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Existing Deep Neural Nets on crops growth prediction mostly rely onavailability of a large amount of data. In practice, it is difficult to collectenough high-quality data to utilize the full potential of these deep learningmodels. In this paper, we construct an innovative network architecture based ondomain adaptation learning to predict crops growth curves with limitedavailable crop data. This network architecture overcomes the challenge of dataavailability by incorporating generated data from the developed cropssimulation model. We are the first to use the temporal convolution filters asthe backbone to construct a domain adaptation network architecture which issuitable for deep learning regression models with very limited training data ofthe target domain. We conduct experiments to test the performance of thenetwork and compare our proposed architecture with other state-of-the-artmethods, including a recent LSTM-based domain adaptation network architecture.The results show that the proposed temporal convolution-based networkarchitecture outperforms all benchmarks not only in accuracy but also in modelsize and convergence rate.

Temporal Convolution Domain Adaptation Learning for Crops Growth Prediction