Pacheco-Gil Rosa Angela, Velasco-Cruz Ciro, Pérez-Rodríguez Paulino, Burgueño Juan, Pérez-Elizalde Sergio, Rodrigues Francelino, Ortiz-Monasterio Ivan, Del Valle-Paniagua David Hebert, Toledo Fernando
Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, México.
Socioeconomics, Statistics and Informatics Department, Colegio de Postgraduados, Texcoco, México.
Plant Methods. 2023 Jan 20;19(1):6. doi: 10.1186/s13007-023-00980-9.
As a result of the technological progress, the use of sensors for crop survey has substantially increased, generating valuable information for modelling agricultural data. Plant spectroscopy jointly with statistical modeling can potentially help to assess certain chemical components of interest present in plants, which may be laborious and expensive to obtain by direct measurements. In this research, the phosphorus content in wheat grain is modeled using reflectance information measured by a hyperspectral sensor at different wavelengths. A Bayesian procedure for selecting variables was used to identify the set of the most important spectral bands. Additionally, three different models were evaluated: the first model assumes that the observations are independent, the other two models assume that the observations are spatially correlated: one of the proposed models, assumes spatial dependence using a Conditionally Autoregressive Model (CAR), and the other through an exponential correlogram. The goodness of fit of the models was evaluated by means of the Deviance Information Criterion, and the predictive power is evaluated using cross validation.
We have found that CAR was the model that best fits and predicts the data. Additionally, the selection variable procedure in the CAR model reveals which wavelengths in the range of 500-690 nm are the most important. Comparing the vegetative indices with the CAR model, it was observed that the average correlation of the CAR model exceeded that of the vegetative indices by 23.26%, - 1.2% and 22.78% for the year 2010, 2011 and 2012 respectively; therefore, the use of the proposed methodology outperformed the vegetative indices in prediction.
The proposal to predict the phosphorus content in wheat grain using Bayesian approach, reflect with the results as a good alternative.
由于技术进步，用于作物调查的传感器使用量大幅增加，为农业数据建模生成了有价值的信息。植物光谱学与统计建模相结合，有可能有助于评估植物中某些感兴趣的化学成分，而直接测量这些成分可能既费力又昂贵。在本研究中，利用高光谱传感器在不同波长下测量的反射率信息对小麦籽粒中的磷含量进行建模。使用贝叶斯变量选择程序来识别最重要的光谱波段集。此外，评估了三种不同的模型：第一个模型假设观测值是独立的，另外两个模型假设观测值是空间相关的：其中一个提出的模型使用条件自回归模型（CAR）假设空间依赖性，另一个通过指数相关图假设空间依赖性。通过偏差信息准则评估模型的拟合优度，并使用交叉验证评估预测能力。
我们发现CAR是最适合和预测数据的模型。此外，CAR模型中的选择变量程序揭示了500 - 690 nm范围内哪些波长是最重要的。将植被指数与CAR模型进行比较，观察到2010年、2011年和2012年CAR模型的平均相关性分别比植被指数高出23.26%、 - 1.2%和22.78%；因此，所提出的方法在预测方面优于植被指数。
使用贝叶斯方法预测小麦籽粒中磷含量的提议，结果表明是一个很好的选择。
