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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 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 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 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 Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. 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 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 Ordnance Equipment Engineering 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 Harbin University of Science & Technology is the property of Journal of Harbin University of 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 Ordnance Equipment Engineering 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 South China University of Technology (Natural Science Edition) is the property of South China 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.)

In response to the scarcity of defect samples in industrial applications, this paper proposed a method for segmenting surface defects in metal workpieces with only a minimal number of required defect samples. The method combined image generation techniques with a semi-supervised learning strategy. It utilized small-sized defect patches, and extracted from a minimal number of defect images to train a defect generation model. Subsequently, the method integrated these generated defect images into normal images to facilitate data augmentation. Additionally, the method applied a semi-supervised learning strategy to train the segmentation network, aiming to mitigate the adverse effects of differences between generated and real data distributions. The experimental phase involved conducting tests on a real-world computer vision detection system for metal workpieces. The results demonstrate that the semi-supervised training strategy significantly enhances the segmentation model's generalization ability to real defects. The method achieves high segmentation accuracy using only five defect sample images. [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.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. 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 Guangdong University of Technology is the property of Journal of Guangdong 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.)

To address the challenge of accurately extracting phenotypic parameters from in situ root images collected from minirhi‐ zotrons amidst background noise interference, this paper proposes a minirhizotron root phenotypic parameter measurement system based on an improved U-Net model. In the U-Net network, optimized ASPP (Atrous Spatial Pyramid Pooling） and ECA (Efficient Channel Attention） modules are employed to increase the receptive field and enhance the ability to capture detailed features, thereby obtaining precise segmentation images. The experimental results show that the mean intersection over union and mean pixel accuracy of the improved U-Net model are 87. 07% and 91. 85%, which are 2. 49% and 2. 3% higher compared to the original U-Net, respec‐ tively. Comparing with measurements obtained using WinRHIZO root analysis software, the determination coefficients for the root length and area are 0. 951 8 and 0. 984 9. respectively. The Spearman correlation coefficients are 0. 972 5 for the root length and 0. 975 7 for root area. This indicates the system′s capability to accurately measure the root length and area. [ABSTRACT FROM AUTHOR]

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 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.)

The quality of cocoons directly affects the quality of raw silk. Before reeling, the cocoons should be sorted to remove the inferior cocoons that cannot be used for reeling. The double cocoon is the best raw material for making silk quilts. Therefore, it should be separated from other types of inferior cocoons and classified separately in the process of selecting cocoons. The traditional cocoon sorting basically relies on manpower, which involves high labor intensity, low efficiency of cocoon selection and high missing rate, and the accuracy of cocoon selection is easily affected by subjective factors such as the technical level and mental state of cocoon selection personnel. With the rapid development of artificial intelligence, machine vision technology is widely used to replace human eyes for detection and recognition. At present, in machine vision technology, the recognition of double cocoons mainly includes area threshold method and the threshold method of the ratio of long to short axis of ellipse. The disadvantage of the area threshold method is that small double cocoons cannot be detected. The threshold method of the ratio of long to short axis of ellipse is not applicable to the double cocoon of the head-to-tail type and relies too much on the accurate fitting of the cocoon shape. However, due to the slight irregularity of the cocoon shape, the attachment silk and the adhesion between the cocoons, the ellipse fitting is different from the actual shape, which may result in people’s misjudging the cocoon type near the threshold. Therefore, there are some limitations in the identification of double cocoons by only depending on the area threshold or the threshold of the ratio of long to short axis of the fitted ellipse. Due to the dense distribution of cocoons in the cocoon image and the interference of attached silk, the gray scale of adjacent cocoons is similar, so it is difficult to conduct segmentation completely by traditional image segmentation methods. Therefore, the segmentation and counting problem of target cocoons should be solved first for the classification and recognition of cocoons. Aiming at the problems that it is difficult to accurately segment the adhered cocoons and the limitations of the existing double cocoon recognition methods, we proposed a series of algorithms for the segmentation and counting of cocoons, the shape restoration of cocoons and the recognition of double cocoons. Firstly, we proposed an improved image segmentation algorithm based on variable segmentation threshold by combining with morphological operation. Otsu binarization threshold was used as the initial segmentation threshold, and binarization, morphological erosion operation and morphological expansion operation were performed on the image. The pixel area threshold and length-width threshold of single cocoons were used to distinguish the connected region. If a cocoon is a single one, it is marked and counted. If it is determined to be connected to other cocoons, the segmentation threshold is increased again for another segmentation. Then, a cocoon shape restoration method based on edge detection and ellipse fitting was proposed. The pixels of each cocoon that had been segmented were traversed. The Canny operator was used to detect the edge of the original image. The pixel points of the segmented cocoon were used to determine the edge points of the cocoon, and the edge points were elliptically fitted to obtain the image of the cocoon. Finally, the ratio of long to short axis of the cocoon and the area parameters were calculated, and the double-parameter threshold was used to recognize the double cocoon. And then, the serial algorithm proposed in this paper was used to segment cocoon graphics, restore cocoon shape, and distinguish double cocoons in 10 cocoon images. The results show that the 222 cocoons of all the ten pictures are completely segmented, and the counting accuracy is 100%. A total of 46 double cocoons in the pictures are detected, and the classification accuracy of double cocoons and single cocoons is 98.6%. This method detects the small double cocoons which are easy to be missed by the area threshold method, and accurately identifies the cocoon type which is close to the threshold of the ratio of long to short axis. The series algorithm of cocoon target segmentation and double cocoon recognition proposed in this paper can effectively process the machine vision image to get the conclusion whether each cocoon is a double one. In the series algorithm, the pre-experiment is used to determine the area of the normal cocoon and the length of the long axis of the normal cocoon as the prior conditions in the captured image. The cocoon target with slight overlap can be separated, but the cocoons with serious overlap may be mistakenly divided into the same group, thus affecting the classification and recognition of the cocoon. Therefore, this method is suitable for the image processing of cocoons laid flat in the field of vision. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering 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 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.)

