Smart Agriculture ›› 2023, Vol. 5 ›› Issue (1): 52-65.doi: 10.12133/j.smartag.SA202205009
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GUO Yangyang1(), DU Shuzeng2, QIAO Yongliang3(), LIANG Dong1
Received:
2022-05-28
Online:
2023-03-30
corresponding author:
QIAO Yongliang, E-mail:yongliang.qiao@outlook.com
About author:
GUO Yangyang, E-mail:guoyangyang113529@ahu.edu.cn
Supported by:
CLC Number:
GUO Yangyang, DU Shuzeng, QIAO Yongliang, LIANG Dong. Advances in the Applications of Deep Learning Technology for Livestock Smart Farming[J]. Smart Agriculture, 2023, 5(1): 52-65.
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URL: https://www.smartag.net.cn/EN/10.12133/j.smartag.SA202205009
1 | QIAO Y L, KONG H, CLARK C, et al. Intelligent perception for cattle monitoring: A review for cattle identification, body condition score evaluation, and weight estimation[J]. Computers and electronics in agriculture, 2021, 185: ID 106143. |
2 | 何东健, 刘冬, 赵凯旋. 精准畜牧业中动物信息智能感知与行为检测研究进展[J]. 农业机械学报, 2016, 47(5): 231-244. |
HE D J, LIU D, ZHAO K X. Review of perceiving animal information and behavior in precision livestock farming[J]. Transactions of the Chinese society for agricultural machinery, 2016, 47(5): 231-244. | |
3 | 秦箐, 刘志红, 赵存, 等. 机器视觉技术在畜牧业中的应用[J]. 农业工程, 2021, 11(7): 27-33. |
QIN Q, LIU Z H, ZHAO C, et al. Application of machine vision technology in livestock and poultry[J]. Agricultural engineering, 2021, 11(7): 27-33. | |
4 | CHEN C, ZHU W X, NORTON T. Behaviour recognition of pigs and cattle: Journey from computer vision to deep learning[J]. Computers and electronics in agriculture, 2021, 187: ID 106255. |
5 | ZIN T T, PHYO C N, TIN P, et al. Image technology based cow identification system using deep learning[C]// International Multi Conference of Engineers and Computer Scientists. Piscataway, NJ, USA: IEEE, 2018 |
6 | 李丹, 张凯锋, 李行健, 等. 基于Mask R-CNN的猪只爬跨行为识别[J]. 农业机械学报, 2019, 50(S1): 261-266, 275. |
LI D, ZHANG K F, LI X J, et al. Mounting behavior recognition for pigs based on mask R-CNN[J]. Transactions of the Chinese society for agricultural machinery, 2019, 50(S1): 261-266, 275. | |
7 | CHENG M, YUAN H B, WANG Q F, et al. Application of deep learning in sheep behaviors recognition and influence analysis of training data characteristics on the recognition effect[J]. Computers and electronics in agriculture, 2022, 198: ID 107010. |
8 | SONG S, LIU T H, WANG H, et al. Using pruning-based YOLOv3 deep learning algorithm for accurate detection of sheep face[J]. Animals: An open access journal from MDPI, 2022, 12(11): ID 1465. |
9 | DENG Z W, VAHDAT A, HU H X, et al. Structure inference machines: Recurrent neural networks for analyzing relations in group activity recognition[C]// IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE, 2016: 4772-4781. |
10 | KARIM F, MAJUMDAR S, DARABI H, et al. Multivariate LSTM-FCNs for time series classification[J]. Neural networks, 2019, 116: 237-245. |
11 | LI W J, QI F, TANG M, et al. Bidirectional LSTM with self-attention mechanism and multi-channel features for sentiment classification[J]. Neurocomputing, 2020, 387: 63-77. |
12 | LIU J, YANG Y H, LYU S Q, et al. Attention-based BiGRU-CNN for Chinese question classification[J]. Journal of ambient intelligence and humanized computing, 2019: 1-12. |
