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Agronomy
Volume 15
Issue 2
10.3390/agronomy15020254
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Article

Development of an Efficient Grading Model for Maize Seedlings Based on Indicator Extraction in High-Latitude Cold Regions of Northeast China

by
Song Yu
1,
Yuxin Lu
1,
Yutao Zhang
1,
Xinran Liu
1,
Yifei Zhang
1,*,
Mukai Li
1,
Haotian Du
1,
Shan Su
1,
Jiawang Liu
1,
Shiqiang Yu
1,
Jiao Yang
2,
Yanjie Lv
3,
Haiou Guan
2,4 and
Chunyu Zhang
1,4
1
College of Agriculture, Heilongjiang Bayi Agricultural University, Daqing 163319, China
2
College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
3
Institute of Agricultural Resources and Environment, Jilin Academy of Agricultural Sciences, Changchun 130033, China
4
Key Laboratory of Low-Carbon Green Agriculture in Northeastern China, Ministry of Agriculture and Rural Affairs, Daqing 163319, China
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(2), 254; https://doi.org/10.3390/agronomy15020254
Submission received: 31 December 2024 / Revised: 16 January 2025 / Accepted: 20 January 2025 / Published: 21 January 2025
(This article belongs to the Section Precision and Digital Agriculture)
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Abstract

Maize, the world’s most widely cultivated food crop, is critical in global food security. Low temperatures significantly hinder maize seedling growth, development, and yield formation. Efficient and accurate assessment of maize seedling quality under cold stress is essential for selecting cold-tolerant varieties and guiding field management strategies. However, existing evaluation methods lack a multimodal approach, resulting in inefficiencies and inaccuracies. This study combines phenotypic extraction technologies with a convolutional neural network–long short-term memory (CNN–LSTM) deep learning model to develop an advanced grading system for maize seedling quality. Initially, 27 quality indices were measured from 3623 samples. The RAGA-PPC model identified seven critical indices: plant height (x1), stem diameter (x2), width of the third spreading leaf (x11), total leaf area (x12), root volume (x17), shoot fresh weight (x22), and root fresh weight (x23). The CNN–LSTM model, leveraging CNNs for feature extraction and LSTM for temporal dependencies, achieved a grading accuracy of 97.57%, surpassing traditional CNN and LSTM models by 1.28% and 1.44%, respectively. This system identifies phenotypic markers for assessing maize seedling quality, aids in selecting cold-tolerant varieties, and offers data-driven support for optimising maize production. It provides a robust framework for evaluating seedling quality under low-temperature stress.
Keywords: maize; low temperature stress; seedling quality; phenotypic evaluation indicators; efficient extraction; deep learning maize; low temperature stress; seedling quality; phenotypic evaluation indicators; efficient extraction; deep learning

Share and Cite

MDPI and ACS Style

Yu, S.; Lu, Y.; Zhang, Y.; Liu, X.; Zhang, Y.; Li, M.; Du, H.; Su, S.; Liu, J.; Yu, S.; et al. Development of an Efficient Grading Model for Maize Seedlings Based on Indicator Extraction in High-Latitude Cold Regions of Northeast China. Agronomy 2025, 15, 254. https://doi.org/10.3390/agronomy15020254

AMA Style

Yu S, Lu Y, Zhang Y, Liu X, Zhang Y, Li M, Du H, Su S, Liu J, Yu S, et al. Development of an Efficient Grading Model for Maize Seedlings Based on Indicator Extraction in High-Latitude Cold Regions of Northeast China. Agronomy. 2025; 15(2):254. https://doi.org/10.3390/agronomy15020254

Chicago/Turabian Style

Yu, Song, Yuxin Lu, Yutao Zhang, Xinran Liu, Yifei Zhang, Mukai Li, Haotian Du, Shan Su, Jiawang Liu, Shiqiang Yu, and et al. 2025. "Development of an Efficient Grading Model for Maize Seedlings Based on Indicator Extraction in High-Latitude Cold Regions of Northeast China" Agronomy 15, no. 2: 254. https://doi.org/10.3390/agronomy15020254

APA Style

Yu, S., Lu, Y., Zhang, Y., Liu, X., Zhang, Y., Li, M., Du, H., Su, S., Liu, J., Yu, S., Yang, J., Lv, Y., Guan, H., & Zhang, C. (2025). Development of an Efficient Grading Model for Maize Seedlings Based on Indicator Extraction in High-Latitude Cold Regions of Northeast China. Agronomy, 15(2), 254. https://doi.org/10.3390/agronomy15020254

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