# Raw Material Flow Rate Measurement on Belt Conveyor System Using Visual Data
## 5 Conclusions  
This paper proposed a comprehensive solution for determining the flow rate of the raw material on the belt conveyor system installed in a Soda-Ash manufacturing plant at LCI Khewra, Pakistan. The flow rate helps us choose the optimal setting for the coke, which is transferred on the conveyor belt through a variable frequency drive. The optimal setting for coke results in optimal gas production and less production waste, which can help save expenditures. We aim to eliminate manual effort by taking the help of an optical lens, making the whole process automatic and making it more efficient and less prone to error.  
Since the belt is mounted in an outdoor environment, using an optical lens involved several challenges. However, each part of the algorithm is resilient enough to deal with each type of situation. First, the raw materials are extracted from the conveyor belt by extracting the region of interest, and then segmentation is performed to separate the material from the conveyor belt. The exact position of the conveyor belt and the stable structure on which the camera is placed makes it easy to extract the region of interest. A novel technique was proposed based on background subtraction. The algorithm combines the sequence history and recent changes to extract the foreground region effectively. After the raw material was segmented, different features were computed from the segmented regions, such as moments, color, and information regarding the area of each material. Since each video sequence in the dataset was of variable length, a window technique was used to capture all the information in a fixed-size window which can be further used as an input to the machine learning algorithm. Aggregation was also performed to capture all the information in fewer frames, which helps in reducing complexity. Complexity was also reduced by employing a feature selection technique and testing the model's performance.  
The extracted features were transformed and given as input to different machine learning algorithms. Since we have regression problems, several well-known regression algorithms were used. In terms of model performance, the linear models, namely Gamma regressor, Bayesian Ridge regressor, and Theil-Sen regressor, produced the best results without the feature selection technique. Whereas the Decision Trees, Random Forest performed well with the feature selection technique. Furthermore, the boosting methods such as XGBoost, and Gradient Boosting regressor methods have shown mixed results. The Gradient Boosting algorithm works well with and without feature selection technique, and it also produced the best mean squared error and root mean squared error value for the final run. The techniques were tested with k-fold and leave-one-out cross-validation to maintain the bias-variance trade-off.  
The future work includes improving the environment by either developing infrastructure on top of the conveyor belt or installing a high-end optical lens to deal with environmental factors. In addition, the data captured weekly is also being added to the dataset, which may impact the algorithm's performance; thus, a pipeline needs to be implemented to monitor the model's performance continuously, and further research can be conducted to optimize the current solution or modify it to cater to the new data.  
Conceptualization, M.S., M.E. and M.H., methodology, M.S., M.S.F., M.E. and M.H.K.; software, M.S., M.S.F, M.E. and M.H.K.; investigation, M.S., M.S.F, M.H., M.H.K. and U.F.; writing--original draft preparation, M.S. and M.S.F., writing--review and editing, M.S., M.S.F. and M.H.K.; visualization, M.S., M.S.F. and U.F., supervision, M.S.F., M.H.K. and U.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.