Journal ID : AMA-07-12-2021-10925
[This article belongs to Volume - 52, Issue - 03]
Total View : 365

Title : Development of a monitoring system for grain loss of paddy rice based on a Logistic Model Tree algorithm

Abstract :

China has the world’s largest planting area of paddy rice, and its production of paddy rice is also the first in the world. In China, large quantities of paddy rice fall to the ground and are lost during harvesting with a combine harvester. Decreasing this grain loss is an effective way to increase production and revenue, and scientific studies should focus on this decrease. In this paper, a monitoring system was developed to monitor the grain loss of the paddy rice and this approach was tested on the test bench for precision. The development of the monitoring system for grain loss consisted of two stages: the first stage included collecting impact signals using a piezoelectric film; extracting the four features of Root Mean Square, Peak number, Frequency and Amplitude (fundamental component); and identifying the kernel impact signals using the LMT (Logistic Model Tree) algorithm. In the second stage, the precision of the monitoring system was tested for the paddy rice at three different moisture contents (10.4%, 19.6%, and 30.4%) and five different grain/impurity ratios (1/0.5, 1/1, 1/1.5, 1/2, and 1/2.5). According to our results, the highest monitoring accuracy was 96.7% (moisture content 30.8% and grain/impurity ratio 1/1.5), the average accuracy of the monitoring tests was 89.5%, and monitoring of grain/impurity ratios of 1/1.5 (>92%) had higher accuracy than monitoring the other grain/impurity ratios. Monitoring accuracy decreased as impurities increased. The lowest accuracy for grain loss monitoring was obtained when the grain/impurity ratio was 1/2.5, with monitoring accuracies of 83.6%, 76.8% and 77% and moisture contents of 10.4%, 19.6% and 30.4%.

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