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Development and test of on-line monitoring system for rice harvester operation quality
*
Man Chen, Chengqian Jin, Tengxiang Yang, Guangyue Zhang, Youliang Ni
Journal of Intelligent Agricultural Mechanization (in Chinese and English) 2020, 1 (2): 26-33. DOI:
10.12398/j.issn.2096-7217.2020.02.004
Abstract
(
189
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45
)
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Aiming at the lack of on-line monitoring system of rice harvester's crushing rate, impurities rate and loss rate, this paper constructs an on-line monitoring system of rice harvester's operation quality. The GPS module in the system can realize the real-time monitoring of field operation speed and operation position. On-line detection devices are used to monitor the crushing rate, impurities rate and the loss rate during field operations. Each function module realizes data communication with a man-machine interaction system through CAN bus. A field experiment was carried out to verify the accuracy of the system. The results show that the accuracy of the on-line monitoring system for the rice harvester operation quality is 82.76% for crushing rate, 78.69% for impurities rate, and 73.53% for loss rate. In the field test, when the manual test results of operation quality increase, the system test results increase correspondingly, and when the manual test results decrease, the system test results decrease correspondingly. Therefore, the detection results of manual and system on the change trend of operation quality are consistent. Therefore, the on-line monitoring system of operation quality of rice harvester constructed in this paper can realize visual monitoring, give an alarm in time when the working quality of harvester becomes worse and provide powerful technical support for intelligent rice harvester and research on adaptive control strategy.
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Nondestructive determination of potato noodle hardness using hyperspectral imaging technology Nondestructive determination of potato noodle hardness using hyperspectral imaging technology
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Zhishang Ren, Guangyao Zhang, Juan Du, Xiang Yin, Chengqian Jin, Chengye Ma
Journal of Intelligent Agricultural Mechanization (in Chinese and English) 2020, 1 (1): 44-48. DOI:
10.12398/j.issn.2096-7217.2020.01.006
Abstract
(
194
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The texture is an important index that indicates the noodle quality. In this research, the relationship between hardness and texture with respect to potato noodles was demonstrated by using 108 noodle samples with different amounts of potatoes. Noodle images would be captured by hyperspectral imaging technology and textures would be analyzed during that process. A calibration and cross-validation model were established based on the least squares regression method. 72 noodles as training samples were used to establish the model and the other 36 samples were used to verify the calibration model. The coefficient of determination (
R
2
C
and
R
2
cv
) values of the calibration model and cross-validation model were 0.877 and 0.842 while the root mean square error of calibration (RMSEC) and root mean square error of cross-validation (RMSECV) values were 11.405 and 13.166. The coefficient of determination (
R
2
P
) of the prediction set was 0.988 and the root mean square error of prediction (RMSEP) was 3.585. Results showed that the proposed determination method of potato noodle hardness was of high robustness and stability and could be used as a non-destructive detection technology for detecting the noodle hardness.
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