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Localization of fruit and vegetable is of great significance to fruit and vegetable harvesting robots and even harvesting industries. However, uncontrollable factors, such as varying illumination, random occlusion, and various surface color and texture, constrain the localization of fruit and vegetable using the vision imaging technology under unconstructed environment. Our previous studies have developed various methods (illumination normalization, features-based classification, etc.) to localize a certain kind of fruit or vegetable using the binocular stereo vision. However, the localization of the multiple fruit and vegetable still faces challenges due to the uncontrollable factors. In order to address this issue, this study proposed an intelligent localization method of targets in fruit and vegetable images acquired by the two charge-coupled device (CCD) color cameras under unstructured environment. The method utilized the Faster region-based convolutional neural network (R-CNN) model to recognize the fruit and vegetable. Based on the recognition results, a window zooming method was proposed for the matching of the recognized target. Finally, the localization of the target was completed by calculating the three-dimensional coordinates of the matched target using the triangular measurement principle. The experimental results showed that the proposed method could be robust against the influences of varying illumination and occlusion, and the average accurate recognition rate was 96.33% under six different conditions. About 93.44% of 1036 pairs of tested targets from unoccluded and partially occluded conditions were successfully matched. Localization errors had no significant difference and they were less than 7.5 mm when the measuring distance was between 300 and 1600 mm under varying illumination and partially occluded conditions.INDEX TERMS Fruit and vegetable localization, vision imaging technology, binocular stereo vision, unstructured environment.

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Aiming to identify the bearing faults level effectively, a new method based on kernel principal component analysis and particle swarm optimization optimized k-nearest neighbour model is proposed. First, the gathered vibration signals are decomposed by time-frequency domain method, i.e., local mean decomposition; as a result, the product functions decomposed from the original signal are derived. Then, the entropy values of the product functions are calculated by Shannon method, which will work as the input features for k-nearest neighbour model. The kernel principal component analysis model is used to reduce the dimension of the features, and then the k-nearest neighbour model which was optimized by the particle swarm optimization method is used to identify the bearing fault levels. Case of test and actually collected signal are analysed. The results validate the effectiveness of the proposed algorithm.

Predicting the degradation process of bearings before they reach the failure threshold is extremely important in industry. This paper proposed a novel method based on the support vector machine (SVM) and the Markov model to achieve this goal. Firstly, the features are extracted by time and time-frequency domain methods. However, the extracted original features are still with high dimensional and include superfluous information, and the nonlinear multifeatures fusion technique LTSA is used to merge the features and reduces the dimension. Then, based on the extracted features, the SVM model is used to predict the bearings degradation process, and the CAO method is used to determine the embedding dimension of the SVM model. After the bearing degradation process is predicted by SVM model, the Markov model is used to improve the prediction accuracy. The proposed method was validated by two bearing run-to-failure experiments, and the results proved the effectiveness of the methodology.

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