BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Sakai K, Oishi K, Miwa M, Kumagai H, Hirooka H. Behavior classification of goats using 9-axis multi sensors: The effect of imbalanced datasets on classification performance. Computers and Electronics in Agriculture 2019;166:105027. [DOI: 10.1016/j.compag.2019.105027] [Cited by in Crossref: 20] [Cited by in F6Publishing: 21] [Article Influence: 5.0] [Reference Citation Analysis]
Number Citing Articles
1 Martinez-rau LS, Weißbrich M, Payá-vayá G. A 4$$\mu$$W Low-Power Audio Processor System for Real-Time Jaw Movements Recognition in Grazing Cattle. J Sign Process Syst 2022. [DOI: 10.1007/s11265-022-01822-y] [Reference Citation Analysis]
2 Williams M, Zhan Lai S. Classification of dairy cow excretory events using a tail-mounted accelerometer. Computers and Electronics in Agriculture 2022;199:107187. [DOI: 10.1016/j.compag.2022.107187] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Chebli Y, El Otmani S, Hornick J, Bindelle J, Cabaraux J, Chentouf M. Estimation of Grazing Activity of Dairy Goats Using Accelerometers and Global Positioning System. Sensors 2022;22:5629. [DOI: 10.3390/s22155629] [Reference Citation Analysis]
4 Jin Z, Guo L, Shu H, Qi J, Li Y, Xu B, Zhang W, Wang K, Wang W. Behavior Classification and Analysis of Grazing Sheep on Pasture with Different Sward Surface Heights Using Machine Learning. Animals 2022;12:1744. [DOI: 10.3390/ani12141744] [Reference Citation Analysis]
5 Turner KE, Thompson A, Harris I, Ferguson M, Sohel F. Deep Learning based Classification of Sheep Behaviour from Accelerometer data with Imbalance. Information Processing in Agriculture 2022. [DOI: 10.1016/j.inpa.2022.04.001] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Price E, Langford J, Fawcett TW, Wilson AJ, Croft DP. Classifying the posture and activity of ewes and lambs using accelerometers and machine learning on a commercial flock. Applied Animal Behaviour Science 2022. [DOI: 10.1016/j.applanim.2022.105630] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
7 Kojima T, Oishi K, Aoki N, Matsubara Y, Uete T, Fukushima Y, Inoue G, Sato S, Shiraishi T, Hirooka H, Masuda T. Estimation of beef cow body condition score: a machine learning approach using three-dimensional image data and a simple approach with heart girth measurements. Livestock Science 2022;256:104816. [DOI: 10.1016/j.livsci.2021.104816] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Su Q, Tang J, Zhai M, He D. An intelligent method for dairy goat tracking based on Siamese network. Computers and Electronics in Agriculture 2022;193:106636. [DOI: 10.1016/j.compag.2021.106636] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
9 Riaboff L, Shalloo L, Smeaton A, Couvreur S, Madouasse A, Keane M. Predicting livestock behaviour using accelerometers: A systematic review of processing techniques for ruminant behaviour prediction from raw accelerometer data. Computers and Electronics in Agriculture 2022;192:106610. [DOI: 10.1016/j.compag.2021.106610] [Cited by in Crossref: 15] [Cited by in F6Publishing: 16] [Article Influence: 15.0] [Reference Citation Analysis]
10 Lemoine M, Piriou M, Charpentier A, Delagarde R. Validation of the Lifecorder Plus device for accurate recording of the grazing time of dairy goats. Small Ruminant Research 2021;202:106469. [DOI: 10.1016/j.smallrumres.2021.106469] [Reference Citation Analysis]
11 Thiebault A, Huetz C, Pistorius P, Aubin T, Charrier I. Animal-borne acoustic data alone can provide high accuracy classification of activity budgets. Anim Biotelemetry 2021;9. [DOI: 10.1186/s40317-021-00251-1] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
12 Yang X, Zhao Y, Street GM, Huang Y, Filip To SD, Purswell JL. Classification of broiler behaviours using triaxial accelerometer and machine learning. Animal 2021;15:100269. [PMID: 34102430 DOI: 10.1016/j.animal.2021.100269] [Cited by in Crossref: 5] [Cited by in F6Publishing: 8] [Article Influence: 2.5] [Reference Citation Analysis]
13 Jin M, Wang C, Jensen DB. Effect of De-noising by Wavelet Filtering and Data Augmentation by Borderline SMOTE on the Classification of Imbalanced Datasets of Pig Behavior. Front Anim Sci 2021;2. [DOI: 10.3389/fanim.2021.666855] [Reference Citation Analysis]
14 Jin M, Wang C. EFFECT OF WAVELET DE-NOISING ON THE CLASSIFICATION OF PIG BEHAVIOUR. Eng Agríc 2021;41:286-96. [DOI: 10.1590/1809-4430-eng.agric.v41n3p286-296/2021] [Reference Citation Analysis]
15 Uenishi S, Oishi K, Kojima T, Kitajima K, Yasunaka Y, Sakai K, Sonoda Y, Kumagai H, Hirooka H. A novel accelerometry approach combining information on classified behaviors and quantified physical activity for assessing health status of cattle: a preliminary study. Applied Animal Behaviour Science 2021;235:105220. [DOI: 10.1016/j.applanim.2021.105220] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
16 Sun G, Shi C, Liu J, Ma P, Ma J. Behavior Recognition and Maternal Ability Evaluation for Sows Based on Triaxial Acceleration and Video Sensors. IEEE Access 2021;9:65346-60. [DOI: 10.1109/access.2021.3075272] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
17 Barwick J, Lamb DW, Dobos R, Welch M, Schneider D, Trotter M. Identifying Sheep Activity from Tri-Axial Acceleration Signals Using a Moving Window Classification Model. Remote Sensing 2020;12:646. [DOI: 10.3390/rs12040646] [Cited by in Crossref: 23] [Cited by in F6Publishing: 23] [Article Influence: 7.7] [Reference Citation Analysis]