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Copyright of China Pulp & Paper is the property of China Pulp & Paper Magazines Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of South China Fisheries Science is the property of South China Fisheries Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Fishery Modernization is the property of Fishery Modernization Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Library & Information Science in Agriculture is the property of Editorial Board of Journal of Library & Information Science in Agriculture and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Traditional manual defect detection methods were not efficient and accurate, so wood defect detection methods were gradually becoming intelligent mechanization. The application of digital image processing technology in wood defect recognition was widely promoted, and improving its accuracy, real-time performance, and robustness has always been the focus of exploration. In this paper, the application status of image processing in wood processing, including image preprocessing, image segmentation, image defect feature extraction and recognition was introduced, the principles, advantages and disadvantages of related algorithms were summarized, and the research direction of image processing in wood processing was prospected. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the development of society and the continuous progress of science and technology, image recognition technology has developed rapidly. As a non-contact and non-destructive information monitoring technology, image recognition technology has been widely used in the field of chicken breeding, which has brought new impetus to the development of modern chicken industry. This paper briefly summarized the image recognition technology, then focused on the application of image recognition technology in chicken behavior recognition, body weight estimation, health monitoring and product classification, and finally summarized the problems and development prospects of image recognition technology in the field of chicken breeding, which was conducive to the efficient, sustainable and healthy development of image recognition technology in the future chicken industry, accelerated the comprehensive combination of artificial intelligence and chicken breeding industry, and provided technical reference basis for promoting the prosperity and development of modern animal husbandry in China. [ABSTRACT FROM AUTHOR]

Copyright of Automobile Technology & Material is the property of Automobile Technology & Material Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Early detection and distinguish of mine fires can avoid or reduce casualties, property damage, and secondary accidents. There are no natural light sources such as sunlight, moonlight, starlight, and lightning underground, and the main factor affecting the recognition of mine fire images is the mine light source. Circularity can eliminate interference from circular light sources, but it is difficult to exclude interference from non-circular light sources. Rectangularity can eliminate interference from rectangular light sources, but it is difficult to exclude interference from non-rectangular light sources. In engineering practice, due to the different shooting angles of the camera, the image of the mine light source may deform and cannot present an ideal regular shape. It is difficult to eliminate the interference of the mine light source using circularity and rectangularity algorithms. It has been revealed that the area of the external connection graphic area of the flame image is significantly larger than the actual area of the image, and the external connection graphic area of the actual light source image of the mine, such as circular lamps, rectangular lamps, and square lamps, is approximately equal to the actual area of the light source image. In this study, a mine fire recognition and anti-interference method is proposed based on the internal concavity of image, the ratio of the target image area to the external connection graphic area of the image (i.e. internal concavity of image) is calculated, and the flames and mine light sources are distinguished based on the small concavity value in the flame image and the large concavity value in the mine light source image. The internal concavity method proposed in this paper is not affected by the distance from the camera to the detection target and the size of the image, the installation position and angle of the camera to capture the detection target, and the shape of the mine light source. It has strong adaptability and high recognition accuracy. The experiment shows that the internal concave recognition method calculates the maximum average difference between the mine interference light source and the flame image, with the smallest fluctuation and the best discrimination. It is also least affected by the camera’s shooting angle and distance, and has the strongest anti-interference ability, with an accuracy of 91.6%. The rectangular recognition method calculates the average difference between the mine interference light source and the flame image, with small fluctuations and good discrimination. It is less affected by the camera’s shooting angle and distance, and has average anti-interference ability, with an accuracy rate of 72.5%. The roundness recognition method calculates the minimum average difference between the mine interference light source and the flame image, with the highest fluctuation and the worst discrimination. It is less affected by the camera shooting distance and more affected by the camera shooting angle, and has the worst anti-interference ability, with an accuracy of 12.0%. Therefore, the internal concave recognition method proposed in this paper is superior to rectangular and circular degrees, with the best discrimination, minimal influence from camera shooting angle and distance, and the strongest anti-interference ability.

