Integrating LoRaWAN sensor network and machine learning models to classify beef cattle behavior on arid rangelands of the southwestern United State
Monitoring cattle on large, often rugged, rangelands is a daunting task that can be improved using Long Range Wide Area Network (LoRaWAN) tracking and monitoring technology. This study tested the performance of five machine learning classifiers to discriminate between active and stationary states, a...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
|
| Series: | Smart Agricultural Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525002357 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850140652611829760 |
|---|---|
| author | Andres Perea Sajidur Rahman Huiying Chen Andrew Cox Shelemia Nyamuryekung’e Mehmet Bakir Huping Cao Richard Estell Brandon Bestelmeyer Andres F. Cibils Santiago A. Utsumi |
| author_facet | Andres Perea Sajidur Rahman Huiying Chen Andrew Cox Shelemia Nyamuryekung’e Mehmet Bakir Huping Cao Richard Estell Brandon Bestelmeyer Andres F. Cibils Santiago A. Utsumi |
| author_sort | Andres Perea |
| collection | DOAJ |
| description | Monitoring cattle on large, often rugged, rangelands is a daunting task that can be improved using Long Range Wide Area Network (LoRaWAN) tracking and monitoring technology. This study tested the performance of five machine learning classifiers to discriminate between active and stationary states, and among walking, grazing, ruminating and resting behaviors of cattle. Models were trained and tested using a single motion index (MI) collected at 1-minute intervals by LoRaWAN cattle collars equipped with a Global Navigation Satellite System (GNSS) receptor and triaxial accelerometer. Twenty-four mature cows of four breeds were monitored across four periods between July and November 2022. Behavioral observations were made using 168 h of video records, which resulted in a dataset of 9222 instances of labeled sensor data. Logistic regression, support vector machine, multilayer perceptron, XGBoost and random forest algorithms were trained and tested. No differences in MI were detected between ruminating and resting; therefore, subsequent model testing considered the combination of rumination and resting as one class. All classifiers correctly differentiated between the two states and among grazing, walking and resting behaviors with an accuracy and macro-F 1 scores of >0.95 and >0.90, respectively. The results suggest satisfactory application of trained models to monitor cattle behavior on desert rangeland. The annotated dataset used in this study is publicly available at Perea et al. [1]. |
| format | Article |
| id | doaj-art-2337da0366d640cc8c27b7da2647dffc |
| institution | OA Journals |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-2337da0366d640cc8c27b7da2647dffc2025-08-20T02:29:43ZengElsevierSmart Agricultural Technology2772-37552025-08-011110100210.1016/j.atech.2025.101002Integrating LoRaWAN sensor network and machine learning models to classify beef cattle behavior on arid rangelands of the southwestern United StateAndres Perea0Sajidur Rahman1Huiying Chen2Andrew Cox3Shelemia Nyamuryekung’e4Mehmet Bakir5Huping Cao6Richard Estell7Brandon Bestelmeyer8Andres F. Cibils9Santiago A. Utsumi10Department of Animal and Range Sciences, New Mexico State University, Las Cruces, NM 88003, USADepartment of Computer Science, New Mexico State University, Las Cruces, NM 88003, USADepartment of Computer Science, New Mexico State University, Las Cruces, NM 88003, USADepartment of Animal and Range Sciences, New Mexico State University, Las Cruces, NM 88003, USADivision of Food Production and Society, Norwegian Institute of Bioeconomy Research (NIBIO), PB 115, N-1431 Ås, NorwayDepartment of Animal and Range Sciences, New Mexico State University, Las Cruces, NM 88003, USADepartment of Computer Science, New Mexico State University, Las Cruces, NM 88003, USAUnited States Department of Agriculture-Agriculture Research Service, Jornada Experimental Range, Las Cruces, NM 88003, USAUnited States Department of Agriculture-Agriculture Research Service, Jornada Experimental Range, Las Cruces, NM 88003, USAUnited States Department of Agriculture Southern Plains Climate Hub, United States Department of Agriculture-Agriculture Research Service, Oklahoma and Central Plains Agricultural Research Center, El Reno, OK 73036, USADepartment of Animal and Range Sciences, New Mexico State University, Las Cruces, NM 88003, USA; Corresponding author.Monitoring cattle on large, often rugged, rangelands is a daunting task that can be improved using Long Range Wide Area Network (LoRaWAN) tracking and monitoring technology. This study tested