Yellow turmeric (Curcuma longa) is widely used for culinary and medicinal purposes, and as a dietary supplement. Due to the commercial popularity of C. longa, economic adulteration and contamination with botanical additives and chemical substances has increased. This study used FT-IR spectroscopy for identifying and estimating white turmeric (Curcuma zedoaria), and Sudan Red G dye mixed with yellow turmeric powder. Fifty replicates of yellow turmeric—Sudan Red mixed samples (1%, 5%, 10%, 15%, 20%, 25% Sudan Red, w/w) and fifty replicates of yellow turmeric—white turmeric mixed samples (10%, 20%, 30%, 40%, 50% white turmeric, w/w) were prepared. The IR spectra of the pure compounds and mixtures were analyzed. The 748 cm⁻¹ Sudan Red peak and the 1078 cm⁻¹ white turmeric peak were used as spectral fingerprints. A partial least square regression (PLSR) model was developed for each mixture type to estimate adulteration concentrations. The coefficient of determination (R²_v) for the Sudan Red mixture model was 0.97 with a root mean square error of prediction (RMSEP) equal to 1.3%. R²_v and RMSEP for the white turmeric model were 0.95 and 3.0%, respectively. Our results indicate that the method developed in this study can be used to identify and quantify yellow turmeric powder adulteration.

In many parts of the world, apple trees suffer from severe foliar damage each year due to infection of Alternaria blotch (Alternaria alternata f. sp. Mali), resulting in serious economic losses to growers. Traditional methods for disease detection and severity classification mostly rely on manual labor, which is slow, labor-intensive and highly subjective. There is an urgent need to develop an effective protocol to rapidly and accurately evaluate disease severity. In this study, DeeplabV3+, PSPNet and UNet were used to assess the severity of apple Alternaria leaf blotch. For identifications of leaves and disease areas, the dataset with a total of 5382 samples was randomly split into 74% (4004 samples) for model training, 9% (494 samples) for validation, 8% (444 samples) for testing and 8% (440 samples) for overall testing. Apple leaves were first segmented from complex backgrounds using the deep-learning algorithms with different backbones. Then, the recognition of disease areas was performed on the segmented leaves. The results showed that the PSPNet model with MobileNetV2 backbone exhibited the highest performance in leaf segmentation, with precision, recall and MIoU values of 99.15%, 99.26% and 98.42%, respectively. The UNet model with VGG backbone performed the best in disease-area prediction, with a precision of 95.84%, a recall of 95.54% and a MIoU value of 92.05%. The ratio of disease area to leaf area was calculated to assess the disease severity. The results showed that the average accuracy for severity classification was 96.41%. Moreover, both the correlation coefficient and the consistency correlation coefficient were 0.992, indicating a high agreement between the reference values and the value that the research predicted. This study proves the feasibility of rapid estimation of the severity of apple Alternaria leaf blotch, which will provide technical support for precise application of pesticides.

Weeds in the field affect the normal growth of lettuce crops by competing with them for resources such as water and sunlight. The increasing costs of weed management and limited herbicide choices are threatening the profitability, yield, and quality of lettuce. The application of intelligent weeding robots is an alternative to control intra-row weeds. The prerequisite for automatic weeding is accurate differentiation and rapid localization of different plants. In this study, a squeeze-and-excitation (SE) network combined with You Only Look Once v5 (SE-YOLOv5x) is proposed for weed-crop classification and lettuce localization in the field. Compared with models including classical support vector machines (SVM), YOLOv5x, single-shot multibox detector (SSD), and faster-RCNN, the SE-YOLOv5x exhibited the highest performance in weed and lettuce plant identifications, with precision, recall, mean average precision (mAP), and F1-score values of 97.6%, 95.6%, 97.1%, and 97.3%, respectively. Based on plant morphological characteristics, the SE-YOLOv5x model detected the location of lettuce stem emerging points in the field with an accuracy of 97.14%. This study demonstrates the capability of SE-YOLOv5x for the classification of lettuce and weeds and the localization of lettuce, which provides theoretical and technical support for automated weed control.