Rapid Detection of Hybrid Maize Parental Lines Using Stacking Ensemble Machine Learning
Hybrid maize seed production is a relatively complex task due to the coexistence of three distinct types of maize plants in the field: female, male, and contaminant/off-type plants. Female and contaminant/off-type plants’ tassels should be removed immediately following flowering initiation, while ma...
Saved in:
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2022/6588949 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832554784706527232 |
|---|---|
| author | M. Aqil M. Azrai M. J. Mejaya N. A. Subekti F. Tabri N. N. Andayani Rahma Wati S. Panikkai S. Suwardi Z. Bunyamin E. Roy M. Muslimin M. Yasin E. Prakasa |
| author_facet | M. Aqil M. Azrai M. J. Mejaya N. A. Subekti F. Tabri N. N. Andayani Rahma Wati S. Panikkai S. Suwardi Z. Bunyamin E. Roy M. Muslimin M. Yasin E. Prakasa |
| author_sort | M. Aqil |
| collection | DOAJ |
| description | Hybrid maize seed production is a relatively complex task due to the coexistence of three distinct types of maize plants in the field: female, male, and contaminant/off-type plants. Female and contaminant/off-type plants’ tassels should be removed immediately following flowering initiation, while male tassels should be retained to allow cross-pollination between male and female plants. Therefore, development of an intelligent tassel classification system is deemed critical for hybrid purity decision-making. The research’s primary contribution is the integration of two widely used transfer learning architectures, Inception V3 and SqueezeNet, with stacking ensemble machine learning using four algorithms (logistic regression, support vector machine, random forest, and k-nearest neighbors) for rapid classification of tassel images. Tenfold cross-validation was used to evaluate the model performance. Cloud computing was also investigated using EfficientNet to compare the predictive performance of the models. The models’ performance was assessed using four metrics: accuracy, AUC, precision, and recall. The results depicted an appropriate developed model that properly distinguished male, female, and contaminant plants. The integration of the model with machine learnings (logistic regression, SVM, random forest, and KNNs) enables rapid recognition of off-type plants even though it is operated by personnel with limited skills of seed technology on ideotype recognition. Among all the evaluated CNN architecture and stacking models, Inception V3-embedded images with logistic regression metaclassifier outperformed other models with accuracy of about 98%. SqueezeNet and EfficientNet provided comparable results for consistent tassel classification with slightly lower performance measures. The model was also subjected to a multidimensional scaling (MDS) analysis to investigate and comprehend misclassification. Male and female plants are clearly distinguished by MDS, but female and off-type/contamination plants are ambiguous. This indicates that the prediction errors were caused by highly similar data features among female and off-type images. The developed modern plant phenotyping model can be used to assist breeders/technicians in maintaining the quality of large-scale hybrid maize seed production activities in Indonesia. |
| format | Article |
| id | doaj-art-6404f91c01cf46d2a53cb405432e89d4 |
| institution | Kabale University |
| issn | 1687-9732 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-6404f91c01cf46d2a53cb405432e89d42025-02-03T05:50:31ZengWileyApplied Computational Intelligence and Soft Computing1687-97322022-01-01202210.1155/2022/6588949Rapid Detection of Hybrid Maize Parental Lines Using Stacking Ensemble Machine LearningM. Aqil0M. Azrai1M. J. Mejaya2N. A. Subekti3F. Tabri4N. N. Andayani5Rahma Wati6S. Panikkai7S. Suwardi8Z. Bunyamin9E. Roy10M. Muslimin11M. Yasin12E. Prakasa13Indonesian Cereals Research InstituteIndonesian Cereals Research InstituteIndonesian Legumes and Tuber Crops Research InstituteIndonesian Center for Food Crops Research and DevelopmentIndonesian Cereals Research InstituteIndonesian Cereals Research InstituteIndonesian Cereals Research InstituteIndonesian Cereals Research InstituteIndonesian Cereals Research InstituteIndonesian Cereals Research InstituteIndonesian Cereals Research InstituteIndonesian Cereals Research InstituteIndonesian Cereals Research InstituteNational Research and Innovation AgencyHybrid maize seed production is a relatively complex task due to the coexistence of three distinct types of