Academic Journal

A novel nondestructive detection approach for seed cotton lint percentage using deep learning

Bibliographic Details
Title: A novel nondestructive detection approach for seed cotton lint percentage using deep learning
Authors: Lijie Geng, Pengji Yan, Zhikun Ji, Chunyu Song, Shuaifei Song, Ruiliang Zhang, Zhifeng Zhang, Yusheng Zhai, Liying Jiang, Kun Yang
Source: Journal of Cotton Research, Vol 7, Iss 1, Pp 1-15 (2024)
Subject Terms: Neural network, MobileNetV2, Nondestructive detection, Smart agriculture, Seed cotton lint percentage, Plant culture, SB1-1110
Publisher Information: BMC, 2024.
Publication Year: 2024
Collection: LCC:Plant culture
Description: Abstract Background The lint percentage of seed cotton is one of the most important parameters for evaluating seed cotton quality and affects its price. The traditional measuring method of lint percentage is labor-intensive and time-consuming; thus, an efficient and accurate measurement method is needed. In recent years, classification-based deep learning and computer vision have shown promise in solving various classification tasks. Results In this study, we propose a new approach for detecting the lint percentage using MobileNetV2 and transfer learning. The model is deployed on a 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 comprising 66 924 seed cotton images from different regions of China. The results of the experiments showed that the model with transfer learning achieved an average classification accuracy of 98.43%, 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 achieved an average accuracy of 97.22% in calculating the lint percentage, showing no significant difference from the performance of experts (independent-sample t-test, t = 0.019, P = 0.860). Conclusion This study demonstrated 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 traditional methods, providing a rapid and accurate solution for the industry.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2523-3254
Relation: https://doaj.org/toc/2523-3254
DOI: 10.1186/s42397-024-00178-x
Access URL: https://doaj.org/article/02ba4cd193f74b24865e7c30e00c2c4f
Accession Number: edsdoj.02ba4cd193f74b24865e7c30e00c2c4f
ISSN: 25233254
DOI: 10.1186/s42397-024-00178-x
Database: Directory of Open Access Journals