The coffee crop spectral behavior identification throughout its cycle can contribute to its development monitoring under pest incidence. We aim to identify coffee development through time signatures of enhanced vegetation index (EVI), as well as to evaluate the use of seasonal autoregressive integrated moving average (SARIMA) models to identify coffee trees spectrum-time patterns under different irrigation management and design future scenarios. Three coffee fields were selected under different irrigation systems, whose EVI data of 8 years were obtained from the moderate resolution image spectroradiometer sensor. Each coffee crop model was subjected to residual autocorrelation test and classified according to information criteria, while its accuracy was assessed by means of prediction error measures and agreement index. The estimated and observed EVI values were similar, even for the predicted year. However, in agricultural years during which coffee diseases occurred, the crops showed vegetative vigor below the expected. We concluded that SARIMA models enabled the establishment of a reliable spectral signature expected for coffee crop, which could help with crop management defining, regardless of the irrigation system adopted. Based on the evaluation of divergence between expected and observed spectral signatures, early signs of coffee underdevelopment were detected, which could reduce economic loss risks on its commercial chain.

中文翻译：

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增强植被指数的时间序列分析可检测不同灌溉系统下咖啡作物的生长

在整个周期内对咖啡作物的光谱行为进行识别可有助于其在有害生物发生率下的发育监测。我们旨在通过增强植被指数（EVI）的时间特征来识别咖啡的发展，并评估季节性自回归综合移动平均线（SARIMA）模型的使用，以识别不同灌溉管理和设计未来方案下的咖啡树频谱-时间模式。 。在不同的灌溉系统下选择了三个咖啡田，这些咖啡田的8年EVI数据是从中分辨率图像光谱仪获得的。对每种咖啡作物模型进行残差自相关测试，并根据信息标准进行分类，同时通过预测误差度量和一致性指标评估其准确性。即使对于预测年份，估计和观察到的EVI值也相似。但是，在发生咖啡病的农业年度中，农作物的营养活力低于预期。我们得出的结论是，无论采用哪种灌溉系统，SARIMA模型都可以为咖啡作物建立可靠的光谱特征，这有助于定义作物管理。根据对预期光谱特征与观察光谱特征之间差异的评估，可以检测到咖啡开发不足的早期迹象，这可以减少其商业链上的经济损失风险。我们得出的结论是，无论采用哪种灌溉系统，SARIMA模型都可以为咖啡作物建立可靠的光谱特征，这有助于定义作物管理。根据对预期光谱特征与观察光谱特征之间差异的评估，可以检测到咖啡开发不足的早期迹象，这可以减少其商业链上的经济损失风险。我们得出的结论是，无论采用哪种灌溉系统，SARIMA模型都可以为咖啡作物建立可靠的光谱特征，这有助于定义作物管理。根据对预期光谱特征与观察光谱特征之间差异的评估，可以检测到咖啡开发不足的早期迹象，这可以减少其商业链上的经济损失风险。