Search

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.)

Abstract: Melanin grading has been widely used in the epidermal layer of live yellow feather broilers. However, it is in high demand to improve the efficiency, cost-saving and susceptibility to lighting conditions. This study aims to explore an intelligent melanin grading (ConvNeXt-WPCA) for the epidermal layer of live yellow feather broilers using the ConvNeXt model. Three key enhancements were proposed to improve the ConvNeXt-WPCA model for the recognition of melanin in broilers. Firstly, the channel weights of the input images were adjusted to treat the uneven distribution of melanin across the RGB channels in the melanin images of yellow feather broilers. The channel with more melanin was then emphasized to extract the melanin features. The reweighting of channels was used to more effectively aggregate the melanin signals for the better classification performance of the deep learning model. Secondly, Depthwise Separable Convolution (DWConv) was replaced with the partial convolution. The computational load and memory access times were reduced to improve the utilization of computational resources. Lastly, the Coordinate Attention (CA) module was introduced to focus on the key skin regions near the chest and anus of yellow feather broiler, thereby improving the classification accuracy of models. At the same time, a dual-light source image acquisition device was designed to efficiently and simultaneously collect images under both normal and polarized lighting conditions. Sufficient data was available for model training and performance evaluation. There was a minimum impact of lighting conditions on grading. Furthermore, the potential application of polarized light was also explored in the tasks of melanin grading. The results demonstrated that the ConvNeXt-WPCA model was improved by 9.68 percentage points in the grading accuracy rate of the melanin image dataset for the yellow feather broiler under natural light, compared with the standard ConvNeXt model. A final recognition accuracy rate reached 89.03%. The grading accuracy rate was improved by 15.26 percentage points in the polarized light ones, with a final recognition accuracy rate of 98.87%. Moreover, both the parameters and floating-point volume were reduced. In conclusion, the melanin image grading of the yellow feather broiler epidermal layer under polarized light conditions was superior to that under natural light. The high recognition accuracy was achieved in the ConvNeXt-WPCA melanin grading for the epidermal layer of yellow feather broilers. This finding can provide a theoretical basis and technical support for the practical application of intelligent melanin grading in the epidermal layer of yellow feather broilers. Significant implications were obtained for the accurate melanin grading of broilers in poultry industry. The ConvNeXt-WPCA model improved the accuracy of melanin grading with the required computational resources, indicating the practical and efficient solution to real-world applications. The model can also be further optimized for the application in other poultry species. Keywords: model; deep learning; attention mechanism; yellow feather broilers; ConvNeXt; quality grading; polarised light [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China Agricultural University is the property of Gai Kan Bian Wei Hui 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.)

A method was constructed to extract individual plant image from the continuously-collected images. The leaf surface region segmentation was obtained, and the individual plant image was separated based on leaf surface image by leaf distance. The two consecutive adjacent images were stitched together, and the newly-formed images were transformed and stitched with empty images to produce individual plant image for determining the centroid. The distance between the centroid of all stitched images in the previous image and the centroid of all the stitched images in the subsequent image was compared. The distance threshold method was used to identify whether the two images were the images of the same crop. Transfer closure was applied for obtaining the plant image set of each crop in multiple continuous images, and the image closest to the center was selected as the best image. Five crops were used to verify the proposed method. The results show that the accuracy of plant number reaches 100%. The proposed method can successfully and precisely extract individual plant image with high computational efficiency. [ABSTRACT FROM AUTHOR]

Waterfowl farming in China is developing rapidly in the direction of large-scale, standardization and intelligence. The research and application of intelligent farming equipment and information technology is the key to promote the healthy and sustainable development of waterfowl farming, which is important to improve the output efficiency of waterfowl farming, reduce the reliance on labor in the production process, fit the development concept of green and environmental protection and achieve high-quality transformational development. In this paper, the latest research and inventions of intelligent waterfowl equipment, waterfowl shed environment intelligent control technology and intelligent waterfowl feeding, drinking water, dosing and disinfection and automatic manure treatment equipment were introduced. At present, compared to pigs, chickens and cattle, the intelligent equipment of waterfowl are still relatively backward. Most waterfowl houses are equipped with chicken equipment directly, lacking improvements for waterfowl. Moreover, the linkage between the equipment is poor and not integrated with the breeding mode and shed structure of waterfowl, resulting in low utilization. Therefore, there is a need to develop and improve equipment for the physiological growth characteristics of waterfowl from the perspective of their breeding welfare. In addition, the latest research advances in the application of real-time production information collection and intelligent management technologies were present. The information collection technologies included visual imaging technology, sound capture systems, and wearable sensors were present. Since the researches of ducks and geese is few, the research of poultry field, which can provide a reference for the waterfowl were also summarized. The research of information perception and processing of waterfowl is currently in its initial stage. Information collection techniques need to be further tailored to the physiological growth characteristics of waterfowl, and better deep learning models need to be established. The waterfowl management platform, taking the intelligent management platform developed by South China Agricultural University as an example were also described. Finally, the intelligent application of the waterfowl industry was pointed out, and the future trends of intelligent farming with the development of mechanized and intelligent equipment for waterfowl in China to improve the recommendations were analyzed. The current waterfowl farming is in urgent need of intelligent equipment reform and upgrading of the industry for support. In the future, intelligent equipment for waterfowl, information perception methods and control platforms are in urgent to be developed. When upgrading the industry, it is necessary to develop a development strategy that fits the current waterfowl farming model in China. [ABSTRACT FROM AUTHOR]

