Timely monitoring and precise estimation of physicochemical properties, such as pH, total soluble solids (TSS), and firmness, are crucial for assessing the quality and ripeness of strawberries. Therefore, this study examined the application of convolutional neural network (CNN)-regression models for predicting pH, TSS, and firmness of strawberries based on image data captured by RGB camera. Three CNN architectures, namely a typical single branch convolutional neural network (CNNtl), a parallel convolutional neural network (CNNpl), and a series convolutional neural network (CNNsl) architectures were developed, and their performance were compared. To develop these models, 600 fruits in six different ripening stages were collected and indexed for enabling the measurement of pH, TSS, and firmness levels, as well as the acquisition of images. Through statistical analysis, significant correlations were obtained among pH, TSS, and firmness in strawberries, suggesting valuable insights into the physicochemical changes that occurred during the ripening process. The pH and TSS levels exhibited a continuous increase from the early to late ripening stages, while fruit firmness significantly decreased throughout the ripening process. Among the tested models, CNNsl outperformed CNNtl and CNNpl in predicting the physicochemical properties of strawberries, which precisely explained the relationship between the image data and the targeted properties. For pH prediction, CNNsl achieved an R2 greater than 0.74 and an RMSE below 0.20. The CNNsl model demonstrated better performance in predicting TSS, with a 9.65% increase in R2 and reductions of 14.34% and 14.51% in RMSE and MAE, respectively, compared to the CNNtl model. Furthermore, the CNNsl architecture achieved the best results for firmness prediction, with an increase inss R2 of 2.74% and 6.92%, and reductions of 9.13% and 16.38% in RMSE, and 8.34% and 15.33% in MAE, compared to the CNNpl and CNNtl models, respectively. The consistency assessment of these models indicated that CNNsl exhibited the highest consistency among the tested models with minimal decreases in R2 and small increases in RMSE and MAE, followed by CNNpl and CNNtl. However, in terms of detection speeds, CNNtl required the shortest prediction time compared to CNNpl and CNNsl. Overall, this study demonstrated the potential of CNN-regression models in precisely predicting the physicochemical properties of strawberries based on image data. The findings may contribute valuable insights in determining physicochemical characteristics of strawberries, emphasizing the importance of advanced deep learning techniques in agricultural applications.

Prediction of physicochemical properties of strawberry fruits using convolutional neural network-regression models

期刊论文
发表日期：

2025年5月21日

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Jayanta Kumar Basak,
Bhola Paudel,
Myeong Yong Kang,
Sijan Karki,
Tapash Kumar Sarkar,
Niraj Tamrakar,
Byeong Eun Moon,
Hyeon Tae Kim

摘要

Timely monitoring and precise estimation of physicochemical properties, such as pH, total soluble solids (TSS), and firmness, are crucial for assessing the quality and ripeness of strawberries. Therefore, this study examined the application of convolutional neural network (CNN)-regression models for predicting pH, TSS, and firmness of strawberries based on image data captured by RGB camera. Three CNN architectures, namely a typical single branch convolutional neural network (CNN_tl), a parallel convolutional neural network (CNN_pl), and a series convolutional neural network (CNN_sl) architectures were developed, and their performance were compared. To develop these models, 600 fruits in six different ripening stages were collected and indexed for enabling the measurement of pH, TSS, and firmness levels, as well as the acquisition of images. Through statistical analysis, significant correlations were obtained among pH, TSS, and firmness in strawberries, suggesting valuable insights into the physicochemical changes that occurred during the ripening process. The pH and TSS levels exhibited a continuous increase from the early to late ripening stages, while fruit firmness significantly decreased throughout the ripening process. Among the tested models, CNN_sl outperformed CNN_tl and CNN_pl in predicting the physicochemical properties of strawberries, which precisely explained the relationship between the image data and the targeted properties. For pH prediction, CNN_sl achieved an R² greater than 0.74 and an RMSE below 0.20. The CNN_sl model demonstrated better performance in predicting TSS, with a 9.65% increase in R² and reductions of 14.34% and 14.51% in RMSE and MAE, respectively, compared to the CNN_tl model. Furthermore, the CNN_sl architecture achieved the best results for firmness prediction, with an increase inss R² of 2.74% and 6.92%, and reductions of 9.13% and 16.38% in RMSE, and 8.34% and 15.33% in MAE, compared to the CNN_pl and CNN_tl models, respectively. The consistency assessment of these models indicated that CNN_sl exhibited the highest consistency among the tested models with minimal decreases in R² and small increases in RMSE and MAE, followed by CNN_pl and CNN_tl. However, in terms of detection speeds, CNN_tl required the shortest prediction time compared to CNN_pl and CNN_sl. Overall, this study demonstrated the potential of CNN-regression models in precisely predicting the physicochemical properties of strawberries based on image data. The findings may contribute valuable insights in determining physicochemical characteristics of strawberries, emphasizing the importance of advanced deep learning techniques in agricultural applications.