A novel non-destructive detection approach for seed cotton lint percentage by using deep learning

Abstract

Abstract Background The lint percentage of seed cotton is one the most important parameters in evaluation the seed cotton quality, which affects the price of the seed cotton during the purchase and sale. The traditional method of measuring lint percentage is labor-intensive and time-consuming, and thus there is a need for an efficient and accurate method. In recent years, classification-based machine learning and computer vision have shown promise in solving various classification tasks. Results In this study, we propose a new approach for detecting lint percentage using MobileNetV2 and transfer learning. The model is deployed on the Lint Percentage detection instrument, which can rapidly and accurately determine the lint percentage of seed cotton. We evaluated the performance of the proposed approach using a dataset of 66924 seed cotton images from different regions of China. The results from the experiments showed that the model achieved an average accuracy of 98.43% in classification with an average precision of 94.97%, an average recall of 95.26%, and an average F1-score of 95.20%. Furthermore, the proposed classification model also achieved an average ac-curacy of 97.22% in calculating the lint percentage, showing no significant difference from the performance of experts (independent-samples t test, t = 0.019, p = 0.860). Conclusions This study demonstrates the effectiveness of the MobileNetV2 model and transfer learning in calculating the lint percentage of seed cotton. The proposed approach is a promising alternative to the traditional method, offering a rapid and accurate solution for the industry.