Rapid Identification of Saline–Alkali Stress-Tolerant Peanut Varieties Based on Multimodal Data
The cultivation of saline–alkali-tolerant peanut (<i>Arachis hypogaea</i> L.) varieties can effectively increase grain yield in saline–alkali land. However, traditional assessment methods are often cumbersome and time consuming. To rapidly identify saline–alkali stress-tolerant peanut va...
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| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/1/197 |
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| author | Fan Zhang Longgang Zhao Tingting Guo Ziyang Wang Peng Lou Juan Li |
| author_facet | Fan Zhang Longgang Zhao Tingting Guo Ziyang Wang Peng Lou Juan Li |
| author_sort | Fan Zhang |
| collection | DOAJ |
| description | The cultivation of saline–alkali-tolerant peanut (<i>Arachis hypogaea</i> L.) varieties can effectively increase grain yield in saline–alkali land. However, traditional assessment methods are often cumbersome and time consuming. To rapidly identify saline–alkali stress-tolerant peanut varieties, this research proposed a saline–alkali stress tolerance evaluation method based on deep learning and multimodal data. Specifically, the research first established multimodal datasets for peanuts at different growth stages and constructed a saline–alkali stress score standard based on unsupervised learning. Subsequently, a deep learning network called BO-MFFNet was built and its structure and hyperparameters were optimized by the Bayes optimization (BO) algorithm. Finally, the point prediction of the saline–alkali stress score were carried out by using the Gaussian process regression model. The experimental results show that the multimodal method is superior to the single-modal data and the BO algorithm significantly improves the performance of the model. The root mean squared error and relative percentage deviation of the BO-MFFNet model are 0.089 and 3.669, respectively. The model effectively predicted the salt–alkali stress tolerance of five varieties, and the predicted results were Huayu25, Yuhua31, Yuhua33, Yuhua32, and Yuhua164 from high to low. This research provides a new method for assessing crop tolerance under extreme environmental stress. |
| format | Article |
| id | doaj-art-3cc74b10805b4783ad16160e883867a9 |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-3cc74b10805b4783ad16160e883867a92025-01-24T13:17:08ZengMDPI AGAgronomy2073-43952025-01-0115119710.3390/agronomy15010197Rapid Identification of Saline–Alkali Stress-Tolerant Peanut Varieties Based on Multimodal DataFan Zhang0Longgang Zhao1Tingting Guo2Ziyang Wang3Peng Lou4Juan Li5College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, ChinaCollege of Grassland Science, Qingdao Agricultural University, Qingdao 266109, ChinaSchool of Electromechanical and Automative Engineering, Yantai University, Yantai 264005, ChinaCollege of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, ChinaCollege of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, ChinaCollege of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, ChinaThe cultivation of saline–alkali-tolerant peanut (<i>Arachis hypogaea</i> L.) varieties can effectively increase grain yield in saline–alkali land. However, traditional assessment methods are often cumbersome and time consuming. To rapidly identify saline–alkali stress-tolerant peanut varieties, this research proposed a saline–alkali stress tolerance evaluation method based on deep learning and multimodal data. Specifically, the research first established multimodal datasets for peanuts at different growth stages and constructed a saline–alkali stress score standard based on unsupervised learning. Subsequently, a deep learning network called BO-MFFNet was built and its structure and hyperparameters were optimized by the Bayes optimization (BO) algorithm. Finally, the point prediction of the saline–alkali stress score were carried out by using the Gaussian process regression model. The experimental results show that the multimodal method is superior to the single-modal data and the BO algorithm significantly improves the performance of the model. The root mean squared error and relative percentage deviation of the BO-MFFNet model are 0.089 and 3.669, respectively. The model effectively predicted the salt–alkali stress tolerance of five varieties, and the predicted results were Huayu25, Yuhua31, Yuhua33, Yuhua32, and Yuhua164 from high to low. This research provides a new method for assessing crop tolerance under extreme environmental stress.https://www.mdpi.com/2073-4395/15/1/197peanutinformation fusiondeep learningsaline–alkali stressmultimodal data |
| spellingShingle | Fan Zhang Longgang Zhao Tingting Guo Ziyang Wang Peng Lou Juan Li Rapid Identification of Saline–Alkali Stress-Tolerant Peanut Varieties Based on Multimodal Data Agronomy peanut information fusion deep learning saline–alkali stress multimodal data |
| title | Rapid Identification of Saline–Alkali Stress-Tolerant Peanut Varieties Based on Multimodal Data |
| title_full | Rapid Identification of Saline–Alkali Stress-Tolerant Peanut Varieties Based on Multimodal Data |
| title_fullStr | Rapid Identification of Saline–Alkali Stress-Tolerant Peanut Varieties Based on Multimodal Data |
| title_full_unstemmed | Rapid Identification of Saline–Alkali Stress-Tolerant Peanut Varieties Based on Multimodal Data |
| title_short | Rapid Identification of Saline–Alkali Stress-Tolerant Peanut Varieties Based on Multimodal Data |
| title_sort | rapid identification of saline alkali stress tolerant peanut varieties based on multimodal data |
| topic | peanut information fusion deep learning saline–alkali stress multimodal data |
| url | https://www.mdpi.com/2073-4395/15/1/197 |
| work_keys_str_mv | AT fanzhang rapididentificationofsalinealkalistresstolerantpeanutvarietiesbasedonmultimodaldata AT longgangzhao rapididentificationofsalinealkalistresstolerantpeanutvarietiesbasedonmultimodaldata AT tingtingguo rapididentificationofsalinealkalistresstolerantpeanutvarietiesbasedonmultimodaldata AT ziyangwang rapididentificationofsalinealkalistresstolerantpeanutvarietiesbasedonmultimodaldata AT penglou rapididentificationofsalinealkalistresstolerantpeanutvarietiesbasedonmultimodaldata AT juanli rapididentificationofsalinealkalistresstolerantpeanutvarietiesbasedonmultimodaldata |