maize plants in the field: female, male, and contaminant/off-type plants. Female and contaminant/off-type plants’ tassels should be removed immediately following flowering initiation, while male tassels should be retained to allow cross-pollination between male and female plants. Therefore, development of an intelligent tassel classification system is deemed critical for hybrid purity decision-making. The research’s primary contribution is the integration of two widely used transfer learning architectures, Inception V3 and SqueezeNet, with stacking ensemble machine learning using four algorithms (logistic regression, support vector machine, random forest, and k-nearest neighbors) for rapid classification of tassel images. Tenfold cross-validation was used to evaluate the model performance. Cloud computing was also investigated using EfficientNet to compare the predictive performance of the models. The models’ performance was assessed using four metrics: accuracy, AUC, precision, and recall. The results depicted an appropriate developed model that properly distinguished male, female, and contaminant plants. The integration of the model with machine learnings (logistic regression, SVM, random forest, and KNNs) enables rapid recognition of off-type plants even though it is operated by personnel with limited skills of seed technology on ideotype recognition. Among all the evaluated CNN architecture and stacking models, Inception V3-embedded images with logistic regression metaclassifier outperformed other models with accuracy of about 98%. SqueezeNet and EfficientNet provided comparable results for consistent tassel classification with slightly lower performance measures. The model was also subjected to a multidimensional scaling (MDS) analysis to investigate and comprehend misclassification. Male and female plants are clearly distinguished by MDS, but female and off-type/contamination plants are ambiguous. This indicates that the prediction errors were caused by highly similar data features among female and off-type images. The developed modern plant phenotyping model can be used to assist breeders/technicians in maintaining the quality of large-scale hybrid maize seed production activities in Indonesia.http://dx.doi.org/10.1155/2022/6588949 |
| spellingShingle | M. Aqil M. Azrai M. J. Mejaya N. A. Subekti F. Tabri N. N. Andayani Rahma Wati S. Panikkai S. Suwardi Z. Bunyamin E. Roy M. Muslimin M. Yasin E. Prakasa Rapid Detection of Hybrid Maize Parental Lines Using Stacking Ensemble Machine Learning Applied Computational Intelligence and Soft Computing |
| title | Rapid Detection of Hybrid Maize Parental Lines Using Stacking Ensemble Machine Learning |
| title_full | Rapid Detection of Hybrid Maize Parental Lines Using Stacking Ensemble Machine Learning |
| title_fullStr | Rapid Detection of Hybrid Maize Parental Lines Using Stacking Ensemble Machine Learning |
| title_full_unstemmed | Rapid Detection of Hybrid Maize Parental Lines Using Stacking Ensemble Machine Learning |
| title_short | Rapid Detection of Hybrid Maize Parental Lines Using Stacking Ensemble Machine Learning |
| title_sort | rapid detection of hybrid maize parental lines using stacking ensemble machine learning |
| url | http://dx.doi.org/10.1155/2022/6588949 |
| work_keys_str_mv | AT maqil rapiddetectionofhybridmaizeparentallinesusingstackingensemblemachinelearning AT mazrai rapiddetectionofhybridmaizeparentallinesusingstackingensemblemachinelearning AT mjmejaya rapiddetectionofhybridmaizeparentallinesusingstackingensemblemachinelearning AT nasubekti rapiddetectionofhybridmaizeparentallinesusingstackingensemblemachinelearning AT ftabri rapiddetectionofhybridmaizeparentallinesusingstackingensemblemachinelearning AT nnandayani rapiddetectionofhybridmaizeparentallinesusingstackingensemblemachinelearning AT rahmawati rapiddetectionofhybridmaizeparentallinesusingstackingensemblemachinelearning AT spanikkai rapiddetectionofhybridmaizeparentallinesusingstackingensemblemachinelearning AT ssuwardi rapiddetectionofhybridmaizeparentallinesusingstackingensemblemachinelearning AT zbunyamin rapiddetectionofhybridmaizeparentallinesusingstackingensemblemachinelearning AT eroy rapiddetectionofhybridmaizeparentallinesusingstackingensemblemachinelearning AT mmuslimin rapiddetectionofhybridmaizeparentallinesusingstackingensemblemachinelearning AT myasin rapiddetectionofhybridmaizeparentallinesusingstackingensemblemachinelearning AT eprakasa rapiddetectionofhybridmaizeparentallinesusingstackingensemblemachinelearning |