13 | AWAD A I. From classical methods to animal biometrics: A review on cattle identification and tracking[J]. Computers and electronics in agriculture, 2016, 123: 423-435. |
14 | KAIXUAN Z, DONGJIAN H. Target detection method for moving cows based on background subtraction[J]. International journal of agricultural and biological engineering, 2015, 8(1): 42-49. |
15 | 刘生智, 李春蓉, 刘同金, 等. 基于YOLO V3模型的奶牛目标检测[J]. 塔里木大学学报, 2019, 31(2): 85-90. |
LIU S Z, LI C R, LIU T J, et al. Detection of dairy cows based on YOLOv3 model[J]. Journal of tarim university, 2019, 31(2): 85-90. | |
16 | THARWAT A, GABER T, HASSANIEN A E. Cattle identification based on muzzle images using Gabor features and SVM classifier[C]// International conference on advanced machine learning technologies and applications. Cham, German: Springer, 2014: 236-247. |
17 | KIM H T, IKEDA Y, CHOI H L. The identification of Japanese black cattle by their faces[J]. Asian-australasian journal of animal sciences, 2005, 18(6): 868-872. |
18 | KUMAR S, SINGH S K, SINGH R, et al. Recognition of cattle using face images[M]// Animal biometrics. Singapore: Springer, 2017: 79-110. |
19 | 李向宇, 李慧盈. 基于卷积神经网络的猪脸特征点检测方法[J]. 吉林大学学报(理学版), 2022, 60(3): 609-616. |
LI X Y, LI H Y. Feature point detection method of pig face based on convolutional neural network[J]. Journal of Jilin university (science edition), 2022, 60(3): 609-616. | |
20 | ANDREW W, HANNUNA S, CAMPBELL N, et al. Automatic individual Holstein Friesian cattle identification via selective local coat pattern matching in RGB-D imagery[C]// 2016 IEEE International Conference on Image Processing (ICIP). Piscataway, NJ, USA: IEEE, 2016: 484-488. |
21 | 何屿彤, 李斌, 张锋, 等. 基于改进YOLOv3的猪脸识别[J]. 中国农业大学学报, 2021, 26(3): 53-62. |
HE Y T, LI B, ZHANG F, et al. Pig face recognition based on improved YOLOv3[J]. Journal of China agricultural university, 2021, 26(3): 53-62. | |
22 | 魏斌, MASUM BILLAH, 王美丽, 等. 基于深度学习的羊脸检测与识别方法[J]. 家畜生态学报, 2022, 43(3): 47-50. |
WEI B, BILLAH M, WANG M L, et al. Method of goat face detection and recognition based on deep learning[J]. Journal of domestic animal ecology, 2022, 43(3): 47-50. | |
23 | XUE H, QIN J, QUAN C, et al. Open set sheep face recognition based on euclidean space metric[J]. Mathematical problems in engineering 2021, 2021: 1-5. |
24 | LI X P, DU J Z, YANG J L, et al. When Mobilenetv2 meets transformer: A balanced sheep face recognition model[J]. Agriculture, 2022, 12(8): ID 1126. |
25 | KUMAR S, PANDEY A, SATWIK KSAI RAM, et al. Deep learning framework for recognition of cattle using muzzle point image pattern[J]. Measurement, 2018, 116: 1-17. |
26 | QIAO Y, SU D, KONG H, et al. Individual cattle identification using a deep learning based framework [J]. IFAC-PapersOnLine, 2019, 52(30): 318-323. |
27 | 何东健, 刘建敏, 熊虹婷, 等. 基于改进YOLOv3模型的挤奶奶牛个体识别方法[J]. 农业机械学报, 2020, 51(4): 250-260. |
HE D J, LIU J M, XIONG H T, et al. Individual identification of dairy cows based on improved YOLOv3[J]. Transactions of the Chinese society for agricultural machinery, 2020, 51(4): 250-260. | |
28 | HU H Q, DAI B S, SHEN W Z, et al. Cow identification based on fusion of deep parts features[J]. Biosystems engineering, 2020, 192: 245-256. |
