Monitoring Multiple Behaviors in Beef Calves Raised in Cow–Calf Contact Systems Using a Machine Learning Approach DOI Creative Commons
Seongjin Kim, Xuecheng Jin, Rajaraman Bharanidharan

et al.

Animals, Journal Year: 2024, Volume and Issue: 14(22), P. 3278 - 3278

Published: Nov. 14, 2024

The monitoring of pre-weaned calf behavior is crucial for ensuring health, welfare, and optimal growth. This study aimed to develop validate a machine learning-based technique the simultaneous multiple behaviors in beef calves within cow–calf contact (CCC) system using collar-mounted sensors integrating accelerometers gyroscopes. Three complementary models were developed classify feeding-related (natural suckling, feeding, rumination, others), postural states (lying standing), coughing events. Sensor data, including tri-axial acceleration angular velocity, along with video recordings, collected from 78 across two farms. LightGBM algorithm was employed classification, model performance evaluated confusion matrix, area under receiver operating characteristic curve (AUC-ROC), Pearson’s correlation coefficient (r). Model 1 achieved high recognizing natural suckling (accuracy: 99.10%; F1 score: 96.88%; AUC-ROC: 0.999; r: 0.997), rumination 97.36%; 95.07%; 0.995; 0.990), feeding 95.76%; 91.89%; 0.990; 0.987). 2 exhibited an excellent classification lying 97.98%; 98.45%; 0.989; 0.982) standing 97.11%; 0.983). 3 reasonable events 88.88%; 78.61%; 0.942; 0.969). demonstrates potential learning calves, providing valuable tool optimizing production management early disease detection CCC

Language: Английский

Smart technologies for sustainable pasture-based ruminant systems: A review DOI

Sara Marchegiani,

G. Gislon, R. Marino

et al.

Smart Agricultural Technology, Journal Year: 2025, Volume and Issue: 10, P. 100789 - 100789

Published: Jan. 18, 2025

Language: Английский

Citations

1
A Review of the Monitoring Techniques Used to Detect Oestrus in Sows DOI Creative Commons

Dannielle Glencorse,

Christopher G. Grupen, R. Bathgate

et al.

Animals, Journal Year: 2025, Volume and Issue: 15(3), P. 331 - 331

Published: Jan. 24, 2025

The agricultural industries have embraced the use of technologies as they improve efficiency and food security. pork industry is no exception to this, monitoring techniques artificial intelligence allow for unprecedented capacity track physiological behavioural condition individual animals. This article reviews a range those in reference detection oestrus sows, time when ability precisely ascertain changes associated with fluctuating hormone levels can an immense impact on economic profitability farm. strengths weaknesses each technique from practical application perspective are discussed, followed by considerations further research refinement.

Language: Английский

Citations

1
May the Extensive Farming System of Small Ruminants Be Smart? DOI Creative Commons
Rosanna Paolino, Adriana Di Trana, Adele Coppola

et al.

Agriculture, Journal Year: 2025, Volume and Issue: 15(9), P. 929 - 929

Published: April 24, 2025

Precision Livestock Farming (PLF) applies a complex of sensor technology, algorithms, and multiple tools for individual, real-time livestock monitoring. In intensive systems, PLF is now quite widespread, allowing the optimisation management, thanks to early recognition diseases possibility monitoring animals’ feeding reproductive behaviour, with an overall improvement their welfare. Similarly, systems represent opportunity improve profitability sustainability extensive farming including those small ruminants, rationalising use pastures by avoiding overgrazing controlling animals. Despite distribution in several parts world, low profit relatively high cost devices cause delays implementing ruminants compared dairy cows. Applying these animals requires customisation systems. many cases, unit prices sensors are higher than developed large due miniaturisation production costs associated lower numbers. Sheep goat farms often mountainous remote areas poor technological infrastructure ineffective electricity, telephone, internet services. Moreover, ruminant usually advanced age farmers, contributing local initiatives implementation. A targeted literature analysis was carried out identify technologies already applied or at stage development management grazing animals, particularly sheep goats, effects on nutrition, production, animal The current developments include wearable, non-wearable, network technologies. review involved main fields application can help most suitable managing goats contribute selecting more sustainable efficient solutions line environmental welfare concerns.

