Development of a monitoring system for grain loss of paddy rice based on a decision tree algorithm - Lian, Yi; Chen, Jin; Guan, Zhuohuai; Song, Jie (International Journal of Agricultural and Biological Engineering, 2021)
China has the world’s largest planting area of paddy rice, but large quantities of paddy rice fall to the ground and are lost during harvesting with a combine harvester. Reducing grain loss is an effective way to increase production and revenue. In this study, a monitoring system was developed to monitor the grain loss of the paddy rice and this approach was tested on the test bench for verifying the precision. The development of the monitoring system for grain loss included two stages: the first stage was to collect impact signals using a piezoelectric film, extract the four features of Root Mean Square, Peak number, Frequency and Amplitude (fundamental component), and identify the kernel impact signals using the J48 (C4.5) Decision 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 the results, the highest monitoring accuracy was 99.3% (moisture content 30.8% and grain/impurity ratio 1/2.5), the average accuracy of the monitoring tests was 92.6%, and monitoring of grain/impurity ratios between 1/1 and 1/1.5 (>95.4%) 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 88.2%, 75.7% and 78.8% at moisture contents of 10.4%, 19.6% and 30.4%.Keywords: monitoring system, combine harvester, paddy rice, grain loss, sensor, data mining, decision tree, developmentDOI: 10.25165/j.ijabe.20211401.5731Citation: Lian Y, Chen J, Guan Z H, Song J. Development of a monitoring system for grain loss of paddy rice based on a decision tree algorithm. Int J Agric & Biol Eng, 2021; 14(1): 224–229.