Nie Jing, Wang Nianyi, Li Jingbin, Wang Kang, Wang Hongkun
College of Mechanical and Electrical Engineering, Shihezi University, Xinjiang, China.
Key Laboratory of Modern Agricultural Machinery of Xinjiang Production and Construction Corps, Shihezi, China.
Plant Methods. 2021 Nov 24;17(1):119. doi: 10.1186/s13007-021-00818-2.
Due to the high cost of data collection for magnetization detection of media, the sample size is limited, it is not suitable to use deep learning method to predict its change trend. The prediction of physical and chemical properties of magnetized water and fertilizer (PCPMWF) by meta-learning can help to explore the effects of magnetized water and fertilizer irrigation on crops.
In this article, we propose a meta-learning optimization model based on the meta-learner LSTM in the field of regression prediction of PCPMWF. In meta-learning, LSTM is used to replace MAML's gradient descent optimizer for regression tasks, enables the meta-learner to learn the update rules of the LSTM, and apply it to update the parameters of the model. The proposed method is compared with the experimental results of MAML and LSTM to verify the feasibility and correctness.
The average absolute percentage error of the meta-learning optimization model of meta-learner LSTM is reduced by 0.37% compared with the MAML model, and by 4.16% compared with the LSTM model. The loss value of the meta-learning optimization model in the iterative process drops the fastest and steadily compared to the MAML model and the LSTM model. In cross-domain experiments, the average accuracy of the meta-learning optimized model can still reach 0.833.
In the case of few sample, the proposed model is superior to the traditional LSTM model and the basic MAML model. And in the training of cross-domain datasets, this model performs best.
由于介质磁化检测的数据采集成本高昂，样本量有限，不适宜采用深度学习方法来预测其变化趋势。通过元学习对磁化水肥物理化学性质（PCPMWF）进行预测，有助于探究磁化水肥灌溉对农作物的影响。
在本文中，我们针对PCPMWF回归预测领域，提出了一种基于元学习器长短期记忆网络（LSTM）的元学习优化模型。在元学习中，LSTM被用于替代MAML的梯度下降优化器来执行回归任务，使元学习器能够学习LSTM的更新规则，并将其应用于更新模型参数。将所提方法与MAML和LSTM的实验结果进行比较，以验证其可行性和正确性。
元学习器LSTM的元学习优化模型的平均绝对百分比误差，与MAML模型相比降低了0.37%，与LSTM模型相比降低了4.16%。在迭代过程中，元学习优化模型的损失值相较于MAML模型和LSTM模型下降得最快且最为稳定。在跨域实验中，元学习优化模型的平均准确率仍能达到0.833。
在样本较少的情况下，所提模型优于传统的LSTM模型和基本的MAML模型。并且在跨域数据集的训练中，该模型表现最佳。
