Determining Mosaic Resilience in Sugarcane Plants using Hyperspectral Images

TL;DR

This paper presents a novel hyperspectral imaging and deep learning approach to efficiently detect mosaic resilience in sugarcane, significantly improving early diagnosis and management of the disease.

Contribution

Introduces a deep learning-based method using hyperspectral data to accurately identify sugarcane mosaic resilience, surpassing classical techniques.

Findings

Deep learning model achieved high classification accuracy.

Hyperspectral imaging effectively captures spatial and spectral variations.

Method enables early detection of mosaic resilience.

Abstract

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.