Falcioni Renan, Antunes Werner Camargos, Demattê José Alexandre M, Nanni Marcos Rafael
Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, PR, Brazil.
Department of Soil Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Av. Pádua Dias, 11, Piracicaba 13418-260, SP, Brazil.
Plants (Basel). 2023 Jun 16;12(12):2347. doi: 10.3390/plants12122347.
Reflectance spectroscopy, in combination with machine learning and artificial intelligence algorithms, is an effective method for classifying and predicting pigments and phenotyping in agronomic crops. This study aims to use hyperspectral data to develop a robust and precise method for the simultaneous evaluation of pigments, such as chlorophylls, carotenoids, anthocyanins, and flavonoids, in six agronomic crops: corn, sugarcane, coffee, canola, wheat, and tobacco. Our results demonstrate high classification accuracy and precision, with principal component analyses (PCAs)-linked clustering and a kappa coefficient analysis yielding results ranging from 92 to 100% in the ultraviolet-visible (UV-VIS) to near-infrared (NIR) to shortwave infrared (SWIR) bands. Predictive models based on partial least squares regression (PLSR) achieved R values ranging from 0.77 to 0.89 and ratio of performance to deviation (RPD) values over 2.1 for each pigment in C and C plants. The integration of pigment phenotyping methods with fifteen vegetation indices further improved accuracy, achieving values ranging from 60 to 100% across different full or range wavelength bands. The most responsive wavelengths were selected based on a cluster heatmap, β-loadings, weighted coefficients, and hyperspectral vegetation index (HVI) algorithms, thereby reinforcing the effectiveness of the generated models. Consequently, hyperspectral reflectance can serve as a rapid, precise, and accurate tool for evaluating agronomic crops, offering a promising alternative for monitoring and classification in integrated farming systems and traditional field production. It provides a non-destructive technique for the simultaneous evaluation of pigments in the most important agronomic plants.
反射光谱法与机器学习和人工智能算法相结合，是对农艺作物中的色素进行分类和预测以及表型分析的有效方法。本研究旨在利用高光谱数据开发一种强大而精确的方法，用于同时评估六种农艺作物（玉米、甘蔗、咖啡、油菜、小麦和烟草）中的叶绿素、类胡萝卜素、花青素和黄酮类等色素。我们的结果显示出高分类准确率和精度，主成分分析（PCA）关联聚类和kappa系数分析在紫外可见（UV-VIS）至近红外（NIR）再到短波红外（SWIR）波段的结果范围为92%至100%。基于偏最小二乘回归（PLSR）的预测模型在C3和C4植物中，每种色素的R值范围为0.77至0.89，性能与偏差比（RPD）值超过2.1。色素表型分析方法与十五种植被指数的整合进一步提高了准确性，在不同的全波段或波段范围内，准确率达到60%至100%。基于聚类热图、β载荷、加权系数和高光谱植被指数（HVI）算法选择了最敏感的波长，从而增强了所生成模型的有效性。因此，高光谱反射率可作为评估农艺作物的快速、精确和准确工具，为综合种植系统和传统田间生产中的监测和分类提供了一种有前景的替代方法。它为同时评估最重要的农艺植物中的色素提供了一种无损技术。
