基于热图像的种蛋气室变化监测算法

doi:10.3964/j.issn.1000-0593(2021)02-0572-07

Abstract
References
Related Citation (15)

Download: PDF (3540 KB)
Export: BibTeX | EndNote (RIS)

Abstract The size of the egg air-cell is one of the important indicators for monitoring the hatching process of the eggs. According to the thermodynamic structure of the breeding egg, during the hatching process of them, the temperature difference between the part of the shell enclosing the air-cell and the other part of the shell may cause a temperature difference, which can be observed by thermal infrared imaging technology. A thermal-image based monitoring method for egg air-cell change was designed. The algorithm for monitoring the thermal-image of the egg air-cell mainly includes three parts: egg target detection and segment, egg air-cell’s size of segment and egg air-cell’s area calculation. The target detection of the eggs is implemented by the fast-RCNN algorithm. The size of the egg air-cell is implemented by BP neural network. The egg air-cell area is calculated based on the segmented egg thermal-image segment. In this paper, eggs had hatched for 5 days or more were used as research objects, and thermal-images of them were taken for testing. The test results show that the mean average precision(mAP) of the target detection of the thermal-image for the egg is 99.85%, which has a good detection effect. Under optimizing of the BP neural network’s hyperparameters, the result shows that the best structure of layers is 1000-1000-1000, the best initial learning rate is 0.000 1, and the best max-iteration is 500. Using F1-measure as the evaluation index of the segment effect compare with the Otsu algorithm, the BP neural network’s result is much better than the Otsu algorithm. The Otsu algorithm’s segment evaluation is 65.25%, and the BP neural network’s result is 87.02%. In the case of only one egg, the segment result of the BP neural network is 87.17%, and the result of the Otsu algorithm is 68.86%. The segment result of BP neural network is 86.94%, and the result of the Otsu algorithm is 61.64% under the interference of other eggs. BP neural network has a stronger anti-interference ability. At the end of the experiment, the air-cell changes of fertilized eggs from 5 to 19 days were extracted, and the hatching of the eggs was monitored by observing the curve of the egg chamber area. The curve shows that the air-cell tends to become larger with the days increasing. The comparison between the artificial measurement method and the thermal-image measurement method shows that the correlation between the two is 0.934 3, which has a good correlation. The thermal-image of egg air-cell change monitoring algorithm can realize individual identification of egg and rapid monitoring of gas chamber size in actual production, which is of great significance for health monitoring during egg hatching.

Key words：Thermal-image; Size of egg’s air-cell; Machine vision; Deep learning; BP neural network; Image segment

Received: 2019-12-02 Accepted: 2020-04-26

ZTFLH:

TN219

Corresponding Authors: XIAO De-qin E-mail: deqinx@scau.edu.cn

Cite this article:

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2021)02-0572-07 OR https://www.gpxygpfx.com/EN/Y2021/V41/I02/572

[1] XU Yan-wei, XU Ai-jun, XIE Tan-cheng, et al(徐彦伟, 徐爱军, 颉潭成, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2015, 46(2): 20.
[2] GUO Qing-liang(郭庆亮). China Poultry(中国家禽), 2019, 41(16): 74.
[3] CHEN Jia-juan, CHEN Xiao-guang, JI Shou-wen(陈佳娟, 陈晓光, 纪寿文). Computer Applications and Software(计算机应用与软件), 2000, (6): 5.
[4] Lin C S, Yeh P T, Chen D C, et al. Computers & Electronics in Agriculture, 2013, 91(4): 94.
[5] ZHANG Wei, TU Kang, LIU Peng, et al(张伟, 屠康, 刘鹏, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2012, 43(2): 140.
[6] ZHU Zhi-hui, TANG Yong, HONG Qi, et al(祝志慧，汤勇，洪琪，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2018, 34(6): 197.
[7] XIAO Fan(肖凡). Chinese Journal of Animal Science(中国畜牧杂志), 2013, 49(18): 56.
[8] TANG Xiao-hui, BA Sang, DaWa Ciren(唐晓惠, 巴桑, 达娃次仁). Animal Husbandry & Veterinary Medicine(畜牧与兽医), 2004, 36(5): 17.
[9] MA Li, JIANG Xiao-xia, YUAN Rong(马力, 蒋晓霞, 袁蓉). Journal of Southwest University for Nationalities·Natural Science Edition(西南民族大学学报·自然科学版), 2009, 35(6): 1194.
[10] LU Jing-zhu, JIANG Huan-yu, CUI Di(卢劲竹, 蒋焕煜, 崔笛). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2014, 45(4): 244.
[11] FENG Lei, GAO Ji-xing, HE Yong, et al(冯雷, 高吉兴, 何勇, 等) Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2013, 44(9): 169.
[12] YU Bin, WANG Xi-bo(于滨, 王喜波). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2012, 28(15): 276.
[13] ZHOU Guo-xiong, WU Shu-ci(周国雄, 吴舒辞). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2012, 43(12): 211.
[14] Ren S, He K, Girshick R, et al. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015, 39(6): 1137.
[15] SUN Zhe, ZHANG Chun-long, GE Lu-zhen, et al(孙哲, 张春龙, 葛鲁镇, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报)，2019, 50(7): 216.
[16] K He, X Cao, Y Shi, et al. IEEE Transactions on Medical Imaging，2019, 38(2): 585.
[17] Xiao Yu, Xi Ye, Qiang Gao, et al. International Journal of Pressure Vessels and Piping. 2019, 172: 329.