BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: 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]
Number Citing Articles
1 Arablouei R, Wang L, Phillips C, Currie L, Yates J, Bishop-hurley G. In-situ animal behavior classification using knowledge distillation and fixed-point quantization. Smart Agricultural Technology 2023;4:100159. [DOI: 10.1016/j.atech.2022.100159] [Reference Citation Analysis]
2 Ferrero M, Vignolo LD, Vanrell SR, Martinez-rau LS, Chelotti JO, Galli JR, Giovanini LL, Rufiner HL. A full end-to-end deep approach for detecting and classifying jaw movements from acoustic signals in grazing cattle. Engineering Applications of Artificial Intelligence 2023;121:106016. [DOI: 10.1016/j.engappai.2023.106016] [Reference Citation Analysis]
3 Bloch V, Frondelius L, Arcidiacono C, Mancino M, Pastell M. Development and Analysis of a CNN- and Transfer-Learning-Based Classification Model for Automated Dairy Cow Feeding Behavior Recognition from Accelerometer Data. Sensors (Basel) 2023;23. [PMID: 36904813 DOI: 10.3390/s23052611] [Reference Citation Analysis]
4 Benaissa S, Tuyttens FAM, Plets D, Martens L, Vandaele L, Joseph W, Sonck B. Improved cattle behaviour monitoring by combining Ultra-Wideband location and accelerometer data. Animal 2023;17:100730. [PMID: 36868057 DOI: 10.1016/j.animal.2023.100730] [Reference Citation Analysis]
5 Castro J, Castro E, Castro M. IoT herd monitoring: an opportunity facing the Iberian mountain challenges.. [DOI: 10.21203/rs.3.rs-2539281/v1] [Reference Citation Analysis]
6 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]
7 Brouwers SP, Simmler M, Savary P, Scriba MF. Towards a novel method for detecting atypical lying down and standing up behaviours in dairy cows using accelerometers and machine learning. Smart Agricultural Technology 2023. [DOI: 10.1016/j.atech.2023.100199] [Reference Citation Analysis]
8 Dhariyal B, Le Nguyen T, Ifrim G. Scalable classifier-agnostic channel selection for multivariate time series classification. Data Min Knowl Disc 2023. [DOI: 10.1007/s10618-022-00909-1] [Reference Citation Analysis]
9 Fonseca L, Corujo D, Xavier W, Gonçalves P. On the Development of a Wearable Animal Monitor. Animals (Basel) 2022;13. [PMID: 36611731 DOI: 10.3390/ani13010120] [Reference Citation Analysis]
10 Arablouei R, Wang Z, Bishop-hurley GJ, Liu J. Multimodal sensor data fusion for in-situ classification of animal behavior using accelerometry and GNSS data. Smart Agricultural Technology 2022. [DOI: 10.1016/j.atech.2022.100163] [Reference Citation Analysis]
11 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]
12 Frondelius L, Van Weyenberg S, Lindeberg H, Van Nuffel A, Maselyne J, Pastell M. Spatial behaviour of dairy cows is affected by lameness. Applied Animal Behaviour Science 2022;256:105763. [DOI: 10.1016/j.applanim.2022.105763] [Reference Citation Analysis]
13 Fan B, Bryant R, Greer A. Behavioral Fingerprinting: Acceleration Sensors for Identifying Changes in Livestock Health. J 2022;5:435-454. [DOI: 10.3390/j5040030] [Reference Citation Analysis]
14 Hajnal É, Kovács L, Vakulya G. Dairy Cattle Rumen Bolus Developments with Special Regard to the Applicable Artificial Intelligence (AI) Methods. Sensors 2022;22:6812. [DOI: 10.3390/s22186812] [Reference Citation Analysis]
15 Horn J, Isselstein J. How do we feed grazing livestock in the future? A case for knowledge‐driven grazing systems. Grass and Forage Science. [DOI: 10.1111/gfs.12577] [Reference Citation Analysis]
16 Szenci O. Accuracy to Predict the Onset of Calving in Dairy Farms by Using Different Precision Livestock Farming Devices. Animals 2022;12:2006. [DOI: 10.3390/ani12152006] [Reference Citation Analysis]
17 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]
18 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]
19 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]
20 Daniela L, Daniel B, Jacopo B, Marcella G. Suggestions for the Environmental Sustainability from Precision Livestock Farming and Replacement in Dairy Cows. Lecture Notes in Computer Science 2022. [DOI: 10.1007/978-3-031-13324-4_30] [Reference Citation Analysis]