Laimburg Research Centre, Laimburg 6, Pfatten (Vadena), IT-39040 Auer (Ora), South Tyrol, Italy.
Laimburg Research Centre, Laimburg 6, Pfatten (Vadena), IT-39040 Auer (Ora), South Tyrol, Italy.
Spectrochim Acta A Mol Biomol Spectrosc. 2021 Dec 15;263:120178. doi: 10.1016/j.saa.2021.120178. Epub 2021 Jul 13.
In this study near infrared spectroscopical analysis of dried and ground leaves was performed and combined with a multivariate data analysis to distinguish 'Candidatus Phytoplasma mali' infected from non-infected apple trees (Malus × domestica). The bacterium is the causative agent of Apple Proliferation, one of the most threatening diseases in commercial apple growing regions. In a two-year study, leaves were sampled from three apple orchards, at different sampling events throughout the vegetation period. The spectral data were analyzed with a principal component analysis and classification models were developed. The model performance for the differentiation of Apple Proliferation diseased from non-infected trees increased throughout the vegetation period and gained best results in autumn. Even with asymptomatic leaves from infected trees a correct classification was possible indicating that the spectral-based method provides reliable results even if samples without visible symptoms are analyzed. The wavelength regions that contributed to the differentiation of infected and non-infected trees could be mainly assigned to a reduction of carbohydrates and N-containing organic compounds. Wet chemical analyses confirmed that N-containing compounds are reduced in leaves from infected trees. The results of our study provide a valuable indication that spectral analysis is a promising technique for Apple Proliferation detection in future smart farming approaches.
本研究对干燥和粉碎的叶片进行了近红外光谱分析，并结合多元数据分析，以区分‘苹果韧皮部杆菌’感染和未感染的苹果树（Malus × domestica）。该细菌是苹果增生病的病原体，是商业苹果种植区最具威胁的病害之一。在为期两年的研究中，从三个苹果园中采集了叶片样本，在整个生育期内进行了不同的采样。使用主成分分析对光谱数据进行分析，并建立了分类模型。随着生育期的进行，用于区分苹果增生病患病和未感染树木的模型性能逐渐提高，在秋季达到最佳效果。即使是来自感染树木的无症状叶片也可以进行正确的分类，这表明基于光谱的方法即使分析没有可见症状的样本也能提供可靠的结果。有助于区分感染和未感染树木的波长区域主要归因于碳水化合物和含氮有机化合物的减少。湿化学分析证实，感染树木的叶片中含氮化合物减少。本研究的结果为光谱分析作为未来智能农业方法中苹果增生病检测的一种有前途的技术提供了有价值的依据。
