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Real-time monitoring system for evaluating the operational quality of rice transplanters

文献类型： 外文期刊

作者： He, Lei 1 ; Li, Yongqiang 1 ; An, Xiaofei 2 ; Yao, Hongxun 3 ;

作者机构： 1.Harbin Inst Technol, Sch Instrumentat Sci & Engn, Harbin, Heilongjiang, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Beijing Acad Agr & Forestry Intelligent Equipment, Beijing, Peoples R China

3.Harbin Inst Technol, Sch Comp Sci & Technol, Harbin, Heilongjiang, Peoples R China

关键词： Operational quality of a rice transplanter; Missing seeding rate; Rice seedling tracking; Real-time monitoring system

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2025 年 234 卷

页码：

收录情况： SCI

摘要： Currently, rice transplanters are extensively employed for the mechanized cultivation of rice seedlings. However, few technologies or systems are available to monitor the operational quality parameters, i.e., the number of missing seedlings, row spacing, plant distance, etc., of rice transplanters. The performance of rice transplanters is directly linked to the growth quality of the seedlings and has a crucial effect on the final yield. Therefore, monitoring the various issues that arise during the operation of rice transplanters in a timely and accurate manner to ensure the quality of the transplanting process is particularly important. To address the above issues, this paper develops a real-time monitoring system for rice transplanters. The system architecture includes embedded devices, an image capture module, and a data upload module. A rice seedling detection model based on an enhanced YOLOv5-Lite neural network is developed, and comparative experimental results demonstrate that the proposed model achieves an mAP@0.5 of 81.9 % for rice seedling detection, which is higher than that of the original YOLOv5-Lite model. We additionally propose a RANSAC-based algorithm to detect rice seeding paths in real time, and the rice seeding path detection results are used to determine the row spacing and plant distance. Specifically, a distance mapping algorithm based on triangular transformations is developed to calculate the row spacing and plant distance in a field. We subsequently calculate the number of missing seedlings between adjacent plants on the basis of the spacing between plants in the same row. Furthermore, a rice seedling tracking and counting algorithm based on an improved ByteTrack algorithm is developed to determine the missing seedling rate, as well as the seeding quantity. We integrate the developed algorithms into a real-time monitoring system and test them at Qixing Farm. The experimental results indicate that the monitoring system achieves an accuracy of 99.2 % for seedling quantity counting and an accuracy of 90.3 % for missing rate counting, with a processing speed of 3.95 frames per second.

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