An Image-Based Rice Weighing Estimation Approach on Clock Type Weighing Scale Using Deep Learning and Geometric Transformations

An Cong Tran; Thanh Trinh Thi Kim; Hai Thanh Nguyen

doi:10.46604/aiti.2023.10926

Authors

An Cong Tran College of Information and Communication Technology, Can Tho University, Can Tho, Vietnam
Thanh Trinh Thi Kim College of Information and Communication Technology, Can Tho University, Can Tho, Vietnam
Hai Thanh Nguyen College of Information and Communication Technology, Can Tho University, Can Tho, Vietnam

DOI:

https://doi.org/10.46604/aiti.2023.10926

Keywords:

scale detection, scale value recognition, rice weighing, geometric transformations, deep learning

Abstract

AI impacts surrounding human life, such as the economy, health, education, and agricultural production; however, the crop prices in the harvest season are still on manual calculation, which causes doubts about accuracy. In this study, an image-based approach is proposed to help farmers calculate rice prices more accurately. YOLOv5 is used to detect and extract the scales in the images taken from the harvesting of rice crops. Then, various image processing techniques, such as brightness balance, background removal, etc., are compiled to determine the needle position and number on the extracted scale. Lastly, geometric transformations are proposed to calculate the weight. A real dataset of 709 images is used for the experiment. The proposed method achieves good results in terms of mAP@0.5 at 0.995, mAP@[0.5:0.95] at 0.830 for scale detection, and MAE at 3.7 for weight calculation.

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