Real-Time Monitoring System of Seedling Amount in Seedling Box Based on Machine Vision

Jinyang Li (), Miao Zhang, Gong Zhang, Deqiang Ge and Meiqing Li
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Jinyang Li: Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education of the People’s Republic of China, Zhenjiang 212013, China
Miao Zhang: Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education of the People’s Republic of China, Zhenjiang 212013, China
Gong Zhang: Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education of the People’s Republic of China, Zhenjiang 212013, China
Deqiang Ge: Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education of the People’s Republic of China, Zhenjiang 212013, China
Meiqing Li: Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education of the People’s Republic of China, Zhenjiang 212013, China

Agriculture, 2023, vol. 13, issue 2, 1-26

Abstract: Conventional mat-type seedlings are still widely used in autonomous rice transplanters and automatically supplying seedling devices suited to conventional mat-type seedlings is difficult to develop. Thus, an autonomous rice transplanter carries at least one person to load the seedling pieces into the seedling box, which has led to an increase in the labor force and low operational efficiency. To solve this problem from another point of view, a machine vision-based system for the real-time monitoring of the seedling amount in a seedling box is developed. This system aims to achieve the monitoring of the fault of seedlings and seedling amount in the seedling box. According to the real-time and accuracy requirements of the image, the image acquisition platform is designed based on a previously developed autonomous rice transplanter. A camera model was developed and camera parameters for correcting the image distortion is obtained. The image processing method and segment method of seedling rows are presented. The algorithms for fault diagnosis and the calculation of the number of remaining seedlings are proposed by image analysis. The software is developed for seedling box fault diagnosis and monitoring the remaining number of seedlings. Field experiments are carried out to test the effectiveness of the developed monitoring system. The experimental results show that the image processing time is less than 1.5 s and the relative error of the seedling amount is below 3%, which indicates that the designed monitoring system can accurately realize the fault diagnosis of the seedling pieces and monitor for the remaining amount of each row. By combining the navigation information, the developed monitoring system can predict the distance from which the remaining seedlings in the seedling box can be planted, which can guarantee remaining seedlings in a seedling box are enough for transplanting until the rice transplanter returns to the supplying seedling site. This implies that one person can provide seedlings for multiple autonomous rice transplanters. This study was limited to supplying the seedling when the rice transplanter passed through the place of the seedling storage situated at the headland. In the future, we decide to conduct a study on path planning of breakpoint endurance so that the rice transplanter can automatically return to the supplying seedling place when the seedling amount in the seedling box is not enough.

Keywords: autonomous rice transplanter; seedling amount; seedling fault; monitoring system; machine 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
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