Language: Английский

Citations

0
OPTIMIZATION OF LIVESTOCK MONITORING SYSTEM IN OUTDOOR BASED ON INTERNET OF THINGS (IOT) DOI Open Access

Andi Chairunnas,

Agung Prahujana Putra

JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer), Journal Year: 2024, Volume and Issue: 9(2), P. 323 - 329

Published: Feb. 29, 2024

Livestock businesses are often underestimated by the public because they associated with less hygienic working environments. However, demand for livestock products such as meat and milk is increasing, providing significant business opportunities. Several obstacles, loss capital required cage construction, barriers to starting a business. losses, especially in outdoor farms, occur of lack proper monitoring data collection. Therefore, technology overcome this problem. The application IoT an effective solution overcoming By utilizing sensors, GPS, temperature, heart rate, farmers can monitor farm animals remotely using Android applications. In study, U-blox Neo6m GPS sensor was used track location animals, temperature conditions rate determine health that had been tested. use 1500 mAh LI-ION LITHIUM battery power source proved be sufficient 7 h. results showed IoT-based Outdoor Monitoring System provide information on last well real-time database. This innovation opens opportunities improve management efficiently, minimize increase productivity their

Language: Английский

Citations

2
Metabolic Periparturient Diseases in Small Ruminants: An Update DOI Creative Commons
João Simões, Gisele Margatho

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(21), P. 10073 - 10073

Published: Nov. 4, 2024

Metabolic diseases are significant that affect the welfare, health, and production of small ruminant flocks raised for dairy meat purposes. In breeding females, they mainly occur from six to eight weeks before after parturition, respectively. Pregnancy toxemia lactational ketosis manifestations hyperketonemia, primarily due energetic deficit. Hypocalcemia hypomagnesemia related metabolic unavailability calcium magnesium, This review aimed identify discuss current most relevant aspects individual herd health management these interrelated with impact on sheep goats’ farm sustainability. These nutritional deficits, but homeostatic homeorhetic disruptions responsible clinical signs forms. Currently, their diagnosis monitoring assessed by biochemistry body fluids feed bromatological evaluation. Epidemiological studies measuring risk factors also contribute prevention. Nevertheless, research specific biomarkers composite indices diseases, in context precision medicine, new pathways driven suitable efficient animal production.

Language: Английский

Citations

0
Monitoring Multiple Behaviors in Beef Calves Raised in Cow–Calf Contact Systems Using a Machine Learning Approach DOI Creative Commons
Seongjin Kim, Xuecheng Jin, Rajaraman Bharanidharan

et al.

Animals, Journal Year: 2024, Volume and Issue: 14(22), P. 3278 - 3278

Published: Nov. 14, 2024

The monitoring of pre-weaned calf behavior is crucial for ensuring health, welfare, and optimal growth. This study aimed to develop validate a machine learning-based technique the simultaneous multiple behaviors in beef calves within cow–calf contact (CCC) system using collar-mounted sensors integrating accelerometers gyroscopes. Three complementary models were developed classify feeding-related (natural suckling, feeding, rumination, others), postural states (lying standing), coughing events. Sensor data, including tri-axial acceleration angular velocity, along with video recordings, collected from 78 across two farms. LightGBM algorithm was employed classification, model performance evaluated confusion matrix, area under receiver operating characteristic curve (AUC-ROC), Pearson’s correlation coefficient (r). Model 1 achieved high recognizing natural suckling (accuracy: 99.10%; F1 score: 96.88%; AUC-ROC: 0.999; r: 0.997), rumination 97.36%; 95.07%; 0.995; 0.990), feeding 95.76%; 91.89%; 0.990; 0.987). 2 exhibited an excellent classification lying 97.98%; 98.45%; 0.989; 0.982) standing 97.11%; 0.983). 3 reasonable events 88.88%; 78.61%; 0.942; 0.969). demonstrates potential learning calves, providing valuable tool optimizing production management early disease detection CCC

Language: Английский

Citations

0