the performance of five machine learning classifiers to discriminate between active and stationary states, and among walking, grazing, ruminating and resting behaviors of cattle. Models were trained and tested using a single motion index (MI) collected at 1-minute intervals by LoRaWAN cattle collars equipped with a Global Navigation Satellite System (GNSS) receptor and triaxial accelerometer. Twenty-four mature cows of four breeds were monitored across four periods between July and November 2022. Behavioral observations were made using 168 h of video records, which resulted in a dataset of 9222 instances of labeled sensor data. Logistic regression, support vector machine, multilayer perceptron, XGBoost and random forest algorithms were trained and tested. No differences in MI were detected between ruminating and resting; therefore, subsequent model testing considered the combination of rumination and resting as one class. All classifiers correctly differentiated between the two states and among grazing, walking and resting behaviors with an accuracy and macro-F 1 scores of >0.95 and >0.90, respectively. The results suggest satisfactory application of trained models to monitor cattle behavior on desert rangeland. The annotated dataset used in this study is publicly available at Perea et al. [1].http://www.sciencedirect.com/science/article/pii/S2772375525002357Animal monitoringAccelerometerLoRaWANMachine learningRangeland cattleReal time |
| spellingShingle | Andres Perea Sajidur Rahman Huiying Chen Andrew Cox Shelemia Nyamuryekung’e Mehmet Bakir Huping Cao Richard Estell Brandon Bestelmeyer Andres F. Cibils Santiago A. Utsumi Integrating LoRaWAN sensor network and machine learning models to classify beef cattle behavior on arid rangelands of the southwestern United State Smart Agricultural Technology Animal monitoring Accelerometer LoRaWAN Machine learning Rangeland cattle Real time |
| title | Integrating LoRaWAN sensor network and machine learning models to classify beef cattle behavior on arid rangelands of the southwestern United State |
| title_full | Integrating LoRaWAN sensor network and machine learning models to classify beef cattle behavior on arid rangelands of the southwestern United State |
| title_fullStr | Integrating LoRaWAN sensor network and machine learning models to classify beef cattle behavior on arid rangelands of the southwestern United State |
| title_full_unstemmed | Integrating LoRaWAN sensor network and machine learning models to classify beef cattle behavior on arid rangelands of the southwestern United State |
| title_short | Integrating LoRaWAN sensor network and machine learning models to classify beef cattle behavior on arid rangelands of the southwestern United State |
| title_sort | integrating lorawan sensor network and machine learning models to classify beef cattle behavior on arid rangelands of the southwestern united state |
| topic | Animal monitoring Accelerometer LoRaWAN Machine learning Rangeland cattle Real time |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525002357 |
| work_keys_str_mv | AT andresperea integratinglorawansensornetworkandmachinelearningmodelstoclassifybeefcattlebehavioronaridrangelandsofthesouthwesternunitedstate AT sajidurrahman integratinglorawansensornetworkandmachinelearningmodelstoclassifybeefcattlebehavioronaridrangelandsofthesouthwesternunitedstate AT huiyingchen integratinglorawansensornetworkandmachinelearningmodelstoclassifybeefcattlebehavioronaridrangelandsofthesouthwesternunitedstate AT andrewcox integratinglorawansensornetworkandmachinelearningmodelstoclassifybeefcattlebehavioronaridrangelandsofthesouthwesternunitedstate AT shelemianyamuryekunge integratinglorawansensornetworkandmachinelearningmodelstoclassifybeefcattlebehavioronaridrangelandsofthesouthwesternunitedstate AT mehmetbakir integratinglorawansensornetworkandmachinelearningmodelstoclassifybeefcattlebehavioronaridrangelandsofthesouthwesternunitedstate AT hupingcao integratinglorawansensornetworkandmachinelearningmodelstoclassifybeefcattlebehavioronaridrangelandsofthesouthwesternunitedstate AT richardestell integratinglorawansensornetworkandmachinelearningmodelstoclassifybeefcattlebehavioronaridrangelandsofthesouthwesternunitedstate AT brandonbestelmeyer integratinglorawansensornetworkandmachinelearningmodelstoclassifybeefcattlebehavioronaridrangelandsofthesouthwesternunitedstate AT andresfcibils integratinglorawansensornetworkandmachinelearningmodelstoclassifybeefcattlebehavioronaridrangelandsofthesouthwesternunitedstate AT santiagoautsumi integratinglorawansensornetworkandmachinelearningmodelstoclassifybeefcattlebehavioronaridrangelandsofthesouthwesternunitedstate |