Pesticide residue in paddy water is one of the main factors affecting the quality and safety of rice, however, the negative effect of this residue can be effectively prevented and reduced through early detection. This study developed a rapid detection method for fonofos, phosmet, and sulfoxaflor in paddy water through chemometric methods and surface-enhanced Raman spectroscopy (SERS). Residue from paddy water samples was directly used for SERS measurement. The obtained spectra from the SERS can detect 0.5 mg/L fonofos, 0.25 mg/L phosmet, and 1 mg/L sulfoxaflor through the appearance of major characteristic peaks. Then, we used chemometric methods to develop models for the intelligent analysis of pesticides, alongside the SERS spectra. The classification models developed by K-nearest neighbor identified all of the samples, with an accuracy of 100%. For the quantitative analysis, the partial least squares regression models obtained the best predicted performance for fonofos and sulfoxaflor, and the support vector machine model provided optimal results, with a root-mean-square error of validation of 0.207 and a coefficient of determination of validation of 0.99952, for phosmet. Experiments for actual contaminated samples also showed that the above models predicted the pesticide residue values with high accuracy. Overall, using SERS with chemometric methods provided a simple and convenient approach for the detection of pesticide residues in paddy water.

Detection of infected kernels is important for Fusarium head blight (FHB) prevention and product quality assurance in wheat. In this study, Raman spectroscopy (RS) and deep learning networks were used for the determination of FHB-infected wheat kernels. First, the RS spectra of healthy, mild, and severe infection kernels were measured and spectral changes and band attribution were analyzed. Then, the Inception network was improved by residual and channel attention modules to develop the recognition models of FHB infection. The Inception–attention network produced the best determination with accuracies in training set, validation set, and prediction set of 97.13%, 91.49%, and 93.62%, among all models. The average feature map of the channel clarified the important information in feature extraction, itself required to clarify the decision-making strategy. Overall, RS and the Inception–attention network provide a noninvasive, rapid, and accurate determination of FHB-infected wheat kernels and are expected to be applied to other pathogens or diseases in various crops.

Aboveground biomass (AGB) is an important indicator used to predict crop yield. Traditional spectral features or image textures have been proposed to estimate the AGB of crops, but they perform poorly at high biomass levels. This study thus evaluated the ability of spectral features, image textures, and their combinations to estimate winter wheat AGB. Spectral features were obtained from the wheat canopy reflectance spectra at 400–1000 nm, including original wavelengths and seven vegetation indices. Effective wavelengths (EWs) were screened through use of the successive projection algorithm, and the optimal vegetation index was selected by correlation analysis. Image texture features, including texture features and the normalized difference texture index, were extracted using gray level co-occurrence matrices. Effective variables, including the optimal texture subset (OTEXS) and optimal normalized difference texture index subset (ONDTIS), were selected by the ranking of feature importance using the random forest (RF) algorithm. Linear regression (LR), partial least squares regression (PLS), and RF were established to evaluate the relationship between each calculated feature and AGB. Results demonstrate that the ONDTIS with PLS based on the validation datasets exhibited better performance in estimating AGB for the post-seedling stage (R² = 0.75, RMSE = 0.04). Moreover, the combinations of the OTEXS and EWs exhibited the highest prediction accuracy for the seeding stage when based on the PLS model (R² = 0.94, RMSE = 0.01), the post-seedling stage when based on the LR model (R² = 0.78, RMSE = 0.05), and for all stages when based on the RF model (R² = 0.87, RMSE = 0.05). Hence, the combined use of spectral and image textures can effectively improve the accuracy of AGB estimation, especially at the post-seedling stage.

Background: Aboveground biomass (AGB) is an important indicator to predict crop yield. Traditional spectral features or image textures have been proposed to estimate the AGB of crops, but they perform poorly in estimation of AGB at high biomass levels. The present study thus evaluated the ability of spectral features, image textures, combinations thereof to estimate winter wheat AGB. Result: The spectral features were obtained from the wheat canopy reflectance spectra of 400–1000 nm including original wavelengths and seven vegetation indices (VIs), then we screened effective wavelengths (EWs) through successive projection algorithm (SPA) and the optimal vegetation index selected by correlation analysis. The image textures features were extracted by gray level co-occurrence matrix including texture features (TEX) and normalized difference texture index (NDTI), then we selected effective variables including the optimal texture subset (OTEXS) and the optimal normalized difference texture index subset (ONDTIS) through the ranking of feature importance of random forest (RF). Linear regression (LR), partial least squares regression (PLS) and random forest (RF) were established to evaluate the relationship between each calculated feature and AGB. The results demonstrate that the ONDTIS with PLS based on validation datasets exhibited better performance in estimating AGB for the post-seedling stage (R² = 0.75, RMSE = 0.04). Moreover, the combinations of OTEXS and EWs with LR based on validation datasets exhibited the highest prediction accuracy for the post-seedling stage (R² = 0.78, RMSE = 0.05). Conclusion: The findings show that the combined use of spectral features and image textures can effectively improve the accuracy for AGB estimation especially in post-seeding stage.