Enhanced Leaf Area Index Estimation With CROP-DualGAN Network

Quantitative estimation of regional leaf area index (LAI) is an important basis for large-scale crop growth monitoring and yield estimation. With the development of deep learning, theoretically, the use of neural networks can effectively improve the accuracy of LAI estimation, but sufficient training samples are often required due to a large number of network parameters. In an actual regional LAI quantitative estimation, there are only a few samples, which is difficult to train in networks. Therefore, a crop dual-learning generative adversarial network (CROP-DualGAN) was proposed in this article for data enhancement of small samples to estimate regional LAI. The method uses dual learning to generate hyperspectral reflectance and corresponding LAI, including two groups of generative adversarial networks, in which the generator is used to generate data that conforms to the distribution of the training set, and the discriminator is used to judge the true or false generated samples. The generators and discriminators are constantly optimized in the confrontation so that the distribution of generated data is closer to that of training samples. In single crop type experiments, 30 training samples with enhanced in VGG16 achieved the
$R^{2}$
of cereal, maize, and rape seed as 0.921, 0.990, and 0.956, and in SSLLAI-Net achieved the
$R^{2}$
of cereal, maize, and rape seed as 0.971, 0.991 and 0.962. In multiple crop types experiments, the result is lower than individual crop estimation, but higher than that without enhancement. Finally, a non-parametric test is used to prove that most improvement in LAI estimation is significant, and the accuracy would not decrease when improvement is not significant. In all, the proposed method is universal and can effectively help benchmark models to improve regional LAI estimation accuracy with neural networks.