Determining Mosaic Resilience in Sugarcane Plants using Hyperspectral Images
This work addresses the threat of sugarcane mosaic disease to the Australian sugarcane industry, which causes up to 30% yield losses, by providing an efficient detection method, though it is incremental as it applies existing deep learning techniques to a new domain.
This study tackled the problem of detecting mosaic disease resilience in sugarcane plants by using hyperspectral imaging and a ResNet18 deep learning model, achieving high classification accuracy compared to classical methods like Support Vector Machines.
Sugarcane mosaic disease poses a serious threat to the Australian sugarcane industry, leading to yield losses of up to 30% in susceptible varieties. Existing manual inspection methods for detecting mosaic resilience are inefficient and impractical for large-scale application. This study introduces a novel approach using hyperspectral imaging and machine learning to detect mosaic resilience by leveraging global feature representation from local spectral patches. Hyperspectral data were collected from eight sugarcane varieties under controlled and field conditions. Local spectral patches were analyzed to capture spatial and spectral variations, which were then aggregated into global feature representations using a ResNet18 deep learning architecture. While classical methods like Support Vector Machines struggled to utilize spatial-spectral relationships effectively, the deep learning model achieved high classification accuracy, demonstrating its capacity to identify mosaic resilience from fine-grained hyperspectral data. This approach enhances early detection capabilities, enabling more efficient management of susceptible strains and contributing to sustainable sugarcane production.