Ru Mengfei, Feng Qingchun, Sun Na, Li Yajun, Sun Jiahui, Li Jianxun, Zhao Chunjiang
College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China.
Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing 100097, China.
Foods. 2024 Jul 9;13(14):2166. doi: 10.3390/foods13142166.
Tomatoes are prone to mechanical damage due to improper gripping forces during automated harvest and postharvest processes. To reduce this damage, a dynamic viscoelastic model based on long short-term memory (LSTM) is proposed to fit the dynamic compression stress relaxation characteristics of the individual fruit. Furthermore, the classical stress relaxation models involved, the triple-element Maxwell and Caputo fractional derivative models, are compared with the LSTM model to validate its performance. Meanwhile, the LSTM and classical stress relaxation models are used to predict the stress relaxation characteristics of tomato fruit with different fruit sizes and compression positions. The results for the whole test dataset show that the LSTM model achieves a RMSE of 2.829×10-5 Mpa and a MAPE of 0.228%. It significantly outperforms the Caputo fractional derivative model by demonstrating a substantial enhancement with a 37% decrease in RMSE and a 36% reduction in MAPE. Further analysis of individual tomato fruit reveals the LSTM model's performance, with the minimum RMSE recorded at the septum position being 3.438×10-5 Mpa, 31% higher than the maximum RMSE at the locule position. Similarly, the lowest MAPE at the septum stands at 0.375%, outperforming the highest MAPE at the locule position by a significant margin of 90%. Moreover, the LSTM model consistently reports the smallest discrepancies between the predicted and observed values compared to classical stress relaxation models. This accuracy suggests that the LSTM model could effectively supplant classical stress relaxation models for predicting stress relaxation changes in individual tomato fruit.
在自动化收获和采后处理过程中，由于夹持力不当，番茄容易受到机械损伤。为了减少这种损伤，提出了一种基于长短期记忆网络（LSTM）的动态粘弹性模型，以拟合单个果实的动态压缩应力松弛特性。此外，将所涉及的经典应力松弛模型，即三元麦克斯韦模型和卡普托分数阶导数模型，与LSTM模型进行比较，以验证其性能。同时，利用LSTM模型和经典应力松弛模型预测不同果实大小和压缩位置的番茄果实的应力松弛特性。整个测试数据集的结果表明，LSTM模型的均方根误差（RMSE）为2.829×10-5 Mpa，平均绝对百分比误差（MAPE）为0.228%。它显著优于卡普托分数阶导数模型，RMSE降低了37%，MAPE降低了36%，有了实质性的提高。对单个番茄果实的进一步分析揭示了LSTM模型的性能，隔膜位置记录的最小RMSE为3.438×10-5 Mpa，比心皮位置的最大RMSE高31%。同样，隔膜位置的最低MAPE为0.375%，比心皮位置的最高MAPE显著高出90%。此外，与经典应力松弛模型相比，LSTM模型预测值与观测值之间的差异始终最小。这种准确性表明，LSTM模型可以有效地取代经典应力松弛模型，用于预测单个番茄果实的应力松弛变化。
