Estimation of separation of Black-eyed pea with gravity separator table using Random Forest optimized by Genetic algorithm

Separation (SP) is an undeniable member in the process set after harvesting of bulk products. The gravity tableseparator machine (GTSM) is one of the devices used to separate modal impurities in grain masses. Due to the continuity in the range of changes in the parameters of the GTSM and the high number of these factors affecting the rate of SP of impurities in the mass of cowpea beans, and considering that it seems almost impossible to examine all the values in this range. The use of machine learning (ML) to predict the process of SP against the changes applied to these factors facilitates the use of the GTSM for black-eyed pea (BeP). The present study is about predicting the performance of a GTSM in separating the BeP. The dependent variables included the cleaned seeds (Y1), weight of the cleaned seeds (Y2), total gross number (Y3), total gross weight (Y4), rotten seeds number (Y5), rotten seeds weight (Y6), broken seeds number (Y7) and broken seeds weight (Y8) and the independent variables included transverse slopes of the table (X1), longitudinal slopes of the table (X2), frequencies of table oscillation (X3) and blower air speeds (X4). The employed methods were single Random forest (RF) and a hybrid Random forest integrated by Genetic algorithm (RF-GA) for optimization of RF parameters. Results were evaluated using correlation coefficient (CC), Scattered Index (SI) and Willmott’s Index (WI). According to findings, hybrid method provided higher performance compared with that for the single method and increased the prediction performance, successfully.
Separation (SP) is an undeniable member in the process set after harvesting of bulk products. The gravity tableseparator machine (GTSM) is one of the devices used to separate modal impurities in grain masses. Due to the continuity in the range of changes in the parameters of the GTSM and the high number of these factors affecting the rate of SP of impurities in the mass of cowpea beans, and considering that it seems almost impossible to examine all the values in this range. The use of machine learning (ML) to predict the process of SP against the changes applied to these factors facilitates the use of the GTSM for black-eyed pea (BeP). The present study is about predicting the performance of a GTSM in separating the BeP. The dependent variables included the cleaned seeds (Y1), weight of the cleaned seeds (Y2), total gross number (Y3), total gross weight (Y4), rotten seeds number (Y5), rotten seeds weight (Y6), broken seeds number (Y7) and broken seeds weight (Y8) and the independent variables included transverse slopes of the table (X1), longitudinal slopes of the table (X2), frequencies of table oscillation (X3) and blower air speeds (X4). The employed methods were single Random forest (RF) and a hybrid Random forest integrated by Genetic algorithm (RF-GA) for optimization of RF parameters. Results were evaluated using correlation coefficient (CC), Scattered Index (SI) and Willmott’s Index (WI). According to findings, hybrid method provided higher performance compared with that for the single method and increased the prediction performance, successfully.
Separation (SP) is an undeniable member in the process set after harvesting of bulk products. The gravity tableseparator machine (GTSM) is one of the devices used to separate modal impurities in grain masses. Due to the continuity in the range of changes in the parameters of the GTSM and the high number of these factors affecting the rate of SP of impurities in the mass of cowpea beans, and considering that it seems almost impossible to examine all the values in this range. The use of machine learning (ML) to predict the process of SP against the changes applied to these factors facilitates the use of the GTSM for black-eyed pea (BeP). The present study is about predicting the performance of a GTSM in separating the BeP. The dependent variables included the cleaned seeds (Y1), weight of the cleaned seeds (Y2), total gross number (Y3), total gross weight (Y4), rotten seeds number (Y5), rotten seeds weight (Y6), broken seeds number (Y7) and broken seeds weight (Y8) and the independent variables included transverse slopes of the table (X1), longitudinal slopes of the table (X2), frequencies of table oscillation (X3) and blower air speeds (X4). The employed methods were single Random forest (RF) and a hybrid Random forest integrated by Genetic algorithm (RF-GA) for optimization of RF parameters. Results were evaluated using correlation coefficient (CC), Scattered Index (SI) and Willmott’s Index (WI). According to findings, hybrid method provided higher performance compared with that for the single method and increased the prediction performance, successfully.
Separation (SP) is an undeniable member in the process set after harvesting of bulk products. The gravity tableseparator machine (GTSM) is one of the devices used to separate modal impurities in grain masses. Due to the continuity in the range of changes in the parameters of the GTSM and the high number of these factors affecting the rate of SP of impurities in the mass of cowpea beans, and considering that it seems almost impossible to examine all the values in this range. The use of machine learning (ML) to predict the process of SP against the changes applied to these factors facilitates the use of the GTSM for black-eyed pea (BeP). The present study is about predicting the performance of a GTSM in separating the BeP. The dependent variables included the cleaned seeds (Y1), weight of the cleaned seeds (Y2), total gross number (Y3), total gross weight (Y4), rotten seeds number (Y5), rotten seeds weight (Y6), broken seeds number (Y7) and broken seeds weight (Y8) and the independent variables included transverse slopes of the table (X1), longitudinal slopes of the table (X2), frequencies of table oscillation (X3) and blower air speeds (X4). The employed methods were single Random forest (RF) and a hybrid Random forest integrated by Genetic algorithm (RF-GA) for optimization of RF parameters. Results were evaluated using correlation coefficient (CC), Scattered Index (SI) and Willmott’s Index (WI). According to findings, hybrid method provided higher performance compared with that for the single method and increased the prediction performance, successfully.