Multi-Modal Late Fusion Rice Seed Variety Classification Based on an Improved Voting Method

Xinyi He, Qiyang Cai, Xiuguo Zou, Hua Li, Xuebin Feng, Wenqing Yin and Yan Qian ()
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Xinyi He: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Qiyang Cai: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Xiuguo Zou: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Hua Li: College of Engineering, Nanjing Agriculture University, Nanjing 210031, China
Xuebin Feng: College of Engineering, Nanjing Agriculture University, Nanjing 210031, China
Wenqing Yin: College of Engineering, Nanjing Agriculture University, Nanjing 210031, China
Yan Qian: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China

Agriculture, 2023, vol. 13, issue 3, 1-16

Abstract: 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.

Keywords: rice seed; variety classification; multimodal fusion; machine vision; point cloud (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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