Determination of Wheat Growth Stages Using Image Sequences and Deep Learning
The growth stage of wheat is key information for critical decision-making related to cultivar screening of wheat and farming activities. In order to solve the problem that it is difficult to determine the growth stages of a large number of wheat breeding materials grown in an artificial climate room...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/1/13 |
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| author | Chunying Wang Xubin Song Weiting Pan Haixia Yu Xiang Li Ping Liu |
| author_facet | Chunying Wang Xubin Song Weiting Pan Haixia Yu Xiang Li Ping Liu |
| author_sort | Chunying Wang |
| collection | DOAJ |
| description | The growth stage of wheat is key information for critical decision-making related to cultivar screening of wheat and farming activities. In order to solve the problem that it is difficult to determine the growth stages of a large number of wheat breeding materials grown in an artificial climate room accurately and quickly, the first attempt was made to determine the growth stages of wheat using image sequences of growth and development. A hybrid model (DenseNet–BiLSTM) based on the DenseNet and Bidirectional Long Short-Term Memory was proposed for determining the growth stage of wheat. The spatiotemporal characteristics of wheat growth and development were modeled by DenseNet–BiLSTM synthetically to classify the growth stage of each wheat image in the sequence. The determination accuracy of the growth stages obtained by the proposed DenseNet–BiLSTM model was 98.43%. Of these, the determination precisions of the tillering, re-greening, jointing, booting, and heading period were 100%, 97.80%, 97.80%, 85.71%, and 95.65%, respectively. In addition, the accurate determination of the growth stages and further analysis of its relationship with meteorological conditions will help biologists, geneticists, and breeders to breed, screen, and evaluate wheat varieties with ecological adaptability. |
| format | Article |
| id | doaj-art-9ba7a435fd05456d941dee6ca479f749 |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-9ba7a435fd05456d941dee6ca479f7492025-01-24T13:16:21ZengMDPI AGAgronomy2073-43952024-12-011511310.3390/agronomy15010013Determination of Wheat Growth Stages Using Image Sequences and Deep LearningChunying Wang0Xubin Song1Weiting Pan2Haixia Yu3Xiang Li4Ping Liu5State Key Laboratory of Wheat Improvement, Shandong Agricultural University, Tai’an 271018, ChinaCollege of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271000, ChinaCollege of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271000, ChinaState Key Laboratory of Wheat Improvement, Shandong Agricultural University, Tai’an 271018, ChinaState Key Laboratory of Wheat Improvement, Shandong Agricultural University, Tai’an 271018, ChinaState Key Laboratory of Wheat Improvement, Shandong Agricultural University, Tai’an 271018, ChinaThe growth stage of wheat is key information for critical decision-making related to cultivar screening of wheat and farming activities. In order to solve the problem that it is difficult to determine the growth stages of a large number of wheat breeding materials grown in an artificial climate room accurately and quickly, the first attempt was made to determine the growth stages of wheat using image sequences of growth and development. A hybrid model (DenseNet–BiLSTM) based on the DenseNet and Bidirectional Long Short-Term Memory was proposed for determining the growth stage of wheat. The spatiotemporal characteristics of wheat growth and development were modeled by DenseNet–BiLSTM synthetically to classify the growth stage of each wheat image in the sequence. The determination accuracy of the growth stages obtained by the proposed DenseNet–BiLSTM model was 98.43%. Of these, the determination precisions of the tillering, re-greening, jointing, booting, and heading period were 100%, 97.80%, 97.80%, 85.71%, and 95.65%, respectively. In addition, the accurate determination of the growth stages and further analysis of its relationship with meteorological conditions will help biologists, geneticists, and breeders to breed, screen, and evaluate wheat varieties with ecological adaptability.https://www.mdpi.com/2073-4395/15/1/13wheatgrowth stage determinationdeep learningspatiotemporal informationimage sequences |
| spellingShingle | Chunying Wang Xubin Song Weiting Pan Haixia Yu Xiang Li Ping Liu Determination of Wheat Growth Stages Using Image Sequences and Deep Learning Agronomy wheat growth stage determination deep learning spatiotemporal information image sequences |
| title | Determination of Wheat Growth Stages Using Image Sequences and Deep Learning |
| title_full | Determination of Wheat Growth Stages Using Image Sequences and Deep Learning |
| title_fullStr | Determination of Wheat Growth Stages Using Image Sequences and Deep Learning |
| title_full_unstemmed | Determination of Wheat Growth Stages Using Image Sequences and Deep Learning |
| title_short | Determination of Wheat Growth Stages Using Image Sequences and Deep Learning |
| title_sort | determination of wheat growth stages using image sequences and deep learning |
| topic | wheat growth stage determination deep learning spatiotemporal information image sequences |
| url | https://www.mdpi.com/2073-4395/15/1/13 |
| work_keys_str_mv | AT chunyingwang determinationofwheatgrowthstagesusingimagesequencesanddeeplearning AT xubinsong determinationofwheatgrowthstagesusingimagesequencesanddeeplearning AT weitingpan determinationofwheatgrowthstagesusingimagesequencesanddeeplearning AT haixiayu determinationofwheatgrowthstagesusingimagesequencesanddeeplearning AT xiangli determinationofwheatgrowthstagesusingimagesequencesanddeeplearning AT pingliu determinationofwheatgrowthstagesusingimagesequencesanddeeplearning |