Rice blast is considered as the most destructive disease that threatens global rice production and causes severe economic losses worldwide. A detection of rice blast infection in an early manner is vital to limit its expansion and proliferation. However, little research has been devoted to spectral detection of rice leaf blast (RLB) infection, especially at the asymptomatic or early stages. To fill the gap, this study aimed to examine the feasibility of detecting RLB infection from leaf reflectance spectra at asymptomatic, early and mild stages of disease development. Greenhouse experiments were conducted over two consecutive years to collect hyperspectral data (350–2500 nm) on various days after inoculation (DAIs) for the three infection stages. These hyperspectral data were processed to select disease specific spectral features (DSSFs). Such DSSFs were then used to feed the machine learning based sequential floating forward selection (ML-SFFS) methodology for determining the optimal feature combination (OFC) and overall accuracy (OA) in the detection of RLB at various infection stages.

The results demonstrated that the rice plants displayed considerable biochemical and spectral variations and this pattern of variations existed consistently during plant-pathogen interactions. A multivariate pool of DSSFs comprising two reflectance bands, fourteen SIs, and five continuous wavelet coefficients, were determined for revealing the dynamic response of RLB infection across two years. The combination of 2 to 4 spectral features selected by the ML-SFFS algorithm was sufficient to identify infected leaves with classification accuracies over 65% and 80% for the asymptomatic and early infection stages, respectively. The OA could rise up to 95% for the mild stage. Compared to the use of all DSSFs with a support vector machine (SVM) classifier, the SVM-based SFFS (SVM-SFFS) algorithm prevailed in the classification accuracy up to 10% over the sampling period. Our results demonstrated the feasibility of accurate classification of RLB infected samples by ML-SFFS. This study suggests that reflectance spectroscopy has great potential in the pre-visual detection of RLB infection and airborne or spaceborne imaging spectroscopy is promising for the mapping of early occurrence and severity levels of RLB infection at large scales.

稻瘟病被认为是最具破坏性的疾病，威胁全球稻米生产并在全球范围内造成严重的经济损失。尽早发现稻瘟病感染对于限制其扩展和扩散至关重要。然而，很少有研究致力于光谱检测稻瘟病（RLB）感染，特别是在无症状或早期阶段。为了填补这一空白，本研究旨在检验在疾病发展的无症状，早期和轻度阶段从叶片反射光谱检测RLB感染的可行性。连续两年进行了温室实验，以收集三个感染阶段的接种后（DAI）后不同天的高光谱数据（350-2500 nm）。处理这些高光谱数据以选择特定疾病的光谱特征（DSSF）。

结果表明，水稻植物表现出相当大的生化和光谱变化，并且这种变化模式在植物与病原体的相互作用过程中始终存在。确定了包含两个反射带，十四个SI和五个连续小波系数的DSSF多元池，以揭示两年中RLB感染的动态响应。通过ML-SFFS算法选择的2至4个光谱特征的组合足以识别无症状和早期感染阶段分类准确度分别超过65％和80％的被感染叶片。在轻度阶段，OA可能上升至95％。与将所有DSSF与支持向量机（SVM）分类器一起使用相比，基于SVM的SFFS（SVM-SFFS）算法在整个采样期间的分类精度高达10％。我们的结果证明了通过ML-SFFS对RLB感染的样本进行准确分类的可行性。这项研究表明，反射光谱法在肉眼可见的RLB感染的检测中具有巨大的潜力，而机载或星载成像光谱法有望用于大规模绘制RLB感染的早期发生和严重程度。