ABSTRACT

Asian soybean rust (Phakopsora pachyrhizi) is the most severe disease in soybean crops production. The early detection of the disease by traditional methods involves visual inspection of the symptoms present in the leaves and is expensive and time-consuming. The limitations of visual detection have led to an interest in the development of spectroscopically based detection techniques for the rapid diagnosis of this disease. Thus, this work aimed to develop a procedure for early and accurate detection and differentiation of soybean under different levels of Asian rust disease, based on spectral analysis and linear discriminant analysis (LDA), with optimum wavelengths selection by a stepwise procedure. Reflectance spectroscopy ranging from the visible (Vis) to the near-infrared (NIR) region (350–2,500 nm) was obtained by a Fieldspec 3 Jr. hyperspectral sensor through the spectral measurement of soybean leaves with different levels of disease that had the following treatments: uninfected (T1), severity 0.6% (T2), severity 2.0% (T3), severity 7.0% (T4), severity 18.0% (T5), and severity 42.0% (T6). There were 15 spectral curves measured in each treatment, totalling 90 spectral samples. Principal component analysis (PCA) was applied as an indicator of the explained variance of the reflectance spectra among the different disease progressions. The spectral signature of the leaves showed the existence of a strong increase in reflectance in the Vis region when the levels of disease increased, associated with a lower concentration of pigments. The PCA explained over 97.00% of the spectral variance in the first and second principal components and the stepwise procedure selected from 87 spectral bands. The LDA achieved global accuracies of 100.00% and 82.51%, in the calibration and validation procedures, respectively. These results suggest the spectral reflectance technique as a promising tool for cost-effective, fast analysis and a non-destructive method for diagnosis Asian soybean rust.

摘要

亚洲大豆锈病（Phakopsora pachyrhizi）是大豆作物生产中最严重的疾病。通过传统方法对疾病进行早期检测需要目视检查叶片中存在的症状，而且昂贵且耗时。视觉检测的局限性引起了人们对基于光谱的检测技术的发展的兴趣，以快速诊断该疾病。因此，这项工作的目的是基于光谱分析和线性判别分析（LDA），通过逐步选择最佳波长，开发一种可以在不同水平的亚洲锈病下早期，准确地检测和区分大豆的方法。通过Fieldspec 3 Jr获得了从可见（Vis）到近红外（NIR）区域（350–2,500 nm）的反射光谱。高光谱传感器通过对具有不同处理水平的大豆叶片进行光谱测量而进行了以下处理：未感染（T1），严重性0.6％（T2），严重性2.0％（T3），严重性7.0％（T4），严重性18.0％（ T5），严重程度为42.0％（T6）。每种处理中测得15条光谱曲线，总共90个光谱样品。主成分分析（PCA）被用作指示不同疾病进展之间反射光谱的解释方差的指标。叶片的光谱特征表明，当病害水平增加时，Vis区域的反射率会显着增加，这与色素的浓度降低有关。PCA对97进行了解释。第一和第二主成分的光谱变化的00％，以及从87个光谱带中选择的逐步过程。在校准和验证程序中，LDA分别达到了100.00％和82.51％的全球精度。这些结果表明，光谱反射技术是一种有成本效益的快速分析方法，是诊断亚洲大豆锈病的一种非破坏性方法。