用高光谱成像+深度学习识别甘蔗抗花叶病能力，提升早期检测效率
Determining Mosaic Resilience in Sugarcane Plants using Hyperspectral Images
- 通过局部光谱块提取空间-光谱特征，构建全局表示
- 深度模型分类准确率显著高于传统方法，可区分抗病品种
- 适合农业科研与大规模病害监测，助力可持续种植
甘蔗花叶病对澳大利亚甘蔗产业构成严重威胁，易感品种产量损失可达30%。现有人工检测方法效率低，难以规模化应用。本研究提出一种结合高光谱成像与机器学习的新方法，通过局部光谱块分析捕捉空间与光谱变化，并利用ResNet18架构聚合为全局特征表示。在受控环境与田间条件下采集了八种甘蔗品种的数据。相比支持向量机等传统方法难以有效利用空间-光谱关系，该深度学习模型展现出更高分类精度，能够从细粒度高光谱数据中识别抗病特性。该方法显著提升早期检测能力，有助于高效管理易感品系，推动可持续甘蔗生产。
原文摘要 · Abstract (English)
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.
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