Method of Attention-Based CNN for Weighing Pleurotus eryngii

Junmin Jia, Fei Hu, Xubo Zhang, Zongyou Ben, Yifan Wang and Kunjie Chen ()
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Junmin Jia: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Fei Hu: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Xubo Zhang: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Zongyou Ben: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Yifan Wang: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Kunjie Chen: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China

Agriculture, 2023, vol. 13, issue 9, 1-14

Abstract: Automatic weight detection is an essential step in the factory production of Pleurotus eryngii . In this study, a data set containing 1154 Pleurotus eryngii images was created, and then machine vision technology was used to extract eight two-dimensional features from the images. Because the fruiting bodies of Pleurotus eryngii have different shapes, these features were less correlated with weight. This paper proposed a multidimensional feature derivation method and an Attention-Based CNN model to solve this problem. This study aimed to realize the traditional feature screening task by deep learning algorithms and built an estimation model. Compared with different regression algorithms, the R 2 , RMSE , MAE , and MAPE of the Attention-Based CNN were 0.971, 7.77, 5.69, and 5.87%, respectively, and showed the best performance. Therefore, it can be used as an accurate, objective, and effective method for automatic weight measurements of Pleurotus eryngii .

Keywords: Pleurotus eryngii; weight estimation; machine vision; multidimensional features; convolutional neural network (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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