Abstract: Pesticides can be often regulated to detect the deposition constitutes on the crops in fields. Nevertheless, the current research cannot focus on the specific difficulties in practical application. Fortunately, the attenuated total reflection spectroscopy (ATR-FTIR) technology can be expected to probe the presence of pesticides on the leaf surface without damage to the samples. This study aims to evaluate the in situ non-destructive sensing of pesticide deposition using infrared ATR-FTIR together with chemometric analysis. The examples were also taken as the broad-spectrum fungicide boscalid and the economic crop cotton. 140 standard samples of cotton leaves were then fabricated with known amounts of pesticide deposition. The spectral data was acquired after experiment. Subsequently, the preliminary analysis was conducted to determine the differences among the samples using Principal Component Analysis (PCA). The great variations in spectral characteristics were attributed to the differences in pesticide deposition. Distinct patterns and trends were also identified from the data. After that the interval partial least squares (iPLS) and correlation analysis were employed to screen out the wavelength variables with strong correlations. This procedure aimed to identify the specific wavelengths the most relevant to pesticide deposition. Finally, a prediction model was established using the optimal variables and partial least squares regression (PLSR). The deposition amount of pesticide was predicted using the spectral data. The outstanding performance of prediction model was achieved with an R² value of 0.83 and an RMSE of 1.21 μg/cm² in calibration, while an R² value of 0.86, an RMSE of 1.18 μg/cm², an RPD of 2.3, and an RPIQ of 2.0 in validation. These results demonstrated the high accuracy and reliability of the prediction model on pesticide deposition. In addition, a classification model was also built with the probabilistic neural network (PNN), in order to distinguish whether the samples were with a higher deposition yield than the limit of detection (LOD) of the PLSR model. The PNN classification model shared an overall accuracy of 95% to discriminate whether the pesticide deposition amount on the samples was greater than the LOD of PLSR model. Therefore, it was feasible to classify the samples using PNN model. The prediction of PLSR model was better performed for the preliminary screening and classification. The results demonstrated that the ATR-FTIR shared the successfully perceived boscalid deposition on the surfaces of cotton leaf. Five well-defined peaks of absorption were observed at 3400, 2800, 1720, 1450, and 1160 cm^-1, corresponding to the O-H, C-H, C=O, aromatic, and C-O stretching vibrations, respectively. These specific peaks were also resulted from the overlapping between the pesticide and leaf surfaces. The detection mechanism was enhanced from the valuable peaks information about the chemical interactions between the pesticide and the leaf. Among them, 272 wavelength variables were selected as the inputs that highly correlated with the pesticide deposition among the five well-defined peaks. The quantitative model was then established using PLSR. The most relevant spectra were selected to further improve the accuracy and performance of the model. The low LOD (3.54 μg/cm²) of PLSR model was calculated and validated by the RMSE value, indicating the sensitivity to detect even small amounts of pesticide deposition. Therefore, the ATR-FTIR technology can be expected to accurately detect the pesticides with different amount of deposition. This finding can provide the theoretical foundation and data support to in situ and non-destructive perception of its original deposition or residual amount in other crops. The promising potential can be offered for the pesticide monitoring and management, thus enabling more efficient and sustainable agricultural practices.