Deep convolution neural network with weighted loss to detect rice seeds vigor based on hyperspectral imaging under the sample-imbalanced condition

Vigor detection of crop seeds before putting them on the market is important for ensuring the yield and quality of the crops. In the actual production, however, different levels tend to vary in the number of samples, leading to the problem of sample imbalance. This study proposed a deep convolution neural network (DCNN) with weighted loss to achieve batch and non-destructive vigor detection of rice seeds based on hyperspectral imaging (HSI) under the sample-imbalanced condition. The true vigor state of seeds with different degrees of artificial aging was labeled by traditional analysis methods. The seeds were first classified into six categories according to the aging time using a constructed DCNN, which was proved to be unreasonable. Then four categories were merged, and the seeds were reclassified into three new categories by a rebuilt DCNN. However, merging categories caused the problem of sample imbalance, leading to much confusion between two aged categories. Thus, a DCNN with weighted loss was further proposed focusing on assigning appropriate weight to each category. Obtaining the highest accuracy and Macro F1 of 97.69% and 97.42%, respectively, it outperformed the DCNN with balanced loss and conventional models. The visualization analysis was conducted using PCA and t-SNE to inspect the aggregation between feature points. The overall results indicated the effectiveness of the proposed DCNN with weighted loss in the vigor detection of rice seeds under the sample-imbalanced condition, which would be conducive to online grading according to seed vigor and other qualities in the actual production.

Automated summary

This study proposed a method for vigor detection of rice seeds using deep convolution neural networks with weighted loss under sample-imbalanced condition. The method achieved high accuracy and F1 score by classifying the seeds into different categories. This method is important for online grading of seeds based on vigor in actual production.