Hybrid Integrated Feature Fusion of Handcrafted and Deep Features for Rice Blast Resistance Identification Using UAV Imagery

Abstract

Nowadays, the combination of UAV remote sensing and deep learning has facilitated effective high-throughput field phenotyping for rice-blast-resistant breeding. However, breeding practices can hardly provide sufficient samples for each category due to the limitations of germplasm resources, which may cause data insufficiency and class imbalance. In addition, foliar and lesion details are often difficult to identify in UAV images due to the limitation of spatial resolution. As a result, the application of deep learning can lead to overfitting, as the model may struggle to acquire discriminative features. While previous studies have attempted to combine handcrafted and deep features to address problems with data insufficiency and class imbalances, image degradation still prevents the network from learning efficient representations for disease identification. To address these issues, this article proposes a hybrid integrated feature fusion (HIFF) method, in which a novel handcrafted-design-guided convolutional neural network module was employed to alleviate the problem of image degradation. Both handcrafted and deep learning branches were integrated in an end-to-end structure and applied to rice blast resistance identification. The proposed method was carefully evaluated using an ablation study, and the comparisons with state-of-the-art deep learning and feature fusion methods were conducted to demonstrate its superiority. Experimental results showed that the HIFF model outperformed mainstream methods by 0.0353 in F1-score and 0.0488 in accuracy on the practical rice-blast-resistant breeding applications. As such, the proposed method could be used to accelerate the process of rice-blast-resistant breeding.