing different varieties of lettuce (Lactuca sativa L.)

Abstract

Accurate identification of high-quality seeds is crucial for maintaining superior crop traits. Lettuce is widely consumed vegetable with diverse varieties, however, the traditionally identification methods are both time-consuming and labor-intensive. This study explores feasibility of rapid, non-destructive identification of different lettuce varieties using multispectral imaging combined with machine learning. We firstly collected seed morphological and spectral data from 15 lettuce varieties using multispectral imaging. Then we applied Support Vector Machine (SVM), Random Forest (RF), and Back-Propagation Neural Network (BP), Linear Discriminant Analysis (LDA) for variety identification. The results demonstrated that multispectral imaging combined with machine learning models, effectively distinguished different lettuce seed varieties. The LDA model based on morphological and spectral fusion feature data performed best, and the average classification accuracy was 92.7 %. In the batch validation, the LDA model achieved an accuracy of 93.2 %.This method reduces cost and improves efficiency, showing great potential for seed identification in other crops.