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For: 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]
Number Citing Articles
1 Fogarty ES, Evans CA, Trotter MG, Manning JK. Sensor-based detection of a Haemonchus contortus (Barber's pole worm) infection in sheep. Smart Agricultural Technology 2023;3:100112. [DOI: 10.1016/j.atech.2022.100112] [Reference Citation Analysis]
2 Shorten P, Welten B. Assessment of a non-invasive accelerometer for detecting cattle urination and defecation events. Smart Agricultural Technology 2022;2:100031. [DOI: 10.1016/j.atech.2021.100031] [Reference Citation Analysis]
3 Csizmadia G, Daróczy B, Ferdinandy B, Miklósi Á. Behavior-specific binary machine learning models: Bout length of behavioral elements as biologically relevant parameter improves machine learning accuracy in analysis of dog behavior sequences.. [DOI: 10.21203/rs.3.rs-2185125/v1] [Reference Citation Analysis]
4 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]
5 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]
6 Kleanthous N, Hussain AJ, Khan W, Sneddon J, Al-shamma'a A, Liatsis P. A survey of machine learning approaches in animal behaviour. Neurocomputing 2022;491:442-63. [DOI: 10.1016/j.neucom.2021.10.126] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 5.0] [Reference Citation Analysis]
7 Li Y, Shu H, Bindelle J, Xu B, Zhang W, Jin Z, Guo L, Wang W. Classification and Analysis of Multiple Cattle Unitary Behaviors and Movements Based on Machine Learning Methods. Animals 2022;12:1060. [DOI: 10.3390/ani12091060] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Rhodes EC, Perotto-baldivieso HL, Reeves MC, Gonzalez LA. Perspectives on the Special Issue for Applications of Remote Sensing for Livestock and Grazingland Management. Remote Sensing 2022;14:1882. [DOI: 10.3390/rs14081882] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 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]
10 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]
11 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]
12 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]
13 Decandia M, Rassu S, Psiroukis V, Hadjigeorgiou I, Fountas S, Molle G, Acciaro M, Cabiddu A, Mameli M, Dimauro C, Giovanetti V. Evaluation of proper sensor position for classification of sheep behaviour through accelerometers. Small Ruminant Research 2021;201:106445. [DOI: 10.1016/j.smallrumres.2021.106445] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Marsden KA, Lush L, Holmberg JA, Harris IM, Whelan MJ, Webb S, King AJ, Wilson RP, Jones DL, Charteris AF, Cardenas LM, Chadwick DR. Quantifying the frequency and volume of urine deposition by grazing sheep using tri-axial accelerometers. Animal 2021;15:100234. [PMID: 34098494 DOI: 10.1016/j.animal.2021.100234] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
15 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]
16 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]
17 Hu S, Ingham A, Schmoelzl S, Mcnally J, Little B, Smith D, Bishop-hurley G, Wang Y, Li Y. Inclusion of features derived from a mixture of time window sizes improved classification accuracy of machine learning algorithms for sheep grazing behaviours. Computers and Electronics in Agriculture 2020;179:105857. [DOI: 10.1016/j.compag.2020.105857] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 4.0] [Reference Citation Analysis]