Rapid and non-destructive classification of rice seeds with different flavors: an approach based on HPFasterNet
Rice is an important part of the food supply, its different varieties in terms of quality, flavor, nutritional value, and other aspects of the differences, directly affect the subsequent yield and economic benefits. However, traditional rice identification methods are time-consuming, inefficient, an...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-01-01
|
| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2024.1502631/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832593526075949056 |
|---|---|
| author | Helong Yu Helong Yu Zhenyang Chen Shaozhong Song Shaozhong Song Shaozhong Song Chunyan Qi Junling Liu Chenglin Yang |
| author_facet | Helong Yu Helong Yu Zhenyang Chen Shaozhong Song Shaozhong Song Shaozhong Song Chunyan Qi Junling Liu Chenglin Yang |
| author_sort | Helong Yu |
| collection | DOAJ |
| description | Rice is an important part of the food supply, its different varieties in terms of quality, flavor, nutritional value, and other aspects of the differences, directly affect the subsequent yield and economic benefits. However, traditional rice identification methods are time-consuming, inefficient, and prone to damage. For this reason, this study proposes a deep learning-based method to classify and identify rice with different flavors in a fast and non-destructive way. In this experiment, 19 categories of japonica rice seeds were selected, and a total of 36735 images were finally obtained. The lightweight network High Precision FasterNet (HPFasterNet) proposed in this study combines the Ghost bottleneck and FasterNet_T0 and introduces group convolution to compare the model performance. The results show that HPFasterNet has the highest classification accuracy of 92%, which is 5.22% better than the original model FasterNet_T0, and the number of parameters and computation is significantly reduced compared to the original model, which is more suitable for resource-limited environments. Comparison with three classical models and three lightweight models shows that HPFasterNet exhibits a more comprehensive and integrated performance. Meanwhile, in this study, HPFasterNet was used to test rice with different flavors, and the accuracy reached 98.98%. The experimental results show that the network model proposed in this study can be used to provide auxiliary experiments for rice breeding and can also be applied to consumer and food industries. |
| format | Article |
| id | doaj-art-1f860616b808465b9f2ffdf8725833c5 |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-1f860616b808465b9f2ffdf8725833c52025-01-20T12:17:10ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-01-011510.3389/fpls.2024.15026311502631Rapid and non-destructive classification of rice seeds with different flavors: an approach based on HPFasterNetHelong Yu0Helong Yu1Zhenyang Chen2Shaozhong Song3Shaozhong Song4Shaozhong Song5Chunyan Qi6Junling Liu7Chenglin Yang8Smart Agriculture Research Institute, Jilin Agricultural University, Changchun, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun, ChinaSmart Agriculture Research Institute, Jilin Agricultural University, Changchun, ChinaSmart Agriculture Research Institute, Jilin Agricultural University, Changchun, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun, ChinaSchool of Data Science and Artificial Intelligence, Jilin Engineering Normal University, Changchun, ChinaRice Research Institute, Jilin Academy of Agricultural Sciences, Changchun, ChinaSchool of Data Science and Artificial Intelligence, Jilin Engineering Normal University, Changchun, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun, ChinaRice is an important part of the food supply, its different varieties in terms of quality, flavor, nutritional value, and other aspects of the differences, directly affect the subsequent yield and economic benefits. However, traditional rice identification methods are time-consuming, inefficient, and prone to damage. For this reason, this study proposes a deep learning-based method to classify and identify rice with different flavors in a fast and non-destructive way. In this experiment, 19 categories of japonica rice seeds were selected, and a total of 36735 images were finally obtained. The lightweight network High Precision FasterNet (HPFasterNet) proposed in this study combines the Ghost bottleneck and FasterNet_T0 and introduces group convolution to compare the model performance. The results show that HPFasterNet has the highest classification accuracy of 92%, which is 5.22% better than the original model FasterNet_T0, and the number of parameters and computation is significantly reduced compared to the original model, which is more suitable for resource-limited environments. Comparison with three classical models and three lightweight models shows that HPFasterNet exhibits a more comprehensive and integrated performance. Meanwhile, in this study, HPFasterNet was used to test rice with different flavors, and the accuracy reached 98.98%. The experimental results show that the network model proposed in this study can be used to provide auxiliary experiments for rice breeding and can also be applied to consumer and food industries.https://www.frontiersin.org/articles/10.3389/fpls.2024.1502631/fullrice seed classificationjaponica ricedeep learningdifferent flavored ricelightweight network |
| spellingShingle | Helong Yu Helong Yu Zhenyang Chen Shaozhong Song Shaozhong Song Shaozhong Song Chunyan Qi Junling Liu Chenglin Yang Rapid and non-destructive classification of rice seeds with different flavors: an approach based on HPFasterNet Frontiers in Plant Science rice seed classification japonica rice deep learning different flavored rice lightweight network |
| title | Rapid and non-destructive classification of rice seeds with different flavors: an approach based on HPFasterNet |
| title_full | Rapid and non-destructive classification of rice seeds with different flavors: an approach based on HPFasterNet |
| title_fullStr | Rapid and non-destructive classification of rice seeds with different flavors: an approach based on HPFasterNet |
| title_full_unstemmed | Rapid and non-destructive classification of rice seeds with different flavors: an approach based on HPFasterNet |
| title_short | Rapid and non-destructive classification of rice seeds with different flavors: an approach based on HPFasterNet |
| title_sort | rapid and non destructive classification of rice seeds with different flavors an approach based on hpfasternet |
| topic | rice seed classification japonica rice deep learning different flavored rice lightweight network |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2024.1502631/full |
| work_keys_str_mv | AT helongyu rapidandnondestructiveclassificationofriceseedswithdifferentflavorsanapproachbasedonhpfasternet AT helongyu rapidandnondestructiveclassificationofriceseedswithdifferentflavorsanapproachbasedonhpfasternet AT zhenyangchen rapidandnondestructiveclassificationofriceseedswithdifferentflavorsanapproachbasedonhpfasternet AT shaozhongsong rapidandnondestructiveclassificationofriceseedswithdifferentflavorsanapproachbasedonhpfasternet AT shaozhongsong rapidandnondestructiveclassificationofriceseedswithdifferentflavorsanapproachbasedonhpfasternet AT shaozhongsong rapidandnondestructiveclassificationofriceseedswithdifferentflavorsanapproachbasedonhpfasternet AT chunyanqi rapidandnondestructiveclassificationofriceseedswithdifferentflavorsanapproachbasedonhpfasternet AT junlingliu rapidandnondestructiveclassificationofriceseedswithdifferentflavorsanapproachbasedonhpfasternet AT chenglinyang rapidandnondestructiveclassificationofriceseedswithdifferentflavorsanapproachbasedonhpfasternet |