The key to intelligent fault diagnosis is to find relevant characteristics with the capability of representing different types of faults. However, the engineering problem is that a few simple empirical features (EFs) cannot obtain high classification accuracy, and complex feature engineering requires strong professional knowledge, which leads to limited applications on a general scale. In addition, intelligent feature extraction and classification methods without prior knowledge cannot guarantee that the model learned the general features used for classification, and its robustness and generalization are not strong when the objects with low-quality training data. Therefore, a fusion method of combining EFs and adaptive features extracted by a deep neural network is proposed. In this method, simple EFs that only need a few professional knowledge are adopted to realize general feature extraction and, hence, maintain the robustness of the model. A modified neural network structure (LiftingNet) is proposed to achieve the adaptive extraction of hidden features for specific objects, for realizing the high precision of bearing fault classification. In order to realize the fusing of EFs and adaptive features, XGBoost is utilized as the final classifier instead of common softmax. The feasibility and validity of the proposed method are verified by two data sets collected from motor bearings at stable work conditions. The experimental results show that the classification accuracy generated by the proposed method is improved. It also can maintain the robust performance on data sets even with various noises.

Stomatal behavior in grapevines has been identified as a good indicator of the water stress level and overall health of the plant. Microscope images are often used to analyze stomatal behavior in plants. However, most of the current approaches involve manual measurement of stomatal features. The main aim of this research is to develop a fully automated stomata detection and pore measurement method for grapevines, taking microscope images as the input. The proposed approach, which employs machine learning and image processing techniques, can outperform available manual and semi-automatic methods used to identify and estimate stomatal morphological features. First, a cascade object detection learning algorithm is developed to correctly identify multiple stomata in a large microscopic image. Once the regions of interest which contain stomata are identified and extracted, a combination of image processing techniques are applied to estimate the pore dimensions of the stomata. The stomata detection approach was compared with an existing fully automated template matching technique and a semi-automatic maximum stable extremal regions approach, with the proposed method clearly surpassing the performance of the existing techniques with a precision of 91.68% and an F1-score of 0.85. Next, the morphological features of the detected stomata were measured. Contrary to existing approaches, the proposed image segmentation and skeletonization method allows us to estimate the pore dimensions even in cases where the stomatal pore boundary is only partially visible in the microscope image. A test conducted using 1267 images of stomata showed that the segmentation and skeletonization approach was able to correctly identify the stoma opening 86.27% of the time. Further comparisons made with manually traced stoma openings indicated that the proposed method is able to estimate stomata morphological features with accuracies of 89.03% for area, 94.06% for major axis length, 93.31% for minor axis length and 99.43% for eccentricity. The proposed fully automated solution for stomata detection and measurement is able to produce results far superior to existing automatic and semi-automatic methods. This method not only produces a low number of false positives in the stomata detection stage, it can also accurately estimate the pore dimensions of partially incomplete stomata images. In addition, it can process thousands of stomata in minutes, eliminating the need for researchers to manually measure stomata, thereby accelerating the process of analysing plant health.