Rapid Identification of Saline–Alkali Stress-Tolerant Peanut Varieties Based on Multimodal Data

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Abstract
The cultivation of saline–alkali-tolerant peanut (Arachis hypogaea 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.
Funding Information
  • Key Technology Research and Development Program of Shandong Province (2021LZGC026-03, SDAIT-22, 32073029, 2022SZX18, 2022YFD2300101-1, 2021LZGC003)
  • Shandong Province Modern Agricultural Technology System (2021LZGC026-03, SDAIT-22, 32073029, 2022SZX18, 2022YFD2300101-1, 2021LZGC003)
  • National Natural Science Foundation of China (2021LZGC026-03, SDAIT-22, 32073029, 2022SZX18, 2022YFD2300101-1, 2021LZGC003)
  • Science & Technology Specific Projects in Agricultural High-Tech Industrial Demonstration Area of the Yellow River Delta (2021LZGC026-03, SDAIT-22, 32073029, 2022SZX18, 2022YFD2300101-1, 2021LZGC003)
  • National Key Research and Development Program of China (2021LZGC026-03, SDAIT-22, 32073029, 2022SZX18, 2022YFD2300101-1, 2021LZGC003)
  • Seed-Industrialized Development Program in Shandong Province (2021LZGC026-03, SDAIT-22, 32073029, 2022SZX18, 2022YFD2300101-1, 2021LZGC003)

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