Temporal Convolution Domain Adaptation Learning for Crops Growth Prediction

TL;DR

This paper introduces a novel temporal convolution-based domain adaptation network for crop growth prediction, effectively handling limited data by integrating simulated data, and outperforming existing methods in accuracy and efficiency.

Contribution

It presents the first use of temporal convolution filters in a domain adaptation network for crop growth regression with limited data.

Findings

Outperforms state-of-the-art methods in accuracy

Achieves faster convergence and smaller model size

Effectively utilizes simulated data to enhance prediction

Abstract

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