Detection of Rice Spikelet Flowering for Hybrid Rice Seed Production Using Hyperspectral Technique and Machine Learning
Effective detection of rice spikelet flowering is crucial to the determination of optimal pollination timing for hybrid rice seed production. Currently, the detection of rice spikelet flowering status relies on manual observation of farmers, which has low efficiency and large errors. This study atte...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/12/6/755 |
| _version_ | 1797491078396379136 |
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| author | Yali Zhang Luchao Bai Yuan Qi Huasheng Huang Xiaoyang Lu Junqi Xiao Yubin Lan Muhua Lin Jizhong Deng |
| author_facet | Yali Zhang Luchao Bai Yuan Qi Huasheng Huang Xiaoyang Lu Junqi Xiao Yubin Lan Muhua Lin Jizhong Deng |
| author_sort | Yali Zhang |
| collection | DOAJ |
| description | Effective detection of rice spikelet flowering is crucial to the determination of optimal pollination timing for hybrid rice seed production. Currently, the detection of rice spikelet flowering status relies on manual observation of farmers, which has low efficiency and large errors. This study attempts to acquire rice spikelet flowering information using a hyperspectral technique and machine learning in order to meet the needs of hybrid rice seed pollination rapidly and automatically. Hyperspectral data of rice male parents with flowering and non-flowering in two experimental sites were collected with an ASD FieldSpec<sup>®</sup> HandHeld™2 spectrometer. Three traditional classifiers, Random Forest (RF), Support Vector Machine (SVM) and Back Propagation (BP) neural network, and Convolutional Neural Network (CNN), were used to build classification models for rice spikelets flowering detection. Three data processing methods, PCA feature extraction, GA feature selection, and the PCA and GA combination algorithm, were used for data dimensionality reduction. By comparing the precision and recall rate of different algorithms and data processing methods, the algorithms applicable to identify rice spikelet flowering were investigated. Results show that by evaluating different feature reduction methods and classifiers, the optimal model for rice spikelets flowering detection is the BP model with PCA feature extraction. The accuracy of the model reaches up to 96–100%. Hyperspectral technology and machine learning algorithm are capable of effective detection of rice spikelet flowering. This study provides technical reference for accurate judgment of rice flowering and helps to determine the optimal operation time for supplementary pollination of hybrid rice. |
| first_indexed | 2024-03-10T00:42:06Z |
| format | Article |
| id | doaj.art-7875baa511f843aeae5b16b34c1bf1d9 |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-10T00:42:06Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-7875baa511f843aeae5b16b34c1bf1d92023-11-23T15:06:09ZengMDPI AGAgriculture2077-04722022-05-0112675510.3390/agriculture12060755Detection of Rice Spikelet Flowering for Hybrid Rice Seed Production Using Hyperspectral Technique and Machine LearningYali Zhang0Luchao Bai1Yuan Qi2Huasheng Huang3Xiaoyang Lu4Junqi Xiao5Yubin Lan6Muhua Lin7Jizhong Deng8College of Engineering, South China Agricultural University, Guangzhou 510642, ChinaCollege of Engineering, South China Agricultural University, Guangzhou 510642, ChinaCollege of Mechanical Engineering, Nantong Vocational University, Nantong 226007, ChinaCollege of Computer Sciences, Guangdong Polytechnic Normal University, Zhongshan Road, Guangzhou 510642, ChinaCollege of Engineering, South China Agricultural University, Guangzhou 510642, ChinaCollege of Engineering, South China Agricultural University, Guangzhou 510642, ChinaNational Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, ChinaCollege of Engineering, South China Agricultural University, Guangzhou 510642, ChinaCollege of Engineering, South China Agricultural University, Guangzhou 510642, ChinaEffective detection of rice spikelet flowering is crucial to the determination of optimal pollination timing for hybrid rice seed production. Currently, the detection of rice spikelet flowering status relies on manual observation of farmers, which has low efficiency and large errors. This study attempts to acquire rice spikelet flowering information using a hyperspectral technique and machine learning in order to meet the needs of hybrid rice seed pollination rapidly and automatically. Hyperspectral data of rice male parents with flowering and non-flowering in two experimental sites were collected with an ASD FieldSpec<sup>®</sup> HandHeld™2 spectrometer. Three traditional classifiers, Random Forest (RF), Support Vector Machine (SVM) and Back Propagation (BP) neural network, and Convolutional Neural Network (CNN), were used to build classification models for rice spikelets flowering detection. Three data processing methods, PCA feature extraction, GA feature selection, and the PCA and GA combination algorithm, were used for data dimensionality reduction. By comparing the precision and recall rate of different algorithms and data processing methods, the algorithms applicable to identify rice spikelet flowering were investigated. Results show that by evaluating different feature reduction methods and classifiers, the optimal model for rice spikelets flowering detection is the BP model with PCA feature extraction. The accuracy of the model reaches up to 96–100%. Hyperspectral technology and machine learning algorithm are capable of effective detection of rice spikelet flowering. This study provides technical reference for accurate judgment of rice flowering and helps to determine the optimal operation time for supplementary pollination of hybrid rice.https://www.mdpi.com/2077-0472/12/6/755hyperspectralmachine learningCNNrice spikeletsRFSVM |
| spellingShingle | Yali Zhang Luchao Bai Yuan Qi Huasheng Huang Xiaoyang Lu Junqi Xiao Yubin Lan Muhua Lin Jizhong Deng Detection of Rice Spikelet Flowering for Hybrid Rice Seed Production Using Hyperspectral Technique and Machine Learning Agriculture hyperspectral machine learning CNN rice spikelets RF SVM |
| title | Detection of Rice Spikelet Flowering for Hybrid Rice Seed Production Using Hyperspectral Technique and Machine Learning |
| title_full | Detection of Rice Spikelet Flowering for Hybrid Rice Seed Production Using Hyperspectral Technique and Machine Learning |
| title_fullStr | Detection of Rice Spikelet Flowering for Hybrid Rice Seed Production Using Hyperspectral Technique and Machine Learning |
| title_full_unstemmed | Detection of Rice Spikelet Flowering for Hybrid Rice Seed Production Using Hyperspectral Technique and Machine Learning |
| title_short | Detection of Rice Spikelet Flowering for Hybrid Rice Seed Production Using Hyperspectral Technique and Machine Learning |
| title_sort | detection of rice spikelet flowering for hybrid rice seed production using hyperspectral technique and machine learning |
| topic | hyperspectral machine learning CNN rice spikelets RF SVM |
| url | https://www.mdpi.com/2077-0472/12/6/755 |
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