Abstract Crop diseases have a devastating effect on agricultural production, and serious diseases can lead to harvest failure entirely. Recent developments in deep learning have greatly improved the accuracy of image identification. In this study, we investigated the transfer learning of deep convolutional neural networks and modified the network structure to improve the learning capability of plant lesion characteristics. The MobileNet with squeeze‐and‐excitation (SE) block was selected in our approach. Integrating the merits of both, the pre‐trained MobileNet and SE block were fused to form a new network, which we termed the SE‐MobileNet, and was used to identify the plant diseases. In particular, the transfer learning was performed twice to obtain the optimum model. The first phase trained the model for the extended layers while the bottom convolution layers were frozen with the pre‐trained weights on ImageNet; by loading the model trained in the first phase, the second phase retrained the model using the target dataset. The proposed procedure provides a significant increase in efficiency relative to other state‐of‐the‐art methods. It reaches an average accuracy of 99.78% in the public dataset with clear backdrops. Even under multiple classes and heterogeneous background conditions, the average accuracy realises 99.33% for identifying the rice disease types. The experimental findings show the feasibility and effectiveness of the proposed procedure.

Known as “the art of strokes”, Chinese calligraphy expresses its aesthetic through the strokes. A calligraphy learner practise the strokes and compose a calligraphic character by the strokes thereafter. Following the same process, the calligraphy robot, Callibot [2] needs to extract the strokes from a character. Therefore, we propose an approach to extract strokes using the geometric properties on the contour(s) of a character. A key discovery is that if two strokes intersect, the contour is concave; otherwise it is convex. The curvature vector defined in [1] is used to locate the vertexes whose interior angles are greater than 180° (these vertexes are named as C-points). C-points separate the contours into sub-contours. The corresponding sub-contours then form the basic strokes (i.e. dot stroke, horizontal stroke, vertical stroke, left-falling stroke and right-falling stroke). The experimental results show that this approach is feasible of extracting strokes from characters. This research is also useful for Chinese character recognition and calligraphic styles classification.

Chinese calligraphy is a unique form of art in the world, whose aesthetic is mainly created by the proper manipulation of the brush. However, it is impossible for a person to figure out the 6-D motion of the brush from calligraphy images, if he has no experience of writing calligraphy. In this paper, we propose a Learning from Demonstration approach for our calligraphy robot, Callibot, to acquire calligraphy skills. We first propose a new stroke parametrization approach. Then we apply Locally Weighted Linear Regression to map from the stroke parameters to the trajectory of the brush. The training data are obtained from several demonstrations. Thereafter, Callibot is capable of writing a new stroke, if the stroke's parameters are given. The resulting motion is as natural as human writing. Experimental results prove the feasibility of our proposed approach. This approach is independent of the robot and is compatible with any robot with six or more degrees of freedom. This approach can be further integrated with our previous research, i.e. stroke extraction, so that Callibot will be able to replicate calligraphy from images.

As one of the most unique types of art in Chinese culture, nowadays Chinese calligraphy is attracting increasing interests from researchers. It will be a big step if we have a robot to write Chinese calligraphy, especially in various styles, and it will form a bridge to combine science with art directly. However, there are so many different types and styles in Chinese calligraphy, and to distinguish them is the most basic quality to a green hand, but it is a big challenge for a robot to do so. For the lack of exploration about this, we conduct a lot of experiments to help the robot to accomplish it automatically. We first propose a parametric representation of calligraphic characters, and then adopt the Mahalanobis distance for similarity measurement and classification. The average accuracies of classifying types and styles of the Chinese calligraphy are 96.36% and 95.61% respectively. During the experiments, some interesting phenomena are discovered through similarity measure. Meanwhile, the parametric representation also has some potential applications, such as defining aesthetic grading standards of calligraphy and synthesizing calligraphy. Based on our research, the calligraphy robot can tell which style of calligraphy it sees for mimicking.