Identification Method of Rice Seedlings Rows Based on Gaussian Heatmap

Rongru He, Xiwen Luo, Zhigang Zhang (), Wenyu Zhang, Chunyu Jiang and Bingxuan Yuan
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Rongru He: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Xiwen Luo: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Zhigang Zhang: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Wenyu Zhang: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Chunyu Jiang: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Bingxuan Yuan: College of Engineering, South China Agricultural University, Guangzhou 510642, China

Agriculture, 2022, vol. 12, issue 10, 1-17

Abstract: The identification method of rice seedling rows based on machine vision is affected by environmental factors that decrease the accuracy and the robustness of the rice seedling row identification algorithm (e.g., ambient light transformation, similarity of weed and rice features, and lack of seedlings in rice rows). To solve the problem of the above environmental factors, a Gaussian Heatmap-based method is proposed for rice seedling row identification in this study. The proposed method is a CNN model that comprises the High-Resolution Convolution Module of the feature extraction model and the Gaussian Heatmap of the regression module of key points. The CNN model is guided using Gaussian Heatmap generated by the continuity of rice row growth and the distribution characteristics of rice in rice rows to learn the distribution characteristics of rice seedling rows in the training process, and the positions of the coordinates of the respective key point are accurately returned through the regression module. For the three rice scenarios (including normal scene, missing seedling scene and weed scene), the PCK and average pixel offset of the model were 94.33%, 91.48%, 94.36% and 3.09, 3.13 and 3.05 pixels, respectively, for the proposed method, and the forward inference speed of the model reached 22 FPS, which can meet the real-time requirements and accuracy of agricultural machinery in field management.

Keywords: recognition of rice seedling rows; Gaussian Heatmap; CNN Model; key points (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: 2022
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