Real-time grain impurity sensing for rice combine harvesters using image processing and decision-tree algorithm

Jin, Chen; Liao, Yi; Li, Yaoming

doi:10.1016/j.compag.2020.105591

Cited by 48 publications

(41 citation statements)

References 26 publications

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Section: Rice Varieties and Farming Area Test Methodsmentioning

confidence: 99%

“…After the test, the quality of stalks on the tarpaulin, the grain contained in the straw, and the unpurified grain were collected manually to obtain the quantity of stalk, the rate of separation loss and the grain damage rate. The data obtained artificially were compared with those collected by the combine harvester by means of online monitoring to get the final degranulation performance data (Chen et al., 2020 ). Design‐Expert software (Stat‐Ease) is used for regression analysis of test data, and the nonsignificant items are eliminated at the significant level when α = 0.05 and the obtained values for simplified regression equations of separation loss rate Y 1 and damage rate Y 2 .…”

Section: Methodsmentioning

confidence: 99%

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Design and performance test of variable diameter threshing drum of combine harvester

Zhao

Tian

et al. 2021

Food Science & Nutrition

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Threshing device is one of the important parts of combine harvesters, whose function determines the working performance, efficiency, and adaptability of the whole machine directly. Threshing gap refers to the clearance between the threshing elements of the drum and the concave grid. During field harvesting, the actual feeding rate shows a great fluctuation due to various biomechanical characteristics and growth density of different varieties of rice, which will deteriorate the threshing and separation performance of combine harvester (Toshikazu and Tatsuro, 2017;Alizadeh and Bagheri, 2009;Singh and Singh, 1981). Therefore, it is necessary to adjust the threshing gap in time according to various working conditions to ensure the operation stability of combine harvesters and improve its harvesting adaptability.Researchers have conducted a lot of studies on the effect of threshing gap on threshing performance, as well as its regulation mode. Scholars have established some basic models of threshing and separation process through comprehensive consideration of multiple factors that exist during the threshing process. They optimized

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Section: Rice Varieties and Farming Area Test Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Design and performance test of variable diameter threshing drum of combine harvester

Zhao

Tian

et al. 2021

Food Science & Nutrition

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“…A standard tree is represented by the J48 algorithm, which consists of a root node, a number of leaf nodes, and a number of branches. Each branch of a tree represents a chain of nodes from the root to a leaf, and each node represents an attribute (or feature) [15][16][17] . Decision trees are one of the most effective and widely used techniques in many areas of Data Mining, such as pattern recognition, machine learning, image processing and information retrieval [18][19][20] .…”

Section: Recognition Using Decision Treementioning

confidence: 99%

Development of a monitoring system for grain loss of paddy rice based on a decision tree algorithm

Liao

Chen

Guan

et al. 2021

International Journal of Agricultural and Biological Engineering

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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%.

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“…is sharp serrated structure can quickly cut stems and leaves; its blade is abrasion resistant and has a certain self-sharpening ability [23][24][25], as shown in Figure 8. To ensure the cutting quality and improve the service life of the machine, the structural parameters should be reasonably designed (i.e., the diameter D, the number of teeth z, the thickness H, the tooth depth h and the edge angle α of the single-disc rotating cutter, and the tooth depth H 1 of the straight fixed cutter).…”

Section: Bionic Design and Parameter Optimization Of Thementioning

confidence: 99%

Bionic Design and Parameter Optimization of Rotating and Fixed Stem- and Leaf-Cutting Devices for Carrot Combine Harvesters

Tang

Jiang

Wang

et al. 2021

Mathematical Problems in Engineering

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To solve the problems of the high rhizome damage and low net cutting during carrot combine harvesting, the following research was performed. The designed carrot stem- and leaf-cutting device was taken as the research object, and a bionic design idea was adopted. MATLAB software was used to extract and optimize the tibial curve of the mantis forelimb, and its shape was applied to the cutting edges of a single-disc rotating cutter and a straight fixed cutter, whose key structural parameters were determined. The kinematic and dynamic models of the cutter, stems, and leaves were established to explore the critical conditions of smooth cutting. A single-factor experiment was performed. The results show that the rhizome damage and the stems and leaf net cutting increased when the rotation speed of the clamping conveyor pulley increased. The flatness of the cutting surface first increased and subsequently decreased. At a speed of 102 r/min, the rhizome damage was 2.86%, the net cutting of stems and leaves was 92.82%, and the flatness of the cutting surface was 85.39%. The net cutting of stems and leaves and the flatness of the cutting surface increased as the disc cutter speed increased, while the rhizome damage decreased. When the rotation speed reached 165 r/min, the rhizome damage, the net cutting of stems and leaves, and the flatness of the cutting surface were 1.97%, 89.59%, and 91.48%, respectively. The bench test and field experiment results showed that the cutting performance of the optimized bionic cutter group is significantly better than that of the conventional knife group. The rhizome damage, net cutting of stems and leaves, and flatness of the cutting surface were 4.01%, 92.05%, and 84.05%, respectively, which meet the agronomic requirements for carrot harvesting.

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Real-time grain impurity sensing for rice combine harvesters using image processing and decision-tree algorithm

Cited by 48 publications

References 26 publications

Design and performance test of variable diameter threshing drum of combine harvester

Design and performance test of variable diameter threshing drum of combine harvester

Development of a monitoring system for grain loss of paddy rice based on a decision tree algorithm

Bionic Design and Parameter Optimization of Rotating and Fixed Stem- and Leaf-Cutting Devices for Carrot Combine Harvesters

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