The accurate identification and positioning of the anchor position of the coal mine tunneling roadway is the key technology that the drilling anchor robot urgently needs to break through to achieve intelligent permanent support. An intelligent identification and positioning method for roadway anchor hole position based on visual image and laser point cloud fusion is proposed, which includes three steps: image target recognition, point cloud image feature fusion, and positioning coordinate extraction: ① In order to solve the problem of blurred image of anchor hole contour caused by environmental factors such as low illumination, water mist and dust in coal mines, the IA (Image-Adaptive)-SimAM-YOLOv7-tiny network was used to visually identify the position of the anchor hole to be anchored in the roadway, which can adaptively enhance the image brightness and contrast, recover the high-frequency information at the edge of the anchor hole, and make the model focus on the characteristics of the anchor hole, so as to improve the success rate of anchor hole detection; ② The Region of Interest (ROI) of image detection is generated by perspective projection relationship to generate a cone-shaped area of interest to obtain the corresponding target point cloud cluster; ③ The point cloud processing algorithm is used to extract the anchor hole boundary point cloud, obtain the central coordinates of the hole position and its normal vector, and judge the correctness of the anchor hole recognition by comparing the coordinate depth difference. In this paper, a drilling and positioning system for bolting trolley manipulator is built to verify the accuracy and accuracy of the algorithm's autonomous positioning, and the experimental results show that the mean average precision (mAP) of the IA-SimAM-YOLOv7-tiny model is 87.3%, which is 4.6% higher than that of the YOLOv7-tiny model. Compared with the single vision method, the fusion algorithm proposed in this paper has a positioning error of 3 mm, and the average recognition time of the system in the case of a single anchor hole is 0.77 s, compared with the single vision method, the fusion of laser and visual multi-source can not only reduce the influence of environment and small sample training on positioning performance, but also obtain the normal vector of the anchor hole position, which provides a basis for the manipulator to adjust the drilling posture to achieve accurate anchoring.

In few-shot learning, the Brownian distance covariance improves classification accuracy by enhancing feature embeddings, but it does not focus on the issue of sample-related features in classification. This paper proposed the deep masked Brownian distance covariance method that generated query-guided masks based on high-dimensional semantic relationships between each pair of query and support samples, and employed the masked Brownian distance covariance matrix as the image features. Under the 5way-1shot and 5way-5shot scenarios, it carried out validation and evaluation on CUB-200-211, MiniImageNet, and Tiered-ImageNet datasets. The experiments show that this method achieves superior classification accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai University / Shanghai Daxue Xuebao is the property of Journal of Shanghai University (Natural Sciences) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The image recognition method based on sequential three-way decisions only considers the signed distance between the testing samples and the support vector machine（SVM） separating hyperplane, ignoring the information of the existing granular layer, which can further improve the performance of image recognition by this means. This paper proposed a new dual confidence score and ensembling（DCE）, which synthesizes signed distance with the distance between the testing samples and the center of each class of training samples in the feature space as the basic confidence score. The current granularity layer integrated with the confidence score of coarse-granularity layer which insufficient to recognized images. DCE improved the accuracy of recognition by the existing information between granularity layers. Meanwhile, DCE enhanced the recognition efficiency as making decisions at the coarser granularity layer. Finally, this paper utilizes two classical pre-trained models for experimental verification on Caltech-101 and Caltech-256 datasets. The experimental results show that this approach can effectively improve the recognition accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Copyright of Railway Signalling & Communication Engineering is the property of Railway Signalling & Communication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Forest Products Industry is the property of China Forest Products Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Rock ore slice identification is a task that requires a high level of expertise. Manual identification often results in unavoidable subjective errors and is highly inefficient. Deep learning image recognition technology can efficiently perform rock ore slice identification, but training deep learning models requires a large amount of annotated data. Therefore, it is important to find efficient ways to utilize limited annotated data. By adopting a multi-label classification approach, a classifier can be trained on a labeled dataset, and then this classifier is used to generate pseudo-labels for a large number of unlabeled rock ore slice images. Finally, the model is retrained using the labeled training data and all the unlabeled data. The results show that the use of multi-label classification approach for identification of rock ore slice structures and minerals is feasible. Additionally, this paper employs a semi-supervised learning approach to train the model and improve the model’s generalization ability without requiring a large amount of manual annotation. [ABSTRACT FROM AUTHOR]