29 | JIANG B, WU Q, YIN X Q, et al. FLYOLOv3 deep learning for key parts of dairy cow body detection[J]. Computers and electronics in agriculture, 2019, 166: ID 104982. |
30 | HODGSON J C, MOTT R, BAYLIS S M, et al. Drones count wildlife more accurately and precisely than humans[J]. Methods in ecology and evolution, 2018, 9(5): 1160-1167. |
31 | ANDREW W, GREATWOOD C, BURGHARDT T. Visual localisation and individual identification of Holstein Friesian cattle via deep learning[C]// 2017 IEEE International Conference on Computer Vision Workshops (ICCVW). Piscataway, NJ, USA: IEEE, 2018: 2850-2859. |
32 | SHAO W, KAWAKAMI R, YOSHIHASHI R, et al. Cattle detection and counting in UAV images based on convolutional neural networks[J]. International journal of remote sensing, 2020, 41(1): 31-52. |
33 | BARBEDO J G A, KOENIGKAN L V, SANTOS P M, et al. Counting cattle in UAV images—Dealing with clustered animals and animal/background contrast changes[J]. Sensors, 2020, 20(7): ID 2126. |
34 | 赵一广, 杨亮, 郑姗姗, 等. 家畜智能养殖设备和饲喂技术应用研究现状与发展趋势[J]. 智慧农业, 2019, 1(1): 20-31. |
ZHAO Y G, YANG L, ZHENG S S, et al. Advances in the development and applications of intelligent equipment and feeding technology for livestock production[J]. Smart agriculture, 2019, 1(1): 20-31. | |
35 | BAHLO C, DAHLHAUS P, THOMPSON H, et al. The role of interoperable data standards in precision livestock farming in extensive livestock systems: A review[J]. Computers and electronics in agriculture, 2019, 156: 459-466. |
36 | CAM M A, OLFAZ M, SOYDAN E. Body measurements reflect body weights and carcass yields in Karayaka sheep[J]. Asian journal of animal and veterinary advances, 2010, 5(2): 120-127. |
37 | DOHMEN R, CATAL C, LIU Q Z. Computer vision-based weight estimation of livestock: A systematic literature review[J]. New Zealand journal of agricultural research, 2022, 65(2/3): 227-247. |
38 | BELL M J, MAAK M, SORLEY M, et al. Comparison of methods for monitoring the body condition of dairy cows[J]. Frontiers in sustainable food systems, 2018, 2: ID 80. |
39 | SALAU J, HAAS J H, JUNGE W, et al. Feasibility of automated body trait determination using the SR4K time-of-flight camera in cow barns[J]. SpringerPlus, 2014, 3: ID 225. |
40 | HALACHMI I, KLOPČIČ M, POLAK P, et al. Automatic assessment of dairy cattle body condition score using thermal imaging[J]. Computers and electronics in agriculture, 2013, 99: 35-40. |
41 | LYNN N C, KYU Z M, ZIN T T, et al. Estimating body condition score of cows from images with the newly developed approach[C]// 2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD). Piscataway, NJ, USA: IEEE, 2017: 91-94. |
42 | 吴宇峰, 李一鸣, 赵远洋, 等. 基于计算机视觉的奶牛体况评分研究综述[J]. 农业机械学报, 2021, 52(S1): 268-275. |
WU Y F, LI Y M, ZHAO Y Y, et al. Review of research on body condition score for dairy cows based on computer vision[J]. Transactions of the Chinese society for agricultural machinery, 2021, 52(S1): 268-275. | |
43 | TEDIN R, BECERRA J A, DURO R J, et al. Towards automatic estimation of the body condition score of dairy cattle using hand-held images and active shape models[M]// Advances in Knowledge-Based and Intelligent Information and Engineering Systems. Amsterdam: IOS Press, 2012: 2150-2159. |
44 | SPOLIANSKY R, EDAN Y, PARMET Y, et al. Development of automatic body condition scoring using a low-cost 3-Dimensional Kinect camera[J]. Journal of dairy science, 2016, 99(9): 7714-7725. |