The vital behaviors of Magang geese are closely related to their growth and welfare status. Therefore, it is very necessary to accurately identify the key vital behaviors of Magang geese in husbandry and production. This study aims to efficiently and accurately monitor the key behaviors of Magang geese in pens. An improved algorithm was also proposed for the vital behavior of Magang geese using Double Head-YoloX (referred to as MGBM-DH-YoloX). The detection efficiency of the network was enhanced to reduce the number of YoloX heads. A tradeoff was made to balance the foreground and background using the Focal loss objective function. Efficient network training was achieved using migration training. Other techniques were utilized to identify the vital behavior and count their key behaviors once at a fixed time in an efficient and accurate way, including the identification and rule analysis of key behaviors, such as feeding, drinking, rest and stress of Magang Geese. Firstly, MGBM-DH-YoloX enhanced the data of the Magang geese images with Mosaic and Mixup. And then the enhanced dataset was used to train the DH-YoloX (Double Head - YoloX) for the detection of the vital behavior of the waterfowl. Finally, the vital behavior of the Magang geese was counted every 25 frames. The experiment was conducted in a flock-reared environment with the Magang geese as the subject of the case study. A behavioral target detection test was conducted during this time. Waterfowl videos were collected on multiple days from 26 December 2021 to 04 January 2022, and from 10-12 May 2022 at the Nanwei Building Experimental Farm, School of Animal Science, South China Agricultural University, Tianhe District, Guangzhou City, Guangdong Province, China. A platform was also built to acquire the data for the behavior detection of geese. The color images of Magang geese were facilitated to integrate the environmental factors in the vicinity of the breeding pen (the height of the pen, as well as the width and height of the equipment box). The bi-directional difference frame was adopted to extract the key frames, in order to quickly extract the required picture production dataset from the video data. A total of 1 600 picture data was extracted from the video data, and selected for a total of 2 000 picture data. A total of 1 600 images were randomly used for the training and validation sets, whereas, 400 images were for the test set in the 10 days of continuous activity video. The results showed that the MGBM-DH-YoloX algorithm achieved an average accuracy of 98.98% mAP, a processing frame rate of 81 frame/s, and a memory consumption of 2 520.04 MB for the detection of Magang geese’s behaviors. Meanwhile, it was found that the geese were foraged less frequently, as they grow older, after monitoring the 10-day breeding data. The vital foraging and drinking behaviors simultaneously accounted for 83.74% of the total foraging time per day, indicating the overall companionship trend. There was also a decrease from 90.78% to 74.57%, indicating the gradual separation from the feeding and drinking behavior of Magang Geese with the increase of age. On the contrary, the resting behavior of the experimental geese increased slowly with the increase of age, indicating a gradual adaptation to the caging. The stress behavior was highly random in this experiment. There was an extremely serious emergency caused by irregular feeding, such as the random walking of personnel. Consequently, the MGBM-DH-YoloX can be expected as the video monitoring to intelligently extract the vital behavior of Magang geese. The finding can provide technical support for the automated monitoring of poultry in intelligent breeding supervision. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China Agricultural University is the property of Gai Kan Bian Wei Hui 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 Agricultural University is the property of Gai Kan Bian Wei Hui 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 Agricultural University is the property of Gai Kan Bian Wei Hui 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.)

The Internet of things (IOT) industry is a highly potential industry with wide application prospect in all sectors of society，it needs a lot of high quality talents. Based on the background of the IOT and the training objectives of the network engineer specialty，South China Agricultural University carried out a reform of the practical teaching for network engineering specialty In order to highlight the agricultural Internet of things，this paper introduced the training objectives of the network engineer practice teaching，and the practical teaching reform methods，ideas and measures in our school. Teaching practice showed that students have been greatly improved in mastering basic knowledge and engineering practice ability， especially in design ability to IOT. The students have been with stronger employment competitiveness. Almost three years of reform practice showed that graduate students in the field of network engineering practice had made great progress in recent years，and their competitiveness of employment，professional satisfaction of students were increased year by year. [ABSTRACT FROM AUTHOR]

Leaf surface region segmentation from crop images is a crucial step for obtaining crop information. The leaf surface region segmentation （LSRS）from crop images was proposed. The color segmentation was used to obtain green region in crop images， and the smoothness segmentation was applied to identify smooth parts in green regions. Using the erosion method and the pixel number of each region， the noises were eliminated by LSRS to separate big noise and leaf regions into different parts by connectivity scheme. The dilation method was finally used to obtain whole leaf surface region. The experiments were conducted with several crop leaf images to verify the proposed method.The results show that compared to existing methods, the proposed system can automatically segment leaf surface region from complicated crop images. [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.)

Bactrocera Dorsalis is a type of invasive pests and causes serious damage to many important economic crops. In order to monitor Bactrocera Dorsalis accurately, and to reduce the misjudgment of light-induced problem, a remote monitoring system which combined a hardware equipment and a software system was designed. The hardware equipment was optimized based on our preliminary design for Bactrocera Dorsalis trapping. By improving the shading system, solar devices and 4G communications devices, the hardware equipment now can be self-powered and integrate the insect pest information collection, processing and transmission as a whole functionality. In addition, this paper presents a good human-computer interaction software system, which includes the Bactrocera Dorsalis monitoring programs, the server and the client-side. The monitoring program can monitor the trapping process, precisely calculate the number of Bactrocera Dorsalis, and automatically transmit the results to the remote server or store it in a local storage card. Users are convenient to obtain the monitoring information through the client-side. In order to improve the accuracy of the Bactrocera Dorsalis detection algorithm(BDDA) and achieve the requirements of real-time systems, an algorithm named Bactrocera Dorsalis detection algorithm under lighting variations(BDDA-LV) was proposed. The BDDA-LV began by selecting appropriate background model according to the light conditions. Then the background difference method was used to extract Bactrocera Dorsalis target preliminarily. Median filtering and morphological filtering for the image was used to reduce white noise, and eliminate holes in the target area to improve the image quality. The image was then divided into blocks based on the adjacent pixels of the image and used these blocks for Geometric feature matching, so that the Bactrocera Dorsalis area can be extracted. Both BDDA-LV algorithm and BDDA algorithm were tested by two sets of data. These data came from the pre-acquired image dorsalis, and was divided into two datasets, one of which was under the moderate influence of light and the other one was under the severe influence of light. Each dataset had 150 images. In the influence of moderate lighting variation, the error rate of BDDA-LV algorithm was 7.21% and 23.07% lower than BBDA algorithm, and its computing time was 41.7% of the BBDA algorithm. In the influence of strong lighting variation, the error rate of BDDA-LV algorithm was 12.4% and 23.07% lower than the BBDA algorithm, and its computing time was 42.2% of the BBDA algorithm. By the long-term tests of the system in the Guangzhou Yangtao Park, the system ran stably and no power outage occurred. A comparative analysis of monitoring system records and artificial counting were carried out from June 18 to 24, 2015. The monitoring system counting was 1634 while artificial counting was 1613. The accuracy of the monitoring system was 98.7%, which improved 3.8% than the preliminary system. The experiments showed that the accuracy and efficiency of the BDDA-LV were higher than the original BBDA algorithm, and the whole system had higher precision and stability. This system can also easily provide accurate pest monitoring information to the regional monitoring personnel in real time, which improves work efficiency. At the same time, the system can provide information support for agricultural researchers to study on the insect activity. This system is thought to be valuable and applicable. [ABSTRACT FROM AUTHOR]

Along with the development of the times and the progress of technology, the existing training mode for network engineering talents could not satisfy the societal demands. The society has put a stronger and stronger advocacy in the reform of talents training. How to cultivate "qualified" network engineering talents has gained more and more attention. This paper analyzes the problems with the training mode of network engineering innovative talents. Coupled with the experiences of SCAU in exploring, researching and practices in the field of talents cultivation, this paper gives a discussion on the reform orientation of network engineering innovative talents training and presents some measures, including curriculum system optimization, teaching methods reform, university-enterprise cooperation, innovation lab construction and etc. Practices in recent years have shown that these measures are indeed effective. [ABSTRACT FROM AUTHOR]