Drip irrigation is one of the most efficient irrigation techniques, but it is susceptible to dripper clogging. This study proposes a novel and non-destructive method based on visible and near infrared (Vis/NIR) spectroscopy coupled with chemometric methods for the discrimination and thickness estimation of physical and chemical fouling in drip-irrigation systems. Four representative materials linked to physical and chemical clogging (kaolin, bentonite, sand and calcium carbonate) at different thicknesses were selected to illustrate the potential of the approach. Partial least squares regression (PLSR) and its modification partial least squares with discriminant analysis (PLS-DA) were selected for the modelling of clogging materials. The PLS-DA model was able to predict with 96.97% accuracy all classes of materials. The PLSR models were able to estimate fouling thickness with relative prediction errors comprised between 134 μm and 164 μm. This difference appears mainly to be due to the physical properties of the selected materials. This prediction accuracy enabled the estimation of the clogging thickness between 10 and 21% of dripper channel coverage depending on the dripper channel section and the material under study. The proposed method offers an appropriate approach for clogging studies in drip irrigation systems that could be transferred to field applications.

滴灌是最有效的灌溉技术之一，但它容易受到滴头堵塞。本研究提出了一种基于可见光和近红外 (Vis/NIR) 光谱以及化学计量学方法的新型无损方法，用于鉴别和估计滴灌系统中物理和化学污垢的厚度。四种与物理和化学堵塞相关的代表性材料（高岭土、膨润土、沙子和碳酸钙）在不同厚度下被选择来说明该方法的潜力。偏最小二乘回归 (PLSR) 及其修正偏最小二乘与判别分析 (PLS-DA) 被选择用于堵塞材料的建模。PLS-DA 模型能够以 96.97% 的准确率预测所有类别的材料。PLSR 模型能够估计结垢厚度，相对预测误差介于 134 μm 和 164 μm 之间。这种差异似乎主要是由于所选材料的物理特性造成的。根据滴头通道截面和所研究的材料，这种预测精度能够估计滴头通道覆盖范围的 10% 到 21% 之间的堵塞厚度。所提出的方法为滴灌系统中的堵塞研究提供了一种合适的方法，可以转移到现场应用中。根据滴头通道截面和所研究的材料，这种预测精度能够估计滴头通道覆盖范围的 10% 到 21% 之间的堵塞厚度。所提出的方法为滴灌系统中的堵塞研究提供了一种合适的方法，可以转移到现场应用中。根据滴头通道截面和所研究的材料，这种预测精度能够估计滴头通道覆盖范围的 10% 到 21% 之间的堵塞厚度。所提出的方法为滴灌系统中的堵塞研究提供了一种合适的方法，可以转移到现场应用中。