Artificial Neural Network for Assessment of Grain Losses for Paddy Combine Harvester a Novel Approach

Hiregoudar, Sharanagouda; Udhaykumar, R.; Ramappa, K. T.; Shreshta, Bijay; Meda, Venkatesh; Anantachar, M.

doi:10.1007/978-3-642-19263-0_27

Cited by 14 publications

(7 citation statements)

References 5 publications

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“…Four pressure sensors were mounted on the cleaning section to monitor the sieve load [11][12][13] . An artificial neural network was introduced to assess the grain losses in the field condition and the simulation result showed that the ANN method was appropriate and feasible to assess the grain losses [14] .…”

Section: Introduction mentioning

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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Section: Introduction mentioning

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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“…Some scientific research institutes and universities, both local and abroad, have performed research on the biomechanical properties of maize and ear picking related injury (Martin et al 1987;Hiregoudar et al 2011;Cook et al 2014;Saini et al 2015;Cheng et al 2016;Yang et al 2016;Aguayo et al 2017;Fu et al 2019Fu et al , 2020. Balastreire et al (1982) accurately measured the critical value of the fracture toughness of maize.…”

Section: Introductionmentioning

confidence: 99%

Simulation of a maize ear picking device with a longitudinal horizontal roller based on hypermesh modeling

Jianjian

Guan

et al. 2021

BioRes

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As a key component of mechanized maize harvesting, the performance of the ear picking device directly affects the operational level of a maize harvester. Because the short harvest period of maize and the multiple experimental factors that lead to the field and bench tests cannot meet the actual research and developmental needs of manufacturing a maize harvester, this paper uses virtual prototype technology to study the influence of design and the motion parameters of a longitudinal horizontal roller in terms of picking performance. Based on the establishment of the finite element model of the corn and the ear picking device, this paper establishes evaluation indicators respectively, analyzes and evaluates the influence of the picking roller and the ground inclination angle, the picking roller speed, and the forward speed of the harvester on the performance of ear picking. Finally, a virtual orthogonal test analysis was carried out for the two factors of the picking roller speed and the forward speed of the harvester, and the significant influence of the picking roller speed and the forward speed of the harvester on the performance of the picking was compared. The research results of this paper can provide reference for optimizing the design of ear picking mechanism.

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“…In order to improve the cleaning efficiency of high impurity content, Craessaerts et al, (2010) developed a fuzzy control strategy by installing wind speed sensors and pressure sensors on the cleaning sieve, which could improve the detection and prediction of the cleaning loss status [9]. At the same time, Hiregoudar et al, (2011) developed a grain detection model for high impurity content cleaning process by artificial intelligence and neural networks [10]. The fuzzy logic control model for the combine harvester's cleaning system was a mature method and technique that can predict and control the cleaning loss rate [11].…”

Section: Introductionmentioning

confidence: 99%

Controlling Rice Leaf Breaking Force by Temperature and Moisture Content to Reduce Breakage

et al. 2020

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Leaf fragments and grain mixture produced by rice threshing with a combine harvester seriously affects the subsequent grain cleaning efficiency. In this paper, rice leaf breaking force was tested at different temperatures and moisture contents to analyze the influence of temperature on the tensile properties of rice leaves. The overlapping regions of rice leaf breaking force and grain separation force at different temperatures were obtained. Based on the rice leaf breaking force, the effect of the temperature change on rice leaf with different moisture content was analyzed. The results showed that tensile strength of rice leaf decreased first, then increased and finally decreased from rice sheath to the top. The tensile breaking force of the leaf sheath was about 75 N (N is Newton, 1 N = 1 kg·m/s²). The tensile fracture resistance of rice leaf was shown to weaken with the increase of temperature. The influence of the moisture content on the mechanical properties of rice leaf during the process of temperature change was small. At 30 to 35 °C, the blade was the strongest tensile fracture resistance, which was the lowest probability of fracture under the same stress condition. At this time, the rice grain separation force has the least coincidence with the rice leaf resistance to breakage. According to the breaking force of different blade layers, the number of blade layers has a significant linear relationship with its tensile limit. Therefore, the overall fracture resistance of the multilayer blade can be improved by controlling the temperature and moisture content.

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Artificial Neural Network for Assessment of Grain Losses for Paddy Combine Harvester a Novel Approach

Cited by 14 publications

References 5 publications

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

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

Simulation of a maize ear picking device with a longitudinal horizontal roller based on hypermesh modeling

Controlling Rice Leaf Breaking Force by Temperature and Moisture Content to Reduce Breakage

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