45 | SUN Y K, HUO P J, WANG Y J, et al. Automatic monitoring system for individual dairy cows based on a deep learning framework that provides identification via body parts and estimation of body condition score[J]. Journal of dairy science, 2019, 102(11): 10140-10151. |
46 | 孔商羽, 陈春雨. 基于多任务学习的猪只体重和体况评分预测[J]. 黑龙江大学工程学报, 2022, 13(2): 70-77. |
KONG S Y, CHEN C Y. Multi-tasking learning on prediction of pig weight and body condition score[J]. Journal of engineering of Heilongjiang university, 2022, 13(2): 70-77. | |
47 | ÇEVIK K K, MUSTAFA B. Body condition score (BCS) segmentation and classification in dairy cows using R-CNN deep learning architecture[J]. Avrupa bilim ve teknoloji dergisi, 2019(17): 1248-1255. |
48 | LI X R, HU Z L, HUANG X P, et al. Cow body condition score estimation with convolutional neural networks[C]//2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC). Piscataway, NJ, USA: IEEE, 2020: 433-437. |
49 | HUANG X P, HU Z L, WANG X R, et al. An improved single shot multibox detector method applied in body condition score for dairy cows[J]. Animals: An open access journal from MDPI, 2019, 9(7): ID 470. |
50 | ALVAREZ J R, ARROQUI M, MANGUDO P, et al. Estimating body condition score in dairy cows from depth images using convolutional neural networks, transfer learning and model ensembling techniques[J]. Agronomy, 2019, 9(2): ID 90. |
51 | DICKINSON R A, MORTON J M, BEGGS D S, et al. An automated walk-over weighing system as a tool for measuring liveweight change in lactating dairy cows[J]. Journal of dairy science, 2013, 96(7): 4477-4486. |
52 | TUYTTENS F A M. The importance of straw for pig and cattle welfare: A review[J]. Applied animal behaviour science, 2005, 92(3): 261-282. |
53 | KASHIHA M, BAHR C, OTT S, et al. Automatic weight estimation of individual pigs using image analysis[J]. Computers and electronics in agriculture, 2014, 107: 38-44. |
54 | HANSEN M F, SMITH M L, SMITH L N, et al. Automated monitoring of dairy cow body condition, mobility and weight using a single 3D video capture device[J]. Computers in industry, 2018, 98: 14-22. |
55 | ZHU A Z, THAKUR D, ÖZASLAN T, et al. The multivehicle stereo event camera dataset: An event camera dataset for 3D perception[J]. IEEE robotics and automation letters, 2018, 3(3): 2032-2039. |
56 | PEZZUOLO A, GUARINO M, SARTORI L, et al. On-barn pig weight estimation based on body measurements by a Kinect v1 depth camera[J]. Computers and electronics in agriculture, 2018, 148: 29-36. |
57 | 张建龙, 冀横溢, 滕光辉. 基于深度卷积网络的育肥猪体重估测[J]. 中国农业大学学报, 2021, 26(8): 111-119. |
ZHANG J L, JI H Y, TENG G H. Weight estimation of fattening pigs based on deep convolutional network[J]. Journal of China agricultural university, 2021, 26(8): 111-119. | |
58 | ZHANG J L, ZHUANG Y R, JI H Y, et al. Pig weight and body size estimation using a multiple output regression convolutional neural network: A fast and fully automatic method[J]. Sensors, 2021, 21(9): ID 3218. |
59 | RUCHAY A, DOROFEEV K, KALSCHIKOV V, et al. Live weight prediction of cattle using deep image regression[C]// 2021 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor). Piscataway, NJ, USA: IEEE, 2021: 32-36. |