Copyright of Mining & Metallurgy (10057854) is the property of Beijing Research Institute of Mining & Metallurgy Technology Group and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

It is important to judge the density of pear flower accurately for automatic thinning. In order to judge the density of pear flowers precisely and achieve the purpose of automatic flower thinning, a density classification of pear flower images method based on improved density peak clustering algorithm was proposed in this study. The pear flower image samples used in the experiment were taken from the research base of Hebei Agricultural University, Niugang Village, Yi County, Baoding City, Hebei Province, and the variety was three-year-old Qiuyue. In this study, the images of pear flowers in natural environment were collected on April 18, 2021, when the pear flowers were in full bloom. The pear flower recognition images with high detection accuracy were obtained by using the depth learning model to detect the pear flower. The central coordinates of the target detection frame were extracted from the recognition images to obtain the data points that needed to be clustered. Secondly, in order to achieve the density classification of pear flower images, the method of selecting the cluster center was improved according to the shortcomings of the original density peak clustering algorithm in the density classification of pear flower and the requirements of pear flower density classification. The decision graph was divided into four parts by four groups of local density and center offset distance segmentation thresholds to select the cluster centers. These four parts corresponded to four grades of high, medium and low density and no thinning treatment. The accurate classification of pear flower images with reasonable density was realized. Finally, to solve the problem of inaccurate clustering classification of pear flower distribution with only cluster distribution, sparse distribution and large-scale close-up, the calculation method of dij parameter of distance between two points was improved. The pear flower scale and density grading standard were unified, and reasonable density grading could be achieved for all distribution types of pear flower images. The experimental results showed that the proposed algorithm could adapt to pear flower images of different scales. The accuracy of scale prediction was 94.89%, and the accuracy of density classification was 94.29%. Compared with the existing methods, the proposed method could achieve the density classification of local flower clusters in natural environment. the algorithm in this paper was compared with K-means algorithm and DBSCAN algorithm, and the accuracy of density classification was 94.29% and 68.57% respectively. Although the density classification accuracy of K-Means clustering algorithm was high, it lacked the algorithm steps to automatically select the clustering centers of different density classification, and was too sensitive to noise points. The density calculation method of DBSCAN algorithm lacked continuity, and the division of density grade was not precise enough. Based on the comprehensive analysis of density classification accuracy and cluster center selection method, the improved density peak clustering algorithm in this paper had the best effect. The clustering centers of different density grade could be automatically selected and the error of random initialization of clustering centers could be reduced. The proposed algorithm was robust to noise points and could eliminate noise points effectively. The proposed algorithm adopted soft statistics method for density calculation, which had continuity and was more accurate for density classification. It provides technical support for analyzing the density of pear flower and machine intelligent flower thinning. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Hydrobiology is the property of Editorial Department of Journal of Hydrobiology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Henan Agricultural Sciences is the property of Editorial Board of Journal of Henan Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Railway Signalling & Communication Engineering is the property of Railway Signalling & Communication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Agricultural Science & Technology (1008-0864) is the property of Journal of Agricultural Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of East China University of Science & Technology is the property of Journal of East China University of Science & Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Zhejiang University (Science Edition) is the property of Journal of Zhejiang University (Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Food & Machinery is the property of Food & Machinery Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Railway Signalling & Communication Engineering is the property of Railway Signalling & Communication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the leapfrog development of the railway industry in China, the operating speed and density of the railways increase gradually, and large tamping machinery for track maintenance is used more widely. Traditional manual tamping truck has high requirements for operators and high working intensity. In order to achieve the goal of reducing the intensity of manual work and ensuring the efficiency of on-site construction, this paper proposes a design scheme of intelligent automatic tamping system based on DCL-32 tamping truck without changing the function and mechanical structure of the original machine, and focuses on the analysis of the railway track sleeper detection and positioning of the pick. In this scheme, the YOLOX model is used for image detection, and the recognized results are checked with the output mileage of laser ranging sensor, to complete the detection of track sleepers quickly and accurately. Based on the historical data of tamping truck braking distance during railway maintenance, this paper fit a braking relationship curve to predict the tamping position. The results of field prototype loading test show that the speed of automatic tamping is equal to that of manual tamping under single and double tamping modes. The control scheme of automatic tamping system proposed in this paper has a good trial effect, which lays a foundation for subsequent product promotion.

