Segmentation and Angle Calculation of Rice Lodging during Harvesting by a Combine Harvester

Xiaobo Zhuang () and Yaoming Li
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Xiaobo Zhuang: Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, Jiangsu University, Zhenjiang 212013, China
Yaoming Li: Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, Jiangsu University, Zhenjiang 212013, China

Agriculture, 2023, vol. 13, issue 7, 1-15

Abstract: Rice lodging not only brings trouble to harvesting but also reduces yield. Therefore, the effective identification of rice lodging is of great significance. In this paper, we have designed a bilinear interpolation upsampling feature fusion module (BIFF) to decompose the quadruple upsampling of the connected part of encoder and decoder into two double upsampling processes and insert the intermediate feature layer in the backbone network for feature fusion in this process. The global attention mechanism(GAM) attention module is added to the feature extraction network, allowing the network to effectively focus on the lodging regions, thus effectively improving the segmentation effect. The average accuracy of the improved network is 93.55%, mrecall is 93.65%, and mIoU is 88.10%, and the feasibility of the improvement is demonstrated by ablation experiments and comparison with other algorithms. In addition, the angle calculation method is designed by combining the detection algorithm, adding a detection head branch to the output results for reading the distance information from the depth camera, and combining the distance information with mechanical analysis to determine the relationship between the angle of the stalk and the vertical direction when the rice is upright, tilted and fallen. A comparison of the calculated angle with the actual measured angle gives a final average error of approximately 5.364%, indicating that the harvest boundary extraction algorithm in this paper is highly accurate and has value for application in real-time harvesting scenarios.

Keywords: deeplabv3+; rice lodging; attention mechanisms; depth of vision (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 references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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