The classification and grading of flue-cured tobacco leaf is an essential step in tobacco production. In order to carry out relevant research, a new flue-cured tobacco leaf image database (FTIDB) is established to provide high-quality and annotated flue-cured tobacco leaf image data. At present, the database consists of 2,113 flue-cured tobacco leaf images from major production regions across China. To ensure the quality of the database, a series of procedures are implemented. Firstly, the image acquisition system was constructed and the acquisition environment was calibrated. Secondly, Image pre-processing was adopted to improve the image quality. Thirdly, according to the characteristics and application requirements of tobacco leaf image, the image quality was objectively evaluated through quantitative metrics in terms of color, texture and greyscale. Fourthly, XML file was used to organize annotated information from tobacco experts. Finally, the image data dictionary was created to complete the data storage management using Microsoft Structured Query Language Server. This database can provide basic tobacco image resources for researchers and agricultural technicians, so that extensive studies can be performed and the automatic level of tobacco classification and grading will increase effectively.
The detection of cigar appearance defects is a critical step in the cigar production process and holds significant importance in ensuring the quality of cigar production. To address the issues of inefficient and unstable manual detection, this paper employed deep learning-based object detection models for cigar appearance defect detection and proposed an algorithm for cigar appearance detection based on YOLOv5. First, the cigar appearance defect acquisition equipment was constructed, the defect image data were collected, and the experimental dataset was established. Then, the deformable convolution was introduced to enhance the learning capability of the backbone network. Furthermore, the Bi-directional Feature Pyramid Network (BiFPN) was employed to improve the feature information of each layer. Lastly, the spatial context pyramid (SCP) was utilized to enable global spatial context learning within the feature layers, further enhancing the features. The model performance was evaluated by mean average precision (mAP). Experimental results demonstrated that the improved YOLOv5 achieved a mAP of 90.7% for cigar appearance defect detection and a detection speed of 10.6ms per image, showcasing excellent detection accuracy and speed. Moreover, this model exhibited significant improvement in detecting small defects and defects located at the edges. Therefore, the improved YOLOv5 model satisfied the requirements for automatic cigar appearance defect detection.
Conventional spectral imaging contains two-dimensional and spectral-dimensional information, but lacks spatial depthdimensional information. The light field imaging technique can acquire both intensity and direction information of the light simultaneously, so that the depth information of the target can be extracted and processed. This paper applies light field imaging technology to spectral imaging, builds a spectral light field imaging system with spectral range in the visible band and resolution of 10 nm, and proposes the theory of recovering true color images from spectral dimensional images of the same viewpoint and then performing depth estimation, and realizes an adaptive depth estimation algorithm for multiinformation fusion. The algorithm uses the correspondences information of the angular entropy representation, the scattered focus information of the color similarity constraint to obtain the initial depth and confidence level, and then fuses with the edge information to achieve the depth estimation of the scene. The experimental results show that the algorithm can solve the occlusion problem well and the edge information of the depth map is preserved intact. The theory and algorithm provide new ideas for the three-dimensional modeling of spectral images and lay an experimental foundation for the extension of spectral imaging technology to three-dimensional space and its wide application in object recognition and classification, three-dimensional display, biomedicine and other fields.
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