60 | GJERGJI M, DE MORAES WEBER V, SILVA L O C, et al. Deep learning techniques for beef cattle body weight prediction[C]// 2020 International Joint Conference on Neural Networks (IJCNN). Piscataway, NJ, USA: IEEE, 2020. |
61 | DOHMEN R, CATAL C, LIU Q Z. Image-based body mass prediction of heifers using deep neural networks[J]. Biosystems engineering, 2021, 204: 283-293. |
62 | ACHOUR B, BELKADI M, FILALI I, et al. Image analysis for individual identification and feeding behaviour monitoring of dairy cows based on Convolutional Neural Networks (CNN)[J]. Biosystems engineering, 2020, 198: 31-49. |
63 | ANDRIAMANDROSO A L H, LEBEAU F, BECKERS Y, et al. Development of an open-source algorithm based on inertial measurement units (IMU) of a smartphone to detect cattle grass intake and ruminating behaviors[J]. Computers and electronics in agriculture, 2017, 139: 126-137. |
64 | 王政, 宋怀波, 王云飞, 等. 奶牛运动行为智能监测研究进展与技术趋势[J]. 智慧农业(中英文), 2022, 4(2): 36-52. |
WANG Z, SONG H B, WANG Y F, et al. Research progress and technology trend of intelligent morning of dairy cow motion behavior[J]. Smart agriculture, 2022, 4(2): 36-52. | |
65 | WANG K, WU P, CUI H M, et al. Identification and classification for sheep foraging behavior based on acoustic signal and deep learning[J]. Computers and electronics in agriculture, 2021, 187: ID 106275. |
66 | 应烨伟, 曾松伟, 赵阿勇, 等. 基于颈环采集节点的母羊产前行为识别方法[J]. 农业工程学报, 2020, 36(21): 210-219. |
YING Y W, ZENG S W, ZHAO A Y, et al. Recognition method for prenatal behavior of ewes based on the acquisition nodes of the collar[J]. Transactions of the Chinese society of agricultural engineering, 2020, 36(21): 210-219. | |
67 | 张春慧, 宣传忠, 于文波, 等. 基于三轴加速度传感器的放牧羊只牧食行为研究[J]. 农业机械学报, 2021, 52(10): 307-313. |
ZHANG C H, XUAN C Z, YU W B, et al. Grazing behavior of herding sheep based on three-axis acceleration sensor[J]. Transactions of the Chinese society for agricultural machinery, 2021, 52(10): 307-313. | |
68 | 郝玉胜, 林强, 王维兰, 等. 基于Wi-Fi无线感知技术的奶牛爬跨行为识别[J]. 农业工程学报, 2020, 36(19): 168-176. |
HAO Y S, LIN Q, WANG W L, et al. Recognition of crawling behavior of dairy cows using Wi-Fi wireless sensing technology[J]. Transactions of the Chinese society of agricultural engineering, 2020, 36(19): 168-176. | |
69 | PENG Y Q, KONDO N, FUJIURA T, et al. Classification of multiple cattle behavior patterns using a recurrent neural network with long short-term memory and inertial measurement units[J]. Computers and electronics in agriculture, 2019, 157: 247-253. |
70 | HOSSEININOORBIN S, LAYEGHY S, KUSY B, et al. Deep learning-based cattle behaviour classification using joint time-frequency data representation[J]. Computers and electronics in agriculture, 2021, 187: ID 106241. |
71 | KIM M, CHOI Y, LEE J, et al. A deep learning-based approach for feeding behavior recognition of weanling pigs[J]. Journal of animal science and technology, 2021, 63(6): 1453-1463. |
72 | JIANG M, RAO Y, ZHANG J Y, et al. Automatic behavior recognition of group-housed goats using deep learning[J]. Computers and electronics in agriculture, 2020, 177: ID 105706. |
73 | 王少华, 何东健. 基于改进YOLOv3模型的奶牛发情行为识别研究[J]. 农业机械学报, 2021, 52(7): 141-150. |
WANG S H, HE D J. Estrus behavior recognition of dairy cows based on improved YOLOv3 model[J]. Transactions of the Chinese society for agricultural machinery, 2021, 52(7): 141-150. | |