[Purpose/Significance] We used to process soybean leaf data by looking at them and process data manually, but this method is very inefficient. In order to improve the classification accuracy and efficiency of soybean leaf images, further for storage and management of these images, we used the deep learning technique to make an in-depth study of text data and image data of soybean leaves for the image recognition and classification. The application of deep learning in agricultural data management mainly focuses on the image recognition and classification of plants and plant phenotypes in large-scale data, detection and classification of agricultural diseases and pests, detection and classification of crops and weeds, and prediction of crop yield. Through case analysis, our sample data demonstrated the application process of deep learning technology. [Method/Process] This paper systematically described the whole process of classification and recognition of agricultural data by using the deep learning technique. Through recognition and disease monitoring of plant leaves, the leaf morphology of soybean plants in the soybean experimental field of Heilongjiang Academy of Agricultural Sciences was taken as an example. We analyzed the image features of soybean leaf morphology, and carried out the classification and recognition research of soybean leaf morphology based on deep learning. Deep learning techniques have replaced shallow classifiers that use manual feature training and can identify soybean leaves with a high degree of accuracy as long as sufficient data are available for training. We adopted DenseNet model, which is suitable for common network model. The advantages of this model are that it has the best performance and the least storage requirements. First,we selected support vector machine (SVM) and random forest (RF) in traditional machine learning methods to identify soybean leaf morphology. Second, AlexNet and ResNet were selected to identify soybean leaf morphology. Finally, the recognition accuracy of different methods were compared with the DenseNet network adopted in this paper. [Results/Conclusions] Through the training of DenseNet model, the recognition accuracy of 94% was achieved, which successfully solved the problems of long time, low efficiency and low recognition accuracy of traditional methods in processing image classification of soybean leaves, and could meet the actual needs of agricultural image data classification. Future research efforts will strive to collect a wide range of large and diverse data sets to facilitate soybean leaf recognition, and focus on developing reliable background removal techniques and incorporating other forms of data to improve the accuracy and reliability of soybean leaf recognition systems.

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Optimal granularity selection is a critical link in constructing multi-granularity features in autoencoder networks. Aiming at the problem of unreasonable granularity selection method in autoencoder networks, which leds to unsatisfactory feature extraction effect, high misclassification cost, and high testing cost, this paper proposed a granularity layer selection strategy based on mini-batch gradient descent (MBGD). This approach reconstructed the multi-granularity space by changing the granularity selection method and designed a new cost-sensitive multi-granularity three-way decision model based on a deep belief network (DBN). The superior granularity selection method enhanced the feature extraction ability of the network, promoted the construction of multi-granularity space to develop towards the fastest reaching the most fine-grained area, and reduced the image reconstruction error to achieve more minor misclassification cost and test cost. The experimental results show that providing a reasonable granularity selection strategy improves the decision accuracy of the cost-sensitive multi-granularity three-way decision model, and can obtain the optimal granular layer with the smallest total cost faster under the given cost. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Intelligent Agriculture Mechanization is the property of Nanjing Institute of Agricultural Mechanization and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)