Copyright of Journal of Fuzhou University is the property of Journal of Fuzhou University, 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 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 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.)

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.)

Droplets deposition characteristics estimation is helpful to know the pesticides deposition distribution on crops, which is related to the crop spray quality directly, and especially the fast detection method will provide the basis for improving the pesticides spraying technology. Droplets image processing based on the water sensitive paper is one of the most common methods to detect the droplets deposition characteristics. The droplets coverage percentage and coverage density were selected to evaluate deposition distribution in this paper. With the development of science and technology, a smart phone was selected as image acquiring tool to replace the special image acquisition system, and the improved image processing algorithm of the water sensitive paper was developed. There are 3 steps in the image processing algorithm to obtain each droplet stain, which are image preprocessing, droplets area segmentation, and overlapped droplets segmentation. Firstly, the image was enhanced and calibrated by a calibration board, and then the image of water sensitive paper was segmented from the R channel and B channel image and transferred to a gray scale image. Secondly, the blue droplets stain area was segmented from yellow paper background, and the dynamic threshold method based on the pixel position was used to solve the problem of the influence of uneven brightness in this step, in which the gray mean value of an area of 25×25 pixels was calculated as the segmentation threshold for the middle pixel, and the droplets coverage percentage was calculated by the stains pixel number divided by the pixel number of water sensitive paper area. Thirdly, the circulatory segmentation method based on region circularity was designed to segment the multiple-droplet overlapped regions. Based on the statistical analysis, 0.6 was selected as the circularity threshold, less than 0.6 was considered to be overlapped droplets, and first erosion and then dilation based on a circle with the radius r were performed; next the segmented area circularity was recalculated, the area with the circularity of greater than 0.6 was selected again and segmented by the circle with the radius (r+1), and the erosion-dilation operation would be repeated over and over until the circularity of all stains was greater than 0.6. Finally, the identified droplets were marked as circle, and the droplets coverage density was calculated by droplets number divided by the water sensitive paper area. Experiments were conducted to test and verify the detection advancement of the proposed image processing algorithm. Experimental results showed that the dynamic threshold segmentation method is not affected by the uneven brightness and can extract 92.64% of droplets, and the droplet coverage percentage detection result is 12.57% and 8.74% greater than constant threshold and partitioned threshold method respectively. Moreover, the proposed overlapped droplets segmentation algorithm can segment successfully more than 2 droplets and the long and thin droplets, the accuracy of droplets identification is 97.2%, and the coverage density detection results showed that the relative error between the algorithm in this paper and manual counting is only 3.31%. The results indicated that the proposed image detection algorithm of droplets deposition characteristics is efficient and convenient, and can completely fulfill the demand of droplets deposition characteristics detection in the field, and the corresponding smartphone applications are in development. [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.)

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 Tourism Tribune / Lvyou Xuekan is the property of Tourism Institute of Beijing Union University 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 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 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 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 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 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.)

[Objectives] The urediniospores of wheat stripe rust pathogen is an important reason for the outbreak of wheat stripe rust. Aiming at the problems of low detection precision and low segmentation precision in the identification of microscopic image of urediniospores, an automatic detection method for urediniospores of wheat stripe rust pathogen based on spin Unet++ network was proposed. [Methods] According to the characteristics of dense and conglutination of the urediniospores in the microscopic image, a spinning connection structure was proposed by improving the feature extraction network of Unet ++, which optimized the segmentation precision of Unet ++ network and improved the segmentation rate of detection. According to the morphological characteristics of spores, a weighted mapping rectangle calculation formula was proposed to calculate its rectangular thermogram, so as to improve the precision of spore detection. [Results] The algorithm test results showed that the average precision of the improved algorithm reached 99.03%, and the segmentation rate was 86.45%, which met the requirements of accurate detection. Compared with the original CenterNet model, the overlap rate increased by 10.35 percentage points, the precision increased by 0.46 percentage points, and the memory usage reduced by 66.09%. [Conclusions] The model in this paper not only ensured a high precision of detection of urediniospores, but also ensured a high spore segmentation rate, which provided an effective method for the early warning of wheat stripe rust. [ABSTRACT FROM AUTHOR]

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 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 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 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 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.)

[Objectives] The paper aimed to explore the method of real-time monitoring of insect pest situation in greenhouse and accurate control of the attacks by pests. [Methods] In this paper, an adaptive segmentation method based on background homogenization, combined with a random forest (RF) classification method was designed to identify and count four categories of tomato pests (Bemisia tabaci, Chromatomyia horticola, Drosophila melanogaster and Laingia psammae) in greenhouse. Firstly, this method extracted the B components from RGB(red-green-blue) color model and the V components from HSY (hue-saturation-value) color model in trapping hoard image, and then it adjusted the background gray level in sections to achieve the uniform background trapping hoard image respectively. Then it used the maximum inter-class variance method to determine the threshold to segment the image, and two binary images of trapping hoard were fused after morphological processing. Finally, 6 color features, 8 shape features and 6 texture features extracted from the pest region were inputted into the random forest classifier, which was trained to identify and count the pests. [Results] By comparing and analyzing Sauvola local threshold method, Prewitt edge segmentation method, k-means clustering method and the method of this paper, it was found that the adaptive segmentation based on background homogenization had the best effects, and the average segmentation accuracy was 95.34%. By comparing and analyzing the method of C-SYC(C-support vector classification), BP(hack propagation) neural network, random forest with 7 kinds of feature vectors, it was found that random forest classification method with the inputs of the integration of the color features, the shape features, and the texture features was more effective, and the respective recognition accuracy for B. tabaci, C. horticola, D. melanogaster and L. psammae was 93. 89%, 90. 71%, 91.54% and 90.40%. [Conclusions] The algorithm can realize the recognition and counting of 4 kinds of pests on the trapping hoard and provide theoretical and methodological references for monitoring and early warning of pest situation in greenhouse. [ABSTRACT FROM AUTHOR]

Aiming at these problems that image segmentation model oriented on level set model can not accurately segment object and is sensitive to noise and initial contour,this paper proposed an active contour model based on joint constraint with superpixel/pixel and sparse decomposition.First,it introduced the superpixel to extract image block information and constructed a symbol pressure function to prevent contour evolution from falling into local optimization.Then it constructed the energy function relied on superpixels and pixels to remedy the defect that the superpixels couldn’t retain local detials.In order to solve the problem of slow evolution speed for active contour model based on non-global information,it utilized superpixels to accelerate the contour evolution.Finally,this paper introduced the constraint conditions based on sparse decomposition to weaken the influence of local noise.Compared with various level set methods,the experimental results verify the effectiveness of the proposed method.The proposed method is more robust to noise and initial contour location,the average Jaccard similarity coefficient is increased by 34% compared with original selective binary and Gaussian filtering regularized level set. [ABSTRACT FROM AUTHOR]