Multi-Modal Late Fusion Rice Seed Variety Classification Based on an Improved Voting Method
Rice seed variety purity, an important index for measuring rice seed quality, has a great impact on the germination rate, yield, and quality of the final agricultural products. To classify rice varieties more efficiently and accurately, this study proposes a multimodal l fusion detection method base...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/13/3/597 |
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| author | Xinyi He Qiyang Cai Xiuguo Zou Hua Li Xuebin Feng Wenqing Yin Yan Qian |
| author_facet | Xinyi He Qiyang Cai Xiuguo Zou Hua Li Xuebin Feng Wenqing Yin Yan Qian |
| author_sort | Xinyi He |
| collection | DOAJ |
| description | Rice seed variety purity, an important index for measuring rice seed quality, has a great impact on the germination rate, yield, and quality of the final agricultural products. To classify rice varieties more efficiently and accurately, this study proposes a multimodal l fusion detection method based on an improved voting method. The experiment collected eight common rice seed types. Raytrix light field cameras were used to collect 2D images and 3D point cloud datasets, with a total of 3194 samples. The training and test sets were divided according to an 8:2 ratio. The experiment improved the traditional voting method. First, multiple models were used to predict the rice seed varieties. Then, the predicted probabilities were used as the late fusion input data. Next, a comprehensive score vector was calculated based on the performance of different models. In late fusion, the predicted probabilities from 2D and 3D were jointly weighted to obtain the final predicted probability. Finally, the predicted value with the highest probability was selected as the final value. In the experimental results, after late fusion of the predicted probabilities, the average accuracy rate reached 97.4%. Compared with the single support vector machine (SVM), k-nearest neighbors (kNN), convolutional neural network (CNN), MobileNet, and PointNet, the accuracy rates increased by 4.9%, 8.3%, 18.1%, 8.3%, and 9%, respectively. Among the eight varieties, the recognition accuracy of two rice varieties, Hannuo35 and Yuanhan35, by applying the voting method improved most significantly, from 73.9% and 77.7% in two dimensions to 92.4% and 96.3%, respectively. Thus, the improved voting method can combine the advantages of different data modalities and significantly improve the final prediction results. |
| first_indexed | 2024-03-11T07:04:46Z |
| format | Article |
| id | doaj.art-73d91b1f95e94c0d921baf7a3a2a11dc |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-11T07:04:46Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-73d91b1f95e94c0d921baf7a3a2a11dc2023-11-17T09:00:43ZengMDPI AGAgriculture2077-04722023-03-0113359710.3390/agriculture13030597Multi-Modal Late Fusion Rice Seed Variety Classification Based on an Improved Voting MethodXinyi He0Qiyang Cai1Xiuguo Zou2Hua Li3Xuebin Feng4Wenqing Yin5Yan Qian6College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, ChinaCollege of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, ChinaCollege of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, ChinaCollege of Engineering, Nanjing Agriculture University, Nanjing 210031, ChinaCollege of Engineering, Nanjing Agriculture University, Nanjing 210031, ChinaCollege of Engineering, Nanjing Agriculture University, Nanjing 210031, ChinaCollege of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, ChinaRice seed variety purity, an important index for measuring rice seed quality, has a great impact on the germination rate, yield, and quality of the final agricultural products. To classify rice varieties more efficiently and accurately, this study proposes a multimodal l fusion detection method based on an improved voting method. The experiment collected eight common rice seed types. Raytrix light field cameras were used to collect 2D images and 3D point cloud datasets, with a total of 3194 samples. The training and test sets were divided according to an 8:2 ratio. The experiment improved the traditional voting method. First, multiple models were used to predict the rice seed varieties. Then, the predicted probabilities were used as the late fusion input data. Next, a comprehensive score vector was calculated based on the performance of different models. In late fusion, the predicted probabilities from 2D and 3D were jointly weighted to obtain the final predicted probability. Finally, the predicted value with the highest probability was selected as the final value. In the experimental results, after late fusion of the predicted probabilities, the average accuracy rate reached 97.4%. Compared with the single support vector machine (SVM), k-nearest neighbors (kNN), convolutional neural network (CNN), MobileNet, and PointNet, the accuracy rates increased by 4.9%, 8.3%, 18.1%, 8.3%, and 9%, respectively. Among the eight varieties, the recognition accuracy of two rice varieties, Hannuo35 and Yuanhan35, by applying the voting method improved most significantly, from 73.9% and 77.7% in two dimensions to 92.4% and 96.3%, respectively. Thus, the improved voting method can combine the advantages of different data modalities and significantly improve the final prediction results.https://www.mdpi.com/2077-0472/13/3/597rice seedvariety classificationmultimodal fusionmachine visionpoint cloud |
| spellingShingle | Xinyi He Qiyang Cai Xiuguo Zou Hua Li Xuebin Feng Wenqing Yin Yan Qian Multi-Modal Late Fusion Rice Seed Variety Classification Based on an Improved Voting Method Agriculture rice seed variety classification multimodal fusion machine vision point cloud |
| title | Multi-Modal Late Fusion Rice Seed Variety Classification Based on an Improved Voting Method |
| title_full | Multi-Modal Late Fusion Rice Seed Variety Classification Based on an Improved Voting Method |
| title_fullStr | Multi-Modal Late Fusion Rice Seed Variety Classification Based on an Improved Voting Method |
| title_full_unstemmed | Multi-Modal Late Fusion Rice Seed Variety Classification Based on an Improved Voting Method |
| title_short | Multi-Modal Late Fusion Rice Seed Variety Classification Based on an Improved Voting Method |
| title_sort | multi modal late fusion rice seed variety classification based on an improved voting method |
| topic | rice seed variety classification multimodal fusion machine vision point cloud |
| url | https://www.mdpi.com/2077-0472/13/3/597 |
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