DSpace-CRIS

Now showing 1 - 5 of 5

Some of the metrics are blocked by your
consent settings
PigSense: Structural Vibration-based Activity and Health Monitoring System for Pigs
(2023-10-18)
Dong, Yiwen
;
Bonde, Amelie
;
Codling, Jesse R.
;
Bannis, Adeola
;
Cao, Jinpu
Precision Swine Farming has the potential to directly benefit swine health and industry profit by automatically monitoring the growth and health of pigs. We introduce the first system to use structural vibration to track animals and the first system for automated characterization of piglet group activities, including nursing, sleeping, and active times. PigSense uses physical knowledge of the structural vibration characteristics caused by pig-activity-induced load changes to recognize different behaviors of the sow and piglets. For our system to survive the harsh environment of the farrowing pen for three months, we designed simple, durable sensors for physical fault tolerance, then installed many of them, pooling their data to achieve algorithmic fault tolerance even when some do stop working. The key focus of this work was to create a robust system that can withstand challenging environments, has limited installation and maintenance requirements, and uses domain knowledge to precisely detect a variety of swine activities in noisy conditions while remaining flexible enough to adapt to future activities and applications. We provided an extensive analysis and evaluation of all-round swine activities and scenarios from our one-year field deployment across two pig farms in Thailand and the USA. To help assess the risk of crushing, farrowing sicknesses, and poor maternal behaviors, PigSense achieves an average of 97.8% and 94% for sow posture and motion monitoring, respectively, and an average of 96% and 71% for ingestion and excretion detection. To help farmers monitor piglet feeding, starvation, and illness, PigSense achieves an average of 87.7%, 89.4%, and 81.9% in predicting different levels of nursing, sleeping, and being active, respectively. In addition, we show that our monitoring of signal energy changes allows the prediction of farrowing in advance, as well as status tracking during the farrowing process and on the occasion of farrowing issues. Furthermore, PigSense also predicts the daily pattern and weight gain in the lactation cycle with 89% accuracy, a metric that can be used to monitor the piglets’ growth progress over the lactation cycle.
Some of the metrics are blocked by your
consent settings
MassHog: Weight-Sensitive Occupant Monitoring for Pig Pens using Actuated Structural Vibrations
(2021-09-24)
Codling, Jesse R.
;
Bonde, Amelie
;
Dong, Yiwen
;
Cao, Siyi
;
Sangpetch, Akkarit
In the swine livestock industry, weight tracking is commonly used to track health and growth of pigs. This is especially crucial during the farrowing period, where mother sows and their newborn piglets are housed together to facilitate nursing. Existing weight measurement methods, however, either provide only sporadic snapshots or have scalability and reliability issues in the harsh environment of industrial farms. This paper presents MassHog, a multi-layered system for ubiquitous weight measurement in harsh environments, such as pig pens, using structural vibrations. MassHog combines an existing sensor concept with actuated vibrations to weigh both piglets and adult hogs together. We evaluate the components of this system through testing deployments, including at real-world operating pig farms. Preliminary results show less than 2 kg mean absolute error for adult hogs.
Some of the metrics are blocked by your
consent settings
PigNet: Failure-tolerant pig activity monitoring system using structural vibration
(2021-05-18)
Bonde, Amelie
;
Codling, Jesse R.
;
Naruethep, Kanittha
;
Dong, Yiwen
;
Siripaktanakon, Wachirawich
Automated monitoring of livestock behavior can help farmers economically by detecting changes in animal welfare. Prior approaches use video, which requires light and high storage capability, or motion detection, which has difficulty separating subtle activities. Wearable sensors can address these issues but are vulnerable to destruction by the animals. To the best of our knowledge, we present the first system that uses structural vibration to track animal behavior, and the first system to automatically detect piglet nursing. PigNet uses vibration sensors attached to a pig pen to sense the unique vibration patterns and changes in structural response caused by the animals' movement and position within the pen. Combined with our knowledge of pig behavior, we use this physical knowledge of vibration characteristics to detect pig activities and track piglet growth in a real farm environment. Our system is designed to be robust to the harsh environment, which can create unpredictable noise, as well as physically damage or disconnect sensor nodes. When deployed in a real-world farm environment, our system was able to achieve a daily pen-level status profile of up to 90% accuracy, which tracks nursing activity, sow lying activity, and changes in piglet growth over the weeks-long pre-weaning period.
Some of the metrics are blocked by your
consent settings
Dependable Sensing System for Pig Farming
(2019-11-01)
Ariyadech, Sripong
;
Bonde, Amelie
;
Sangpetch, Orathai
;
Woramontri, Woranun
;
Siripaktanakon, Wachirawich
We have deployed our smart sensing system in a real commercial farm complex for at least 17 months. One of critical factors to success of the sensing system deployment is the resilience and fault tolerance of the system in a harsh environment with unreliable infrastructure and limited access. Power interruptions and intermittent connectivity is not uncommon. Sensors must work even submerging in animal excretion. Correct and continuous streams of sensor data is essential to our smart farming analytics. To make our sensing system sustain such challenges, we have designed and implemented the system with a capability of self-rejuvenation to ensure system liveness. We also equip it with our anomaly detection system to examine small sensor connectivity logs in order to identify potential faulty or deteriorating sensors or external event abnormality with minimal manual intervention.
Some of the metrics are blocked by your
consent settings
Structural vibration sensing to evaluate animal activity on a pig farm
(2018-11-04)
Bonde, Amelie
;
Sangpetch, Akkarit
;
Pan, Shijia
;
Woramontri, Woranun
;
Zhang, Pei

KMITL

Browse

Filters

Settings

Sort By

Results per page

Search Results