74 | WU D H, WU Q, YIN X Q, et al. Lameness detection of dairy cows based on the YOLOv3 deep learning algorithm and a relative step size characteristic vector[J]. Biosystems engineering, 2020, 189: 150-163. |
75 | AYADI S, SAID ABEN, JABBAR R, et al. Dairy cow rumination detection: A deep learning approach[C]// In International Workshop on Distributed Computing for Emerging Smart Networks. Berlin,German: Springer, 2020: 123-139. |
76 | FUENTES A, YOON S, PARK J, et al. Deep learning-based hierarchical cattle behavior recognition with spatio-temporal information[J]. Computers and Electronics in Agriculture, 2020, 177: ID 105627. |
77 | CHEN C, ZHU W X, STEIBEL J, et al. Classification of drinking and drinker-playing in pigs by a video-based deep learning method[J]. Biosystems Engineering, 2020, 196: 1-14. |
78 | GUO Y Y, QIAO Y L, SUKKARIEH S, et al. BiGRU-attention based cow behavior classification using video data for precision livestock farming[J]. Transactions of the ASABE, 2021, 64(6): 1823-1833. |
79 | QIAO Y L, GUO Y Y, YU K P, et al. C3D-ConvLSTM based cow behaviour classification using video data for precision livestock farming[J]. Computers and electronics in agriculture, 2022, 193: ID 106650. |
80 | JIANG B, YIN X Q, SONG H B. Single-stream long-term optical flow convolution network for action recognition of lameness dairy cow[J]. Computers and electronics in agriculture, 2020, 175: ID 105536. |
81 | WU D H, WANG Y F, HAN M X, et al. Using a CNN-LSTM for basic behaviors detection of a single dairy cow in a complex environment[J]. Computers and electronics in agriculture, 2021, 182: ID 106016. |
82 | DOUGLAS S L, SZYSZKA O, STODDART K, et al. Animal and management factors influencing grower and finisher pig performance and efficiency in European systems: A meta-analysis[J]. Animal, 2015, 9(7): 1210-1220. |
83 | REICH L J, WEARY D M, VEIRA D M, et al. Effects of sawdust bedding dry matter on lying behavior of dairy cows: A dose-dependent response[J]. Journal of dairy science, 2010, 93(4): 1561-1565. |
84 | DEBRECENI O, LEHOTAYOVÁ A, BUČKO O, et al. The behaviour of the pigs housed in hot climatic conditions[J]. Journal of central european agriculture, 2014, 15(1): 64-75. |
85 | FREGONESI J A, VEIRA D M, VON KEYSERLINGK M A G, et al. Effects of bedding quality on lying behavior of dairy cows[J]. Journal of dairy science, 2007, 90(12): 5468-5472. |
86 | HEPOLA H, HÄNNINEN L, PURSIAINEN P, et al. Feed intake and oral behaviour of dairy calves housed individually or in groups in warm or cold buildings[J]. Livestock science, 2006, 105(1/2/3): 94-104. |
87 | GUO Y Y, HE D J, CHAI L L. A machine vision-based method for monitoring scene-interactive behaviors of dairy calf[J]. Animals: An open access journal from MDPI, 2020, 10(2): ID 190. |
88 | COSTA, ISMAYILOVA, BORGONOVO, et al. Image-processing technique to measure pig activity in response to climatic variation in a pig barn[J]. Animal production science, 2014, 54(8): 1075-1083. |
89 | CHEN C, ZHU W, STEIBEL J, et al. Recognition of feeding behaviour of pigs and determination of feeding time of each pig by a video-based deep learning method[J]. Computers and electronics in agriculture, 2020, 176: